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sha256:67f58bc42875064b3b3df6622762b6cdffa8f32a23b77db99c2b1cb4cfff05b1 +size 11625 diff --git a/configs/inference/inference_v1.yaml b/configs/inference/inference_v1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e888888b547bf0316e7963a957fa905cb6fe9d65 --- /dev/null +++ b/configs/inference/inference_v1.yaml @@ -0,0 +1,23 @@ +unet_additional_kwargs: + unet_use_cross_frame_attention: false + unet_use_temporal_attention: false + use_motion_module: true + motion_module_resolutions: [1,2,4,8] + motion_module_mid_block: false + motion_module_decoder_only: false + motion_module_type: "Vanilla" + + motion_module_kwargs: + num_attention_heads: 8 + num_transformer_block: 1 + attention_block_types: [ "Temporal_Self", "Temporal_Self" ] + temporal_position_encoding: true + temporal_position_encoding_max_len: 24 + temporal_attention_dim_div: 1 + +noise_scheduler_kwargs: + beta_start: 0.00085 + beta_end: 0.012 + beta_schedule: "linear" + steps_offset: 1 + clip_sample: False \ No newline at end of file diff --git a/configs/inference/inference_v2.yaml b/configs/inference/inference_v2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a501c42b3e9049f5884a20e827d99f7b6deb513b --- /dev/null +++ b/configs/inference/inference_v2.yaml @@ -0,0 +1,36 @@ +unet_additional_kwargs: + use_inflated_groupnorm: true + unet_use_cross_frame_attention: false + unet_use_temporal_attention: false + use_motion_module: true + cross_attention_dim: 384 + motion_module_resolutions: + - 1 + - 2 + - 4 + - 8 + motion_module_mid_block: true + motion_module_decoder_only: false + motion_module_type: Vanilla + motion_module_kwargs: + num_attention_heads: 8 + num_transformer_block: 1 + attention_block_types: + - Temporal_Self + - Temporal_Self + temporal_position_encoding: true + temporal_position_encoding_max_len: 32 + temporal_attention_dim_div: 1 + +noise_scheduler_kwargs: + beta_start: 0.00085 + beta_end: 0.012 + beta_schedule: "linear" + clip_sample: false + steps_offset: 1 + ### Zero-SNR params + prediction_type: "v_prediction" + rescale_betas_zero_snr: True + timestep_spacing: "trailing" + +sampler: DDIM \ No newline at end of file diff --git a/configs/prompts/animation.yaml b/configs/prompts/animation.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ae7d5f8968b66d14c715356f5217e44a8be2b313 --- /dev/null +++ b/configs/prompts/animation.yaml @@ -0,0 +1,27 @@ +## dependency models +pretrained_base_model_path: "./pretrained_weights/sd-image-variations-diffusers/" +pretrained_vae_path: "./pretrained_weights/sd-vae-ft-mse/" +audio_model_path: "./pretrained_weights/audio_processor/whisper_tiny.pt" + +## echo mimic checkpoint +denoising_unet_path: "./pretrained_weights/denoising_unet.pth" +reference_unet_path: "./pretrained_weights/reference_unet.pth" +face_locator_path: "./pretrained_weights/face_locator.pth" +motion_module_path: "./pretrained_weights/motion_module.pth" + +## deonise model configs +inference_config: "./configs/inference/inference_v2.yaml" +weight_dtype: 'fp16' + +## test cases +test_cases: + "./assets/test_imgs/a.png": + - "./assets/test_audios/echomimic_en.wav" + "./assets/test_imgs/b.png": + - "./assets/test_audios/echomimic_en_girl.wav" + "./assets/test_imgs/c.png": + - "./assets/test_audios/echomimic_en_girl.wav" + "./assets/test_imgs/d.png": + - "./assets/test_audios/echomimic_en_girl.wav" + "./assets/test_imgs/e.png": + - "./assets/test_audios/echomimic_en.wav" \ No newline at end of file diff --git a/configs/prompts/animation_pose.yaml b/configs/prompts/animation_pose.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9bdc5af88b23b89c9ec92d0c13a5440201fd689b --- /dev/null +++ b/configs/prompts/animation_pose.yaml @@ -0,0 +1,21 @@ +## dependency models +pretrained_base_model_path: "./pretrained_weights/sd-image-variations-diffusers" +pretrained_vae_path: "./pretrained_weights/sd-vae-ft-mse" +audio_model_path: "./pretrained_weights/audio_processor/whisper_tiny.pt" + +## echo mimic checkpoint +denoising_unet_path: "./pretrained_weights/denoising_unet_pose.pth" +reference_unet_path: "./pretrained_weights/reference_unet_pose.pth" +face_locator_path: "./pretrained_weights/face_locator_pose.pth" +motion_module_path: "./pretrained_weights/motion_module_pose.pth" + +## deonise model configs +inference_config: "./configs/inference/inference_v2.yaml" +weight_dtype: 'fp16' + +## test cases +test_cases: + "./assets/test_pose_demo/d.jpg": + - "./assets/test_pose_demo_audios/movie_0_clip_0.wav" + - "./assets/test_pose_demo_pose" + \ No newline at end of file diff --git a/configs/prompts/animation_pose_acc.yaml b/configs/prompts/animation_pose_acc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6412149955315a2b2ff5b6175a96a3cc66263018 --- /dev/null +++ b/configs/prompts/animation_pose_acc.yaml @@ -0,0 +1,21 @@ +## dependency models +pretrained_base_model_path: "./pretrained_weights/sd-image-variations-diffusers" +pretrained_vae_path: "./pretrained_weights/sd-vae-ft-mse" +audio_model_path: "./pretrained_weights/audio_processor/whisper_tiny.pt" + +## echo mimic checkpoint +denoising_unet_path: "./pretrained_weights/denoising_unet_pose_acc.pth" +reference_unet_path: "./pretrained_weights/reference_unet_pose.pth" +face_locator_path: "./pretrained_weights/face_locator_pose.pth" +motion_module_path: "./pretrained_weights/motion_module_pose_acc.pth" + +## deonise model configs +inference_config: "./configs/inference/inference_v2.yaml" +weight_dtype: 'fp16' + +## test cases +test_cases: + "./assets/test_pose_demo/d.jpg": + - "./assets/test_pose_demo_audios/movie_0_clip_0.wav" + - "./assets/test_pose_demo_pose" + diff --git a/demo_motion_sync.py b/demo_motion_sync.py new file mode 100644 index 0000000000000000000000000000000000000000..751011a444146d27af850354ba26a02b2c8751d3 --- /dev/null +++ b/demo_motion_sync.py @@ -0,0 +1,136 @@ +from src.utils.mp_utils import LMKExtractor +from src.utils.draw_utils import FaceMeshVisualizer +from src.utils.img_utils import pil_to_cv2, cv2_to_pil, center_crop_cv2, pils_from_video, save_videos_from_pils, save_video_from_cv2_list +from PIL import Image +import cv2 +from IPython import embed +import numpy as np +import copy +from src.utils.motion_utils import motion_sync +import pathlib +import torch +import pickle +from glob import glob +import os + +vis = FaceMeshVisualizer(draw_iris=False, draw_mouse=True, draw_eye=True, draw_nose=True, draw_eyebrow=True, draw_pupil=True) + +imsize = (512, 512) +visualization = True +driver_video = "./assets/driven_videos/a.mp4" +# driver_videos = glob("/nas2/luoque.lym/evaluation/test_datasets/gt_data/OurDataset/*.mp4") +ref_image = './assets/test_imgs/d.png' +# ref_image = 'panda.png' + +lmk_extractor = LMKExtractor() + +input_frames_cv2 = [cv2.resize(center_crop_cv2(pil_to_cv2(i)), imsize) for i in pils_from_video(driver_video)] +ref_frame =cv2.resize(cv2.imread(ref_image), (512, 512)) +ref_det = lmk_extractor(ref_frame) +# print(ref_det) + +sequence_driver_det = [] +try: + for frame in input_frames_cv2: + result = lmk_extractor(frame) + assert result is not None, "{}, bad video, face not detected".format(driver_video) + sequence_driver_det.append(result) +except: + print("face detection failed") + exit() +print(len(sequence_driver_det)) + +if visualization: + pose_frames_driver = [vis.draw_landmarks((512, 512), i["lmks"], normed=True) for i in sequence_driver_det] + poses_add_driver = [(i * 0.5 + j * 0.5).clip(0,255).astype(np.uint8) for i, j in zip(input_frames_cv2, pose_frames_driver)] + +save_dir = './{}'.format(ref_image.split('/')[-1].replace('.png', '')) +os.makedirs(save_dir, exist_ok=True) + +sequence_det_ms = motion_sync(sequence_driver_det, ref_det) +for i in range(len(sequence_det_ms)): + with open('{}/{}.pkl'.format(save_dir, i), 'wb') as file: + pickle.dump(sequence_det_ms[i], file) +if visualization: + pose_frames = [vis.draw_landmarks((512, 512), i, normed=False) for i in sequence_det_ms] + poses_add = [(i * 0.5 + ref_frame * 0.5).clip(0,255).astype(np.uint8) for i in pose_frames] + +# sequence_det_ms = motion_sync(sequence_driver_det, ref_det, per_landmark_align=False) +# for i in range(len(sequence_det_ms)): +# tmp = {} +# tmp["lmks"] = sequence_det_ms[i] +# with open('{}_v2/{}.pkl'.format(save_dir, i), 'wb') as file: +# pickle.dump(tmp, file) +# pose_frames_wo_lmkalign = [vis.draw_landmarks((512, 512), i, normed=False) for i in sequence_det_ms] +# poses_add_wo_lmkalign = [(i * 0.5 + ref_frame * 0.5).clip(0,255).astype(np.uint8) for i in pose_frames_wo_lmkalign] + +poses_cat = [np.concatenate([i, j], axis=1) for i, j in zip(poses_add_driver, poses_add)] + +save_video_from_cv2_list(poses_cat, "./vis_example.mp4", fps=24.0) + + +# for ref_image in ref_images[:1]: +# # for driver_video in driver_videos: +# # ref_image = "./samples/007.png" +# # save_dir = '/nas2/jiajiong.caojiajio/data/test_pose/OurDataset/{}'.format(driver_video.split('/')[-1].replace('.mp4', '')) +# save_dir = './{}'.format(ref_image.split('/')[-1].replace('.png', '')) +# os.makedirs(save_dir+'_v1', exist_ok=True) +# os.makedirs(save_dir+'_v2', exist_ok=True) +# #"./samples/hedra_003.png" + +# #"./samples/video_temp_fix.mov" +# input_frames_cv2 = [cv2.resize(center_crop_cv2(pil_to_cv2(i)), imsize) for i in pils_from_video(driver_video)] +# # input_frames_cv2 = [cv2.resize(pil_to_cv2(i), imsize) for i in pils_from_video(driver_video)] +# lmk_extractor = LMKExtractor() + +# ref_frame =cv2.resize(cv2.imread(ref_image), (512, 512)) +# ref_det = lmk_extractor(ref_frame) + +# sequence_driver_det = [] +# try: +# for frame in input_frames_cv2: +# result = lmk_extractor(frame) +# assert result is not None, "{}, bad video, face not detected".format(driver_video) +# sequence_driver_det.append(result) +# except: +# continue +# print(len(sequence_driver_det)) + +# # os.makedirs(save_dir, exist_ok=True) +# # for i in range(len(sequence_driver_det)): +# # with open('{}/{}.pkl'.format(save_dir, i), 'wb') as file: +# # pickle.dump(sequence_driver_det[i]["lmks"] * imsize[0], file) + + + +# #[vis.draw_landmarks(imsize, i["lmks"], normed=True, white=True) for i in det_results] + +# pose_frames_driver = [vis.draw_landmarks((512, 512), i["lmks"], normed=True) for i in sequence_driver_det] +# poses_add_driver = [(i * 0.5 + j * 0.5).clip(0,255).astype(np.uint8) for i, j in zip(input_frames_cv2, pose_frames_driver)] + +# sequence_det_ms = motion_sync(sequence_driver_det, ref_det) +# for i in range(len(sequence_det_ms)): +# tmp = {} +# tmp["lmks"] = sequence_det_ms[i] +# with open('{}_v1/{}.pkl'.format(save_dir, i), 'wb') as file: +# pickle.dump(tmp, file) +# pose_frames = [vis.draw_landmarks((512, 512), i, normed=False) for i in sequence_det_ms] +# poses_add = [(i * 0.5 + ref_frame * 0.5).clip(0,255).astype(np.uint8) for i in pose_frames] + +# sequence_det_ms = motion_sync(sequence_driver_det, ref_det, per_landmark_align=False) +# for i in range(len(sequence_det_ms)): +# tmp = {} +# tmp["lmks"] = sequence_det_ms[i] +# with open('{}_v2/{}.pkl'.format(save_dir, i), 'wb') as file: +# pickle.dump(tmp, file) +# pose_frames_wo_lmkalign = [vis.draw_landmarks((512, 512), i, normed=False) for i in sequence_det_ms] +# poses_add_wo_lmkalign = [(i * 0.5 + ref_frame * 0.5).clip(0,255).astype(np.uint8) for i in pose_frames_wo_lmkalign] + +# poses_cat = [np.concatenate([i, j, k], axis=1) for i, j, k in zip(poses_add_driver, poses_add_wo_lmkalign, poses_add)] + +# save_video_from_cv2_list(poses_cat, "./output/example2.mp4", fps=24.0) +# # exit() +# #embed() + +# #poses_cat = [(i * 0.5 + j * 0.5).clip(0,255).astype(np.uint8) for i, j in zip(input_frames_cv2, pose_frames)] +# #save_videos_from_pils([cv2_to_pil(i) for i in poses_cat], "./output/pose_cat.mp4", fps=24) \ No newline at end of file diff --git a/infer_audio2vid.py b/infer_audio2vid.py new file mode 100644 index 0000000000000000000000000000000000000000..359f8f1d3e630088f27ebd5aa5b20a1b173164de --- /dev/null +++ b/infer_audio2vid.py @@ -0,0 +1,257 @@ +#!/usr/bin/env python +# -*- coding: UTF-8 -*- +''' +@Project :EchoMimic +@File :audio2vid.py +@Author :juzhen.czy +@Date :2024/3/4 17:43 +''' +import argparse +import os + +import random +import platform +import subprocess +from datetime import datetime +from pathlib import Path + +import cv2 +import numpy as np +import torch +from diffusers import AutoencoderKL, DDIMScheduler +from omegaconf import OmegaConf +from PIL import Image + +from src.models.unet_2d_condition import UNet2DConditionModel +from src.models.unet_3d_echo import EchoUNet3DConditionModel +from src.models.whisper.audio2feature import load_audio_model +from src.pipelines.pipeline_echo_mimic import Audio2VideoPipeline +from src.utils.util import save_videos_grid, crop_and_pad +from src.models.face_locator import FaceLocator +from moviepy.editor import VideoFileClip, AudioFileClip +from facenet_pytorch import MTCNN + +ffmpeg_path = os.getenv('FFMPEG_PATH') +if ffmpeg_path is None and platform.system() in ['Linux', 'Darwin']: + try: + result = subprocess.run(['which', 'ffmpeg'], capture_output=True, text=True) + if result.returncode == 0: + ffmpeg_path = result.stdout.strip() + print(f"FFmpeg is installed at: {ffmpeg_path}") + else: + print("FFmpeg is not installed. Please download ffmpeg-static and export to FFMPEG_PATH.") + print("For example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static") + except Exception as e: + pass + +if ffmpeg_path is not None and ffmpeg_path not in os.getenv('PATH'): + print("Adding FFMPEG_PATH to PATH") + os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}" + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=str, default="./configs/prompts/animation.yaml") + parser.add_argument("-W", type=int, default=512) + parser.add_argument("-H", type=int, default=512) + parser.add_argument("-L", type=int, default=1200) + parser.add_argument("--seed", type=int, default=420) + parser.add_argument("--facemusk_dilation_ratio", type=float, default=0.1) + parser.add_argument("--facecrop_dilation_ratio", type=float, default=0.5) + + parser.add_argument("--context_frames", type=int, default=12) + parser.add_argument("--context_overlap", type=int, default=3) + + parser.add_argument("--cfg", type=float, default=2.5) + parser.add_argument("--steps", type=int, default=30) + parser.add_argument("--sample_rate", type=int, default=16000) + parser.add_argument("--fps", type=int, default=24) + parser.add_argument("--device", type=str, default="cuda") + + args = parser.parse_args() + + return args + +def select_face(det_bboxes, probs): + ## max face from faces that the prob is above 0.8 + ## box: xyxy + if det_bboxes is None or probs is None: + return None + filtered_bboxes = [] + for bbox_i in range(len(det_bboxes)): + if probs[bbox_i] > 0.8: + filtered_bboxes.append(det_bboxes[bbox_i]) + if len(filtered_bboxes) == 0: + return None + + sorted_bboxes = sorted(filtered_bboxes, key=lambda x:(x[3]-x[1]) * (x[2] - x[0]), reverse=True) + return sorted_bboxes[0] + + + +def main(): + args = parse_args() + + config = OmegaConf.load(args.config) + if config.weight_dtype == "fp16": + weight_dtype = torch.float16 + else: + weight_dtype = torch.float32 + + device = args.device + if device.__contains__("cuda") and not torch.cuda.is_available(): + device = "cpu" + + inference_config_path = config.inference_config + infer_config = OmegaConf.load(inference_config_path) + + + ############# model_init started ############# + + ## vae init + vae = AutoencoderKL.from_pretrained( + config.pretrained_vae_path, + ).to("cuda", dtype=weight_dtype) + + ## reference net init + reference_unet = UNet2DConditionModel.from_pretrained( + config.pretrained_base_model_path, + subfolder="unet", + ).to(dtype=weight_dtype, device=device) + reference_unet.load_state_dict( + torch.load(config.reference_unet_path, map_location="cpu"), + ) + + ## denoising net init + if os.path.exists(config.motion_module_path): + ### stage1 + stage2 + denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d( + config.pretrained_base_model_path, + config.motion_module_path, + subfolder="unet", + unet_additional_kwargs=infer_config.unet_additional_kwargs, + ).to(dtype=weight_dtype, device=device) + else: + ### only stage1 + denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d( + config.pretrained_base_model_path, + "", + subfolder="unet", + unet_additional_kwargs={ + "use_motion_module": False, + "unet_use_temporal_attention": False, + "cross_attention_dim": infer_config.unet_additional_kwargs.cross_attention_dim + } + ).to(dtype=weight_dtype, device=device) + denoising_unet.load_state_dict( + torch.load(config.denoising_unet_path, map_location="cpu"), + strict=False + ) + + ## face locator init + face_locator = FaceLocator(320, conditioning_channels=1, block_out_channels=(16, 32, 96, 256)).to( + dtype=weight_dtype, device="cuda" + ) + face_locator.load_state_dict(torch.load(config.face_locator_path)) + + ### load audio processor params + audio_processor = load_audio_model(model_path=config.audio_model_path, device=device) + + ### load face detector params + face_detector = MTCNN(image_size=320, margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, device=device) + + ############# model_init finished ############# + + width, height = args.W, args.H + sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs) + scheduler = DDIMScheduler(**sched_kwargs) + + pipe = Audio2VideoPipeline( + vae=vae, + reference_unet=reference_unet, + denoising_unet=denoising_unet, + audio_guider=audio_processor, + face_locator=face_locator, + scheduler=scheduler, + ) + pipe = pipe.to("cuda", dtype=weight_dtype) + + date_str = datetime.now().strftime("%Y%m%d") + time_str = datetime.now().strftime("%H%M") + save_dir_name = f"{time_str}--seed_{args.seed}-{args.W}x{args.H}" + save_dir = Path(f"output/{date_str}/{save_dir_name}") + save_dir.mkdir(exist_ok=True, parents=True) + + for ref_image_path in config["test_cases"].keys(): + for audio_path in config["test_cases"][ref_image_path]: + + if args.seed is not None and args.seed > -1: + generator = torch.manual_seed(args.seed) + else: + generator = torch.manual_seed(random.randint(100, 1000000)) + + ref_name = Path(ref_image_path).stem + audio_name = Path(audio_path).stem + final_fps = args.fps + + #### face musk prepare + face_img = cv2.imread(ref_image_path) + face_mask = np.zeros((face_img.shape[0], face_img.shape[1])).astype('uint8') + + det_bboxes, probs = face_detector.detect(face_img) + select_bbox = select_face(det_bboxes, probs) + if select_bbox is None: + face_mask[:, :] = 255 + else: + xyxy = select_bbox[:4] + xyxy = np.round(xyxy).astype('int') + rb, re, cb, ce = xyxy[1], xyxy[3], xyxy[0], xyxy[2] + r_pad = int((re - rb) * args.facemusk_dilation_ratio) + c_pad = int((ce - cb) * args.facemusk_dilation_ratio) + face_mask[rb - r_pad : re + r_pad, cb - c_pad : ce + c_pad] = 255 + + #### face crop + r_pad_crop = int((re - rb) * args.facecrop_dilation_ratio) + c_pad_crop = int((ce - cb) * args.facecrop_dilation_ratio) + crop_rect = [max(0, cb - c_pad_crop), max(0, rb - r_pad_crop), min(ce + c_pad_crop, face_img.shape[1]), min(re + c_pad_crop, face_img.shape[0])] + print(crop_rect) + face_img = crop_and_pad(face_img, crop_rect) + face_mask = crop_and_pad(face_mask, crop_rect) + face_img = cv2.resize(face_img, (args.W, args.H)) + face_mask = cv2.resize(face_mask, (args.W, args.H)) + + ref_image_pil = Image.fromarray(face_img[:, :, [2, 1, 0]]) + face_mask_tensor = torch.Tensor(face_mask).to(dtype=weight_dtype, device="cuda").unsqueeze(0).unsqueeze(0).unsqueeze(0) / 255.0 + + video = pipe( + ref_image_pil, + audio_path, + face_mask_tensor, + width, + height, + args.L, + args.steps, + args.cfg, + generator=generator, + audio_sample_rate=args.sample_rate, + context_frames=args.context_frames, + fps=final_fps, + context_overlap=args.context_overlap + ).videos + + video = video + save_videos_grid( + video, + f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4", + n_rows=1, + fps=final_fps, + ) + + video_clip = VideoFileClip(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4") + audio_clip = AudioFileClip(audio_path) + video_clip = video_clip.set_audio(audio_clip) + video_clip.write_videofile(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}_withaudio.mp4", codec="libx264", audio_codec="aac") + print(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}_withaudio.mp4") + + +if __name__ == "__main__": + main() diff --git a/infer_audio2vid_pose.py b/infer_audio2vid_pose.py new file mode 100644 index 0000000000000000000000000000000000000000..39852593c3e49e6e14e3fbe02bebfb866d880dd5 --- /dev/null +++ b/infer_audio2vid_pose.py @@ -0,0 +1,232 @@ +import argparse +import os +import random +from datetime import datetime +from pathlib import Path +from typing import List + +import av +import cv2 +import numpy as np +import torch +import torchvision +from diffusers import AutoencoderKL, DDIMScheduler +from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline +from einops import repeat +from omegaconf import OmegaConf +from PIL import Image +from torchvision import transforms +from transformers import CLIPVisionModelWithProjection + +from src.models.unet_2d_condition import UNet2DConditionModel +from src.models.unet_3d_echo import EchoUNet3DConditionModel +from src.models.whisper.audio2feature import load_audio_model +from src.pipelines.pipeline_echo_mimic_pose import AudioPose2VideoPipeline +from src.utils.util import get_fps, read_frames, save_videos_grid, crop_and_pad +import sys +from src.models.face_locator import FaceLocator +from moviepy.editor import VideoFileClip, AudioFileClip +from facenet_pytorch import MTCNN +from src.utils.draw_utils import FaceMeshVisualizer +import pickle + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=str, default="./configs/prompts/animation_pose.yaml") + parser.add_argument("-W", type=int, default=512) + parser.add_argument("-H", type=int, default=512) + parser.add_argument("-L", type=int, default=160) + parser.add_argument("--seed", type=int, default=420) + parser.add_argument("--facemusk_dilation_ratio", type=float, default=0.1) + parser.add_argument("--facecrop_dilation_ratio", type=float, default=0.5) + + parser.add_argument("--context_frames", type=int, default=12) + parser.add_argument("--context_overlap", type=int, default=3) + + parser.add_argument("--cfg", type=float, default=2.5) + parser.add_argument("--steps", type=int, default=30) + parser.add_argument("--sample_rate", type=int, default=16000) + parser.add_argument("--fps", type=int, default=24) + parser.add_argument("--device", type=str, default="cuda") + + args = parser.parse_args() + + return args + +def select_face(det_bboxes, probs): + ## max face from faces that the prob is above 0.8 + ## box: xyxy + filtered_bboxes = [] + for bbox_i in range(len(det_bboxes)): + if probs[bbox_i] > 0.8: + filtered_bboxes.append(det_bboxes[bbox_i]) + if len(filtered_bboxes) == 0: + return None + + sorted_bboxes = sorted(filtered_bboxes, key=lambda x:(x[3]-x[1]) * (x[2] - x[0]), reverse=True) + return sorted_bboxes[0] + + +def main(): + args = parse_args() + + config = OmegaConf.load(args.config) + if config.weight_dtype == "fp16": + weight_dtype = torch.float16 + else: + weight_dtype = torch.float32 + + device = args.device + if device.__contains__("cuda") and not torch.cuda.is_available(): + device = "cpu" + + inference_config_path = config.inference_config + infer_config = OmegaConf.load(inference_config_path) + + ############# model_init started ############# + + ## vae init + vae = AutoencoderKL.from_pretrained( + config.pretrained_vae_path, + ).to("cuda", dtype=weight_dtype) + + ## reference net init + reference_unet = UNet2DConditionModel.from_pretrained( + config.pretrained_base_model_path, + subfolder="unet", + ).to(dtype=weight_dtype, device=device) + reference_unet.load_state_dict( + torch.load(config.reference_unet_path, map_location="cpu"), + ) + + ## denoising net init + if os.path.exists(config.motion_module_path): + ### stage1 + stage2 + denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d( + config.pretrained_base_model_path, + config.motion_module_path, + subfolder="unet", + unet_additional_kwargs=infer_config.unet_additional_kwargs, + ).to(dtype=weight_dtype, device=device) + else: + ### only stage1 + denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d( + config.pretrained_base_model_path, + "", + subfolder="unet", + unet_additional_kwargs={ + "use_motion_module": False, + "unet_use_temporal_attention": False, + "cross_attention_dim": infer_config.unet_additional_kwargs.cross_attention_dim + } + ).to(dtype=weight_dtype, device=device) + denoising_unet.load_state_dict( + torch.load(config.denoising_unet_path, map_location="cpu"), + strict=False + ) + + ## face locator init + face_locator = FaceLocator(320, conditioning_channels=3, block_out_channels=(16, 32, 96, 256)).to( + dtype=weight_dtype, device="cuda" + ) + face_locator.load_state_dict(torch.load(config.face_locator_path)) + + visualizer = FaceMeshVisualizer(draw_iris=False, draw_mouse=False) + + ### load audio processor params + audio_processor = load_audio_model(model_path=config.audio_model_path, device=device) + + ### load face detector params + face_detector = MTCNN(image_size=320, margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, device=device) + + ############# model_init finished ############# + + width, height = args.W, args.H + sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs) + scheduler = DDIMScheduler(**sched_kwargs) + + pipe = AudioPose2VideoPipeline( + vae=vae, + reference_unet=reference_unet, + denoising_unet=denoising_unet, + audio_guider=audio_processor, + face_locator=face_locator, + scheduler=scheduler, + ) + + pipe = pipe.to("cuda", dtype=weight_dtype) + + date_str = datetime.now().strftime("%Y%m%d") + time_str = datetime.now().strftime("%H%M") + save_dir_name = f"{time_str}--seed_{args.seed}-{args.W}x{args.H}" + save_dir = Path(f"output/{date_str}/{save_dir_name}") + save_dir.mkdir(exist_ok=True, parents=True) + + for ref_image_path in config["test_cases"].keys(): + for file_path in config["test_cases"][ref_image_path]: + if ".wav" in file_path: + audio_path = file_path + else: + pose_dir = file_path + + if args.seed is not None and args.seed > -1: + generator = torch.manual_seed(args.seed) + else: + generator = torch.manual_seed(random.randint(100, 1000000)) + + ref_name = Path(ref_image_path).stem + audio_name = Path(audio_path).stem + final_fps = args.fps + + ref_image_pil = Image.open(ref_image_path).convert("RGB") + + # ==================== face_locator ===================== + pose_list = [] + for index in range(len(os.listdir(pose_dir))): + tgt_musk_path = os.path.join(pose_dir, f"{index}.pkl") + + with open(tgt_musk_path, "rb") as f: + tgt_kpts = pickle.load(f) + tgt_musk = visualizer.draw_landmarks((args.W, args.H), tgt_kpts) + tgt_musk_pil = Image.fromarray(np.array(tgt_musk).astype(np.uint8)).convert('RGB') + pose_list.append(torch.Tensor(np.array(tgt_musk_pil)).to(dtype=weight_dtype, device="cuda").permute(2,0,1) / 255.0) + face_mask_tensor = torch.stack(pose_list, dim=1).unsqueeze(0) + + + video = pipe( + ref_image_pil, + audio_path, + face_mask_tensor, + width, + height, + args.L, + args.steps, + args.cfg, + generator=generator, + audio_sample_rate=args.sample_rate, + context_frames=12, + fps=final_fps, + context_overlap=3 + ).videos + + final_length = min(video.shape[2], face_mask_tensor.shape[2]) + video = torch.cat([video[:, :, :final_length, :, :], face_mask_tensor[:, :, :final_length, :, :].detach().cpu()], dim=-1) + save_videos_grid( + video, + f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4", + n_rows=2, + fps=final_fps, + ) + + from moviepy.editor import VideoFileClip, AudioFileClip + video_clip = VideoFileClip(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4") + audio_clip = AudioFileClip(audio_path) + video_clip = video_clip.set_audio(audio_clip) + video_clip.write_videofile(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}_withaudio.mp4", codec="libx264", audio_codec="aac") + print(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}_withaudio.mp4") + + +if __name__ == "__main__": + main() + diff --git a/infer_audio2vid_pose_acc.py b/infer_audio2vid_pose_acc.py new file mode 100644 index 0000000000000000000000000000000000000000..009b8a79352e74c3fb0cf2b2a5328390edad2803 --- /dev/null +++ b/infer_audio2vid_pose_acc.py @@ -0,0 +1,232 @@ +import argparse +import os +import random +from datetime import datetime +from pathlib import Path +from typing import List + +import av +import cv2 +import numpy as np +import torch +import torchvision +from diffusers import AutoencoderKL, DDIMScheduler +from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline +from einops import repeat +from omegaconf import OmegaConf +from PIL import Image +from torchvision import transforms +from transformers import CLIPVisionModelWithProjection + +from src.models.unet_2d_condition import UNet2DConditionModel +from src.models.unet_3d_echo import EchoUNet3DConditionModel +from src.models.whisper.audio2feature import load_audio_model +from src.pipelines.pipeline_echo_mimic_pose_acc import AudioPose2VideoPipeline +from src.utils.util import get_fps, read_frames, save_videos_grid, crop_and_pad +import sys +from src.models.face_locator import FaceLocator +from moviepy.editor import VideoFileClip, AudioFileClip +from facenet_pytorch import MTCNN +from src.utils.draw_utils import FaceMeshVisualizer +import pickle + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=str, default="./configs/prompts/animation_pose_acc.yaml") + parser.add_argument("-W", type=int, default=512) + parser.add_argument("-H", type=int, default=512) + parser.add_argument("-L", type=int, default=160) + parser.add_argument("--seed", type=int, default=420) + parser.add_argument("--facemusk_dilation_ratio", type=float, default=0.1) + parser.add_argument("--facecrop_dilation_ratio", type=float, default=0.5) + + parser.add_argument("--context_frames", type=int, default=12) + parser.add_argument("--context_overlap", type=int, default=3) + + parser.add_argument("--cfg", type=float, default=1.0) + parser.add_argument("--steps", type=int, default=6) + parser.add_argument("--sample_rate", type=int, default=16000) + parser.add_argument("--fps", type=int, default=24) + parser.add_argument("--device", type=str, default="cuda") + + args = parser.parse_args() + + return args + +def select_face(det_bboxes, probs): + ## max face from faces that the prob is above 0.8 + ## box: xyxy + filtered_bboxes = [] + for bbox_i in range(len(det_bboxes)): + if probs[bbox_i] > 0.8: + filtered_bboxes.append(det_bboxes[bbox_i]) + if len(filtered_bboxes) == 0: + return None + + sorted_bboxes = sorted(filtered_bboxes, key=lambda x:(x[3]-x[1]) * (x[2] - x[0]), reverse=True) + return sorted_bboxes[0] + + +def main(): + args = parse_args() + + config = OmegaConf.load(args.config) + if config.weight_dtype == "fp16": + weight_dtype = torch.float16 + else: + weight_dtype = torch.float32 + + device = args.device + if device.__contains__("cuda") and not torch.cuda.is_available(): + device = "cpu" + + inference_config_path = config.inference_config + infer_config = OmegaConf.load(inference_config_path) + + ############# model_init started ############# + + ## vae init + vae = AutoencoderKL.from_pretrained( + config.pretrained_vae_path, + ).to("cuda", dtype=weight_dtype) + + ## reference net init + reference_unet = UNet2DConditionModel.from_pretrained( + config.pretrained_base_model_path, + subfolder="unet", + ).to(dtype=weight_dtype, device=device) + reference_unet.load_state_dict( + torch.load(config.reference_unet_path, map_location="cpu"), + ) + + ## denoising net init + if os.path.exists(config.motion_module_path): + ### stage1 + stage2 + denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d( + config.pretrained_base_model_path, + config.motion_module_path, + subfolder="unet", + unet_additional_kwargs=infer_config.unet_additional_kwargs, + ).to(dtype=weight_dtype, device=device) + else: + ### only stage1 + denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d( + config.pretrained_base_model_path, + "", + subfolder="unet", + unet_additional_kwargs={ + "use_motion_module": False, + "unet_use_temporal_attention": False, + "cross_attention_dim": infer_config.unet_additional_kwargs.cross_attention_dim + } + ).to(dtype=weight_dtype, device=device) + denoising_unet.load_state_dict( + torch.load(config.denoising_unet_path, map_location="cpu"), + strict=False + ) + + ## face locator init + face_locator = FaceLocator(320, conditioning_channels=3, block_out_channels=(16, 32, 96, 256)).to( + dtype=weight_dtype, device="cuda" + ) + face_locator.load_state_dict(torch.load(config.face_locator_path)) + + visualizer = FaceMeshVisualizer(draw_iris=False, draw_mouse=False) + + ### load audio processor params + audio_processor = load_audio_model(model_path=config.audio_model_path, device=device) + + ### load face detector params + face_detector = MTCNN(image_size=320, margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, device=device) + + ############# model_init finished ############# + + width, height = args.W, args.H + sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs) + scheduler = DDIMScheduler(**sched_kwargs) + + pipe = AudioPose2VideoPipeline( + vae=vae, + reference_unet=reference_unet, + denoising_unet=denoising_unet, + audio_guider=audio_processor, + face_locator=face_locator, + scheduler=scheduler, + ) + + pipe = pipe.to("cuda", dtype=weight_dtype) + + date_str = datetime.now().strftime("%Y%m%d") + time_str = datetime.now().strftime("%H%M") + save_dir_name = f"{time_str}--seed_{args.seed}-{args.W}x{args.H}" + save_dir = Path(f"output/{date_str}/{save_dir_name}") + save_dir.mkdir(exist_ok=True, parents=True) + + for ref_image_path in config["test_cases"].keys(): + for file_path in config["test_cases"][ref_image_path]: + if ".wav" in file_path: + audio_path = file_path + else: + pose_dir = file_path + + if args.seed is not None and args.seed > -1: + generator = torch.manual_seed(args.seed) + else: + generator = torch.manual_seed(random.randint(100, 1000000)) + + ref_name = Path(ref_image_path).stem + audio_name = Path(audio_path).stem + final_fps = args.fps + + ref_image_pil = Image.open(ref_image_path).convert("RGB") + + # ==================== face_locator ===================== + pose_list = [] + for index in range(len(os.listdir(pose_dir))): + tgt_musk_path = os.path.join(pose_dir, f"{index}.pkl") + + with open(tgt_musk_path, "rb") as f: + tgt_kpts = pickle.load(f) + tgt_musk = visualizer.draw_landmarks((args.W, args.H), tgt_kpts) + tgt_musk_pil = Image.fromarray(np.array(tgt_musk).astype(np.uint8)).convert('RGB') + pose_list.append(torch.Tensor(np.array(tgt_musk_pil)).to(dtype=weight_dtype, device="cuda").permute(2,0,1) / 255.0) + face_mask_tensor = torch.stack(pose_list, dim=1).unsqueeze(0) + + + video = pipe( + ref_image_pil, + audio_path, + face_mask_tensor, + width, + height, + args.L, + args.steps, + args.cfg, + generator=generator, + audio_sample_rate=args.sample_rate, + context_frames=12, + fps=final_fps, + context_overlap=3 + ).videos + + final_length = min(video.shape[2], face_mask_tensor.shape[2]) + video = torch.cat([video[:, :, :final_length, :, :], face_mask_tensor[:, :, :final_length, :, :].detach().cpu()], dim=-1) + save_videos_grid( + video, + f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4", + n_rows=2, + fps=final_fps, + ) + + from moviepy.editor import VideoFileClip, AudioFileClip + video_clip = VideoFileClip(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4") + audio_clip = AudioFileClip(audio_path) + video_clip = video_clip.set_audio(audio_clip) + video_clip.write_videofile(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}_withaudio.mp4", codec="libx264", audio_codec="aac") + print(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}_withaudio.mp4") + + +if __name__ == "__main__": + main() + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..cd20b89b1287eaf413b9921ab5c1b13cbb0e37df --- /dev/null +++ b/requirements.txt @@ -0,0 +1,17 @@ +torch>=2.0.1,<=2.2.2 +torchvision>=0.15.2,<=0.17.2 +torchaudio>=2.0.2,<=2.2.2 +mediapipe +transformers>=4.38.2 +diffusers==0.24.0 +torchmetrics +torchtyping +tqdm +ffmpeg-python==0.2.0 +facenet_pytorch==2.5.0 +moviepy==1.0.3 +einops==0.4.1 +omegaconf==2.3.0 +opencv-python +av==11.0.0 +gradio diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/models/attention.py b/src/models/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..b38f555a3f52839c34d4adea786db46796d5caac --- /dev/null +++ b/src/models/attention.py @@ -0,0 +1,338 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py + +from typing import Any, Dict, Optional + +import torch +from diffusers.models.attention import AdaLayerNorm, Attention, FeedForward +from diffusers.models.embeddings import SinusoidalPositionalEmbedding +from einops import rearrange +from torch import nn + + +class BasicTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + upcast_attention (`bool`, *optional*): + Whether to upcast the attention computation to float32. This is useful for mixed precision training. + norm_elementwise_affine (`bool`, *optional*, defaults to `True`): + Whether to use learnable elementwise affine parameters for normalization. + norm_type (`str`, *optional*, defaults to `"layer_norm"`): + The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`. + final_dropout (`bool` *optional*, defaults to False): + Whether to apply a final dropout after the last feed-forward layer. + attention_type (`str`, *optional*, defaults to `"default"`): + The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`. + positional_embeddings (`str`, *optional*, defaults to `None`): + The type of positional embeddings to apply to. + num_positional_embeddings (`int`, *optional*, defaults to `None`): + The maximum number of positional embeddings to apply. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single' + norm_eps: float = 1e-5, + final_dropout: bool = False, + attention_type: str = "default", + positional_embeddings: Optional[str] = None, + num_positional_embeddings: Optional[int] = None, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm_zero = ( + num_embeds_ada_norm is not None + ) and norm_type == "ada_norm_zero" + self.use_ada_layer_norm = ( + num_embeds_ada_norm is not None + ) and norm_type == "ada_norm" + self.use_ada_layer_norm_single = norm_type == "ada_norm_single" + self.use_layer_norm = norm_type == "layer_norm" + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + if positional_embeddings and (num_positional_embeddings is None): + raise ValueError( + "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined." + ) + + if positional_embeddings == "sinusoidal": + self.pos_embed = SinusoidalPositionalEmbedding( + dim, max_seq_length=num_positional_embeddings + ) + else: + self.pos_embed = None + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_zero: + self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm( + dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps + ) + + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + + # 3. Feed-forward + if not self.use_ada_layer_norm_single: + self.norm3 = nn.LayerNorm( + dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps + ) + + self.ff = FeedForward( + dim, + dropout=dropout, + activation_fn=activation_fn, + final_dropout=final_dropout, + ) + + # 4. Fuser + if attention_type == "gated" or attention_type == "gated-text-image": + self.fuser = GatedSelfAttentionDense( + dim, cross_attention_dim, num_attention_heads, attention_head_dim + ) + + # 5. Scale-shift for PixArt-Alpha. + if self.use_ada_layer_norm_single: + self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + ) -> torch.FloatTensor: + # Notice that normalization is always applied before the real computation in the following blocks. + # 0. Self-Attention + batch_size = hidden_states.shape[0] + + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + elif self.use_layer_norm: + norm_hidden_states = self.norm1(hidden_states) + elif self.use_ada_layer_norm_single: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) + ).chunk(6, dim=1) + norm_hidden_states = self.norm1(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa + norm_hidden_states = norm_hidden_states.squeeze(1) + else: + raise ValueError("Incorrect norm used") + + if self.pos_embed is not None: + norm_hidden_states = self.pos_embed(norm_hidden_states) + + # 1. Retrieve lora scale. + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + + # 2. Prepare GLIGEN inputs + cross_attention_kwargs = ( + cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} + ) + gligen_kwargs = cross_attention_kwargs.pop("gligen", None) + + attn_output = self.attn1( + norm_hidden_states, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + elif self.use_ada_layer_norm_single: + attn_output = gate_msa * attn_output + + hidden_states = attn_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + # 2.5 GLIGEN Control + if gligen_kwargs is not None: + hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) + + + # 4. Feed-forward + if not self.use_ada_layer_norm_single: + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = ( + norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + ) + + if self.use_ada_layer_norm_single: + norm_hidden_states = self.norm2(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp + + ff_output = self.ff(norm_hidden_states, scale=lora_scale) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + elif self.use_ada_layer_norm_single: + ff_output = gate_mlp * ff_output + + hidden_states = ff_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + return hidden_states + + +class TemporalBasicTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + self.use_ada_layer_norm = num_embeds_ada_norm is not None + self.unet_use_cross_frame_attention = unet_use_cross_frame_attention + self.unet_use_temporal_attention = unet_use_temporal_attention + + # SC-Attn + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + self.norm1 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim) + ) + + # Audio Cross-Attn + if cross_attention_dim is not None: + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + else: + self.attn2 = None + + if cross_attention_dim is not None: + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim) + ) + else: + self.norm2 = None + + # Feed-forward + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.norm3 = nn.LayerNorm(dim) + self.use_ada_layer_norm_zero = False + + # Temp-Attn + assert unet_use_temporal_attention is not None + if unet_use_temporal_attention: + self.attn_temp = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + nn.init.zeros_(self.attn_temp.to_out[0].weight.data) + self.norm_temp = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim) + ) + + def forward( + self, + hidden_states, + encoder_hidden_states=None, + audio_cond_fea = None, + timestep=None, + attention_mask=None, + video_length=None, + ): + ## implemented in mutual_self_attention.py + pass + return hidden_states diff --git a/src/models/face_locator.py b/src/models/face_locator.py new file mode 100644 index 0000000000000000000000000000000000000000..ce51aa512327a2d4dcdeed23edbf63c9172e3c06 --- /dev/null +++ b/src/models/face_locator.py @@ -0,0 +1,57 @@ +from typing import Tuple + +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init +from diffusers.models.modeling_utils import ModelMixin + +from src.models.motion_module import zero_module +from src.models.resnet import InflatedConv3d + + +class FaceLocator(ModelMixin): + def __init__( + self, + conditioning_embedding_channels: int, + conditioning_channels: int = 1, + block_out_channels: Tuple[int] = (16, 32, 64, 128), + ): + super().__init__() + self.conv_in = InflatedConv3d( + conditioning_channels, block_out_channels[0], kernel_size=3, padding=1 + ) + + self.blocks = nn.ModuleList([]) + + for i in range(len(block_out_channels) - 1): + channel_in = block_out_channels[i] + channel_out = block_out_channels[i + 1] + self.blocks.append( + InflatedConv3d(channel_in, channel_in, kernel_size=3, padding=1) + ) + self.blocks.append( + InflatedConv3d( + channel_in, channel_out, kernel_size=3, padding=1, stride=2 + ) + ) + + self.conv_out = zero_module( + InflatedConv3d( + block_out_channels[-1], + conditioning_embedding_channels, + kernel_size=3, + padding=1, + ) + ) + + def forward(self, conditioning): + embedding = self.conv_in(conditioning) + embedding = F.silu(embedding) + + for block in self.blocks: + embedding = block(embedding) + embedding = F.silu(embedding) + + embedding = self.conv_out(embedding) + + return embedding diff --git a/src/models/motion_module.py b/src/models/motion_module.py new file mode 100644 index 0000000000000000000000000000000000000000..44232766aed25ea0cc10e141e263fc265ee3aef2 --- /dev/null +++ b/src/models/motion_module.py @@ -0,0 +1,388 @@ +# Adapt from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/motion_module.py +import math +from dataclasses import dataclass +from typing import Callable, Optional + +import torch +from diffusers.models.attention import FeedForward +from diffusers.models.attention_processor import Attention, AttnProcessor +from diffusers.utils import BaseOutput +from diffusers.utils.import_utils import is_xformers_available +from einops import rearrange, repeat +from torch import nn + + +def zero_module(module): + # Zero out the parameters of a module and return it. + for p in module.parameters(): + p.detach().zero_() + return module + + +@dataclass +class TemporalTransformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + + +def get_motion_module(in_channels, motion_module_type: str, motion_module_kwargs: dict): + if motion_module_type == "Vanilla": + return VanillaTemporalModule( + in_channels=in_channels, + **motion_module_kwargs, + ) + else: + raise ValueError + + +class VanillaTemporalModule(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads=8, + num_transformer_block=2, + attention_block_types=("Temporal_Self", "Temporal_Self"), + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + temporal_attention_dim_div=1, + zero_initialize=True, + ): + super().__init__() + + self.temporal_transformer = TemporalTransformer3DModel( + in_channels=in_channels, + num_attention_heads=num_attention_heads, + attention_head_dim=in_channels + // num_attention_heads + // temporal_attention_dim_div, + num_layers=num_transformer_block, + attention_block_types=attention_block_types, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + + if zero_initialize: + self.temporal_transformer.proj_out = zero_module( + self.temporal_transformer.proj_out + ) + + def forward( + self, + input_tensor, + temb, + encoder_hidden_states, + attention_mask=None, + anchor_frame_idx=None, + ): + hidden_states = input_tensor + hidden_states = self.temporal_transformer( + hidden_states, encoder_hidden_states, attention_mask + ) + + output = hidden_states + return output + + +class TemporalTransformer3DModel(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads, + attention_head_dim, + num_layers, + attention_block_types=( + "Temporal_Self", + "Temporal_Self", + ), + dropout=0.0, + norm_num_groups=32, + cross_attention_dim=768, + activation_fn="geglu", + attention_bias=False, + upcast_attention=False, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + ): + super().__init__() + + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm( + num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True + ) + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + TemporalTransformerBlock( + dim=inner_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + attention_block_types=attention_block_types, + dropout=dropout, + norm_num_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + attention_bias=attention_bias, + upcast_attention=upcast_attention, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + for d in range(num_layers) + ] + ) + self.proj_out = nn.Linear(inner_dim, in_channels) + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None): + assert ( + hidden_states.dim() == 5 + ), f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + + batch, channel, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( + batch, height * weight, inner_dim + ) + hidden_states = self.proj_in(hidden_states) + + # Transformer Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + video_length=video_length, + ) + + # output + hidden_states = self.proj_out(hidden_states) + hidden_states = ( + hidden_states.reshape(batch, height, weight, inner_dim) + .permute(0, 3, 1, 2) + .contiguous() + ) + + output = hidden_states + residual + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) + + return output + + +class TemporalTransformerBlock(nn.Module): + def __init__( + self, + dim, + num_attention_heads, + attention_head_dim, + attention_block_types=( + "Temporal_Self", + "Temporal_Self", + ), + dropout=0.0, + norm_num_groups=32, + cross_attention_dim=768, + activation_fn="geglu", + attention_bias=False, + upcast_attention=False, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + ): + super().__init__() + + attention_blocks = [] + norms = [] + + for block_name in attention_block_types: + attention_blocks.append( + VersatileAttention( + attention_mode=block_name.split("_")[0], + cross_attention_dim=cross_attention_dim + if block_name.endswith("_Cross") + else None, + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + ) + norms.append(nn.LayerNorm(dim)) + + self.attention_blocks = nn.ModuleList(attention_blocks) + self.norms = nn.ModuleList(norms) + + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.ff_norm = nn.LayerNorm(dim) + + def forward( + self, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + video_length=None, + ): + for attention_block, norm in zip(self.attention_blocks, self.norms): + norm_hidden_states = norm(hidden_states) + hidden_states = ( + attention_block( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states + if attention_block.is_cross_attention + else None, + video_length=video_length, + ) + + hidden_states + ) + + hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states + + output = hidden_states + return output + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model, dropout=0.0, max_len=24): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) + ) + pe = torch.zeros(1, max_len, d_model) + pe[0, :, 0::2] = torch.sin(position * div_term) + pe[0, :, 1::2] = torch.cos(position * div_term) + self.register_buffer("pe", pe) + + def forward(self, x): + x = x + self.pe[:, : x.size(1)] + return self.dropout(x) + + +class VersatileAttention(Attention): + def __init__( + self, + attention_mode=None, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + assert attention_mode == "Temporal" + + self.attention_mode = attention_mode + self.is_cross_attention = kwargs["cross_attention_dim"] is not None + + self.pos_encoder = ( + PositionalEncoding( + kwargs["query_dim"], + dropout=0.0, + max_len=temporal_position_encoding_max_len, + ) + if (temporal_position_encoding and attention_mode == "Temporal") + else None + ) + + def extra_repr(self): + return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}" + + def set_use_memory_efficient_attention_xformers( + self, + use_memory_efficient_attention_xformers: bool, + attention_op: Optional[Callable] = None, + ): + if use_memory_efficient_attention_xformers: + if not is_xformers_available(): + raise ModuleNotFoundError( + ( + "Refer to https://github.com/facebookresearch/xformers for more information on how to install" + " xformers" + ), + name="xformers", + ) + elif not torch.cuda.is_available(): + raise ValueError( + "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is" + " only available for GPU " + ) + else: + try: + # Make sure we can run the memory efficient attention + _ = xformers.ops.memory_efficient_attention( + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + ) + except Exception as e: + raise e + + # XFormersAttnProcessor corrupts video generation and work with Pytorch 1.13. + # Pytorch 2.0.1 AttnProcessor works the same as XFormersAttnProcessor in Pytorch 1.13. + # You don't need XFormersAttnProcessor here. + # processor = XFormersAttnProcessor( + # attention_op=attention_op, + # ) + processor = AttnProcessor() + else: + processor = AttnProcessor() + + self.set_processor(processor) + + def forward( + self, + hidden_states, + encoder_hidden_states=None, + attention_mask=None, + video_length=None, + **cross_attention_kwargs, + ): + if self.attention_mode == "Temporal": + d = hidden_states.shape[1] # d means HxW + hidden_states = rearrange( + hidden_states, "(b f) d c -> (b d) f c", f=video_length + ) + + if self.pos_encoder is not None: + hidden_states = self.pos_encoder(hidden_states) + + encoder_hidden_states = ( + repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d) + if encoder_hidden_states is not None + else encoder_hidden_states + ) + + else: + raise NotImplementedError + + hidden_states = self.processor( + self, + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + + if self.attention_mode == "Temporal": + hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d) + + return hidden_states diff --git a/src/models/mutual_self_attention.py b/src/models/mutual_self_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..b5d4a7f54f73979398466616f6309cf37fc4bd30 --- /dev/null +++ b/src/models/mutual_self_attention.py @@ -0,0 +1,329 @@ +# Adapted from https://github.com/magic-research/magic-animate/blob/main/magicanimate/models/mutual_self_attention.py +from typing import Any, Dict, Optional + +import torch +from einops import rearrange + +from src.models.attention import TemporalBasicTransformerBlock + +from .attention import BasicTransformerBlock + + +def torch_dfs(model: torch.nn.Module): + result = [model] + for child in model.children(): + result += torch_dfs(child) + return result + + +class ReferenceAttentionControl: + def __init__( + self, + unet, + mode="write", + do_classifier_free_guidance=False, + attention_auto_machine_weight=float("inf"), + gn_auto_machine_weight=1.0, + style_fidelity=1.0, + reference_attn=True, + reference_adain=False, + fusion_blocks="midup", + batch_size=1, + ) -> None: + # 10. Modify self attention and group norm + self.unet = unet + assert mode in ["read", "write"] + assert fusion_blocks in ["midup", "full"] + self.reference_attn = reference_attn + self.reference_adain = reference_adain + self.fusion_blocks = fusion_blocks + self.register_reference_hooks( + mode, + do_classifier_free_guidance, + attention_auto_machine_weight, + gn_auto_machine_weight, + style_fidelity, + reference_attn, + reference_adain, + fusion_blocks, + batch_size=batch_size, + ) + + def register_reference_hooks( + self, + mode, + do_classifier_free_guidance, + attention_auto_machine_weight, + gn_auto_machine_weight, + style_fidelity, + reference_attn, + reference_adain, + dtype=torch.float16, + batch_size=1, + num_images_per_prompt=1, + device=torch.device("cuda"), + fusion_blocks="midup", + ): + MODE = mode + do_classifier_free_guidance = do_classifier_free_guidance + attention_auto_machine_weight = attention_auto_machine_weight + gn_auto_machine_weight = gn_auto_machine_weight + style_fidelity = style_fidelity + reference_attn = reference_attn + reference_adain = reference_adain + fusion_blocks = fusion_blocks + num_images_per_prompt = num_images_per_prompt + dtype = dtype + if do_classifier_free_guidance: + uc_mask = ( + torch.Tensor( + [1] * batch_size * num_images_per_prompt * 16 + + [0] * batch_size * num_images_per_prompt * 16 + ) + .to(device) + .bool() + ) + else: + uc_mask = ( + torch.Tensor([0] * batch_size * num_images_per_prompt * 2) + .to(device) + .bool() + ) + + def hacked_basic_transformer_inner_forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + audio_cond_fea: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + video_length=None, + audio_feature_ratio = 3.0 + ): + if self.use_ada_layer_norm: # False + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + ( + norm_hidden_states, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + ) = self.norm1( + hidden_states, + timestep, + class_labels, + hidden_dtype=hidden_states.dtype, + ) + else: + norm_hidden_states = self.norm1(hidden_states) + + # 1. Self-Attention + # self.only_cross_attention = False + cross_attention_kwargs = ( + cross_attention_kwargs if cross_attention_kwargs is not None else {} + ) + if self.only_cross_attention: + attn_output = self.attn1( + norm_hidden_states, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + else: + if MODE == "write": + self.bank.append(norm_hidden_states.clone()) + attn_output = self.attn1( + norm_hidden_states, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + if MODE == "read": + bank_feas = [ + rearrange( + d.unsqueeze(1).repeat(1, video_length, 1, 1), + "b t l c -> (b t) l c", + ) + for d in self.bank + ] + modify_norm_hidden_states = torch.cat( + [norm_hidden_states] + bank_feas, dim=1 + ) + # Reference Cross-Attention + hidden_states_uc = ( + self.attn1( + norm_hidden_states, + encoder_hidden_states=modify_norm_hidden_states, + attention_mask=attention_mask, + ) + + hidden_states + ) + if do_classifier_free_guidance: + ## inference with cfg + hidden_states_c = hidden_states_uc.clone() + _uc_mask = uc_mask.clone() + if hidden_states.shape[0] != _uc_mask.shape[0]: + _uc_mask = ( + torch.Tensor( + [1] * (hidden_states.shape[0] // 2) + + [0] * (hidden_states.shape[0] // 2) + ) + .to(device) + .bool() + ) + hidden_states_c[_uc_mask] = ( + self.attn1( + norm_hidden_states[_uc_mask], + encoder_hidden_states=norm_hidden_states[_uc_mask], # B * 4096 * 768 + attention_mask=attention_mask, + ) + + hidden_states[_uc_mask] + ) + hidden_states = hidden_states_c.clone() + else: + hidden_states = hidden_states_uc + + if self.attn2 is not None: + # Audio Cross-Attention + norm_hidden_states = ( + self.norm2(hidden_states, timestep) + if self.use_ada_layer_norm + else self.norm2(hidden_states) + ) + hidden_states = ( + self.attn2( + norm_hidden_states, + encoder_hidden_states=audio_cond_fea, + attention_mask=attention_mask, + ) * audio_feature_ratio + + hidden_states + ) + + # Feed-forward + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + # Temporal-Attention + return hidden_states + + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = ( + norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + ) + + ff_output = self.ff(norm_hidden_states) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = ff_output + hidden_states + + return hidden_states + + if self.reference_attn: + if self.fusion_blocks == "midup": + attn_modules = [ + module + for module in ( + torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks) + ) + if isinstance(module, BasicTransformerBlock) + or isinstance(module, TemporalBasicTransformerBlock) + ] + elif self.fusion_blocks == "full": + attn_modules = [ + module + for module in torch_dfs(self.unet) + if isinstance(module, BasicTransformerBlock) + or isinstance(module, TemporalBasicTransformerBlock) + ] + attn_modules = sorted( + attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + + for i, module in enumerate(attn_modules): + module._original_inner_forward = module.forward + if isinstance(module, BasicTransformerBlock): + module.forward = hacked_basic_transformer_inner_forward.__get__( + module, BasicTransformerBlock + ) + if isinstance(module, TemporalBasicTransformerBlock): + module.forward = hacked_basic_transformer_inner_forward.__get__( + module, TemporalBasicTransformerBlock + ) + + module.bank = [] + module.attn_weight = float(i) / float(len(attn_modules)) + + def update(self, writer, do_classifier_free_guidance=False, dtype=torch.float16): + if self.reference_attn: + if self.fusion_blocks == "midup": + reader_attn_modules = [ + module + for module in ( + torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks) + ) + if isinstance(module, TemporalBasicTransformerBlock) + ] + writer_attn_modules = [ + module + for module in ( + torch_dfs(writer.unet.mid_block) + + torch_dfs(writer.unet.up_blocks) + ) + if isinstance(module, BasicTransformerBlock) + ] + elif self.fusion_blocks == "full": + reader_attn_modules = [ + module + for module in torch_dfs(self.unet) + if isinstance(module, TemporalBasicTransformerBlock) + ] + writer_attn_modules = [ + module + for module in torch_dfs(writer.unet) + if isinstance(module, BasicTransformerBlock) + ] + reader_attn_modules = sorted( + reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + writer_attn_modules = sorted( + writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + for r, w in zip(reader_attn_modules, writer_attn_modules): + if do_classifier_free_guidance: + r.bank = [torch.cat([v, v]).to(dtype) for v in w.bank] + else: + r.bank = [v.clone().to(dtype) for v in w.bank] + + def clear(self): + if self.reference_attn: + if self.fusion_blocks == "midup": + reader_attn_modules = [ + module + for module in ( + torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks) + ) + if isinstance(module, BasicTransformerBlock) + or isinstance(module, TemporalBasicTransformerBlock) + ] + elif self.fusion_blocks == "full": + reader_attn_modules = [ + module + for module in torch_dfs(self.unet) + if isinstance(module, BasicTransformerBlock) + or isinstance(module, TemporalBasicTransformerBlock) + ] + reader_attn_modules = sorted( + reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + for r in reader_attn_modules: + r.bank.clear() diff --git a/src/models/resnet.py b/src/models/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..b489aee2f28a13954809827b1f2a0e825b893e2e --- /dev/null +++ b/src/models/resnet.py @@ -0,0 +1,252 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + + +class InflatedConv3d(nn.Conv2d): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class InflatedGroupNorm(nn.GroupNorm): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class Upsample3D(nn.Module): + def __init__( + self, + channels, + use_conv=False, + use_conv_transpose=False, + out_channels=None, + name="conv", + ): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + self.name = name + + conv = None + if use_conv_transpose: + raise NotImplementedError + elif use_conv: + self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1) + + def forward(self, hidden_states, output_size=None): + assert hidden_states.shape[1] == self.channels + + if self.use_conv_transpose: + raise NotImplementedError + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + dtype = hidden_states.dtype + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(torch.float32) + + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + hidden_states = hidden_states.contiguous() + + # if `output_size` is passed we force the interpolation output + # size and do not make use of `scale_factor=2` + if output_size is None: + hidden_states = F.interpolate( + hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest" + ) + else: + hidden_states = F.interpolate( + hidden_states, size=output_size, mode="nearest" + ) + + # If the input is bfloat16, we cast back to bfloat16 + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(dtype) + + # if self.use_conv: + # if self.name == "conv": + # hidden_states = self.conv(hidden_states) + # else: + # hidden_states = self.Conv2d_0(hidden_states) + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class Downsample3D(nn.Module): + def __init__( + self, channels, use_conv=False, out_channels=None, padding=1, name="conv" + ): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.padding = padding + stride = 2 + self.name = name + + if use_conv: + self.conv = InflatedConv3d( + self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + raise NotImplementedError + + def forward(self, hidden_states): + assert hidden_states.shape[1] == self.channels + if self.use_conv and self.padding == 0: + raise NotImplementedError + + assert hidden_states.shape[1] == self.channels + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class ResnetBlock3D(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout=0.0, + temb_channels=512, + groups=32, + groups_out=None, + pre_norm=True, + eps=1e-6, + non_linearity="swish", + time_embedding_norm="default", + output_scale_factor=1.0, + use_in_shortcut=None, + use_inflated_groupnorm=None, + ): + super().__init__() + self.pre_norm = pre_norm + self.pre_norm = True + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + self.time_embedding_norm = time_embedding_norm + self.output_scale_factor = output_scale_factor + + if groups_out is None: + groups_out = groups + + assert use_inflated_groupnorm != None + if use_inflated_groupnorm: + self.norm1 = InflatedGroupNorm( + num_groups=groups, num_channels=in_channels, eps=eps, affine=True + ) + else: + self.norm1 = torch.nn.GroupNorm( + num_groups=groups, num_channels=in_channels, eps=eps, affine=True + ) + + self.conv1 = InflatedConv3d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + + if temb_channels is not None: + if self.time_embedding_norm == "default": + time_emb_proj_out_channels = out_channels + elif self.time_embedding_norm == "scale_shift": + time_emb_proj_out_channels = out_channels * 2 + else: + raise ValueError( + f"unknown time_embedding_norm : {self.time_embedding_norm} " + ) + + self.time_emb_proj = torch.nn.Linear( + temb_channels, time_emb_proj_out_channels + ) + else: + self.time_emb_proj = None + + if use_inflated_groupnorm: + self.norm2 = InflatedGroupNorm( + num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True + ) + else: + self.norm2 = torch.nn.GroupNorm( + num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True + ) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = InflatedConv3d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + + if non_linearity == "swish": + self.nonlinearity = lambda x: F.silu(x) + elif non_linearity == "mish": + self.nonlinearity = Mish() + elif non_linearity == "silu": + self.nonlinearity = nn.SiLU() + + self.use_in_shortcut = ( + self.in_channels != self.out_channels + if use_in_shortcut is None + else use_in_shortcut + ) + + self.conv_shortcut = None + if self.use_in_shortcut: + self.conv_shortcut = InflatedConv3d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, input_tensor, temb): + hidden_states = input_tensor + + hidden_states = self.norm1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.conv1(hidden_states) + + if temb is not None: + temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None] + + if temb is not None and self.time_embedding_norm == "default": + hidden_states = hidden_states + temb + + hidden_states = self.norm2(hidden_states) + + if temb is not None and self.time_embedding_norm == "scale_shift": + scale, shift = torch.chunk(temb, 2, dim=1) + hidden_states = hidden_states * (1 + scale) + shift + + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.conv_shortcut is not None: + input_tensor = self.conv_shortcut(input_tensor) + + output_tensor = (input_tensor + hidden_states) / self.output_scale_factor + + return output_tensor + + +class Mish(torch.nn.Module): + def forward(self, hidden_states): + return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states)) diff --git a/src/models/transformer_2d.py b/src/models/transformer_2d.py new file mode 100644 index 0000000000000000000000000000000000000000..f1f66e948bf31f8aca870fff0225b9194b429fb0 --- /dev/null +++ b/src/models/transformer_2d.py @@ -0,0 +1,396 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py +from dataclasses import dataclass +from typing import Any, Dict, Optional + +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.embeddings import CaptionProjection +from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear +from diffusers.models.modeling_utils import ModelMixin +from diffusers.models.normalization import AdaLayerNormSingle +from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_torch_version +from torch import nn + +from .attention import BasicTransformerBlock + + +@dataclass +class Transformer2DModelOutput(BaseOutput): + """ + The output of [`Transformer2DModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): + The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability + distributions for the unnoised latent pixels. + """ + + sample: torch.FloatTensor + ref_feature: torch.FloatTensor + + +class Transformer2DModel(ModelMixin, ConfigMixin): + """ + A 2D Transformer model for image-like data. + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + The number of channels in the input and output (specify if the input is **continuous**). + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. + sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). + This is fixed during training since it is used to learn a number of position embeddings. + num_vector_embeds (`int`, *optional*): + The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). + Includes the class for the masked latent pixel. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. + num_embeds_ada_norm ( `int`, *optional*): + The number of diffusion steps used during training. Pass if at least one of the norm_layers is + `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are + added to the hidden states. + + During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. + attention_bias (`bool`, *optional*): + Configure if the `TransformerBlocks` attention should contain a bias parameter. + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + sample_size: Optional[int] = None, + num_vector_embeds: Optional[int] = None, + patch_size: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_type: str = "layer_norm", + norm_elementwise_affine: bool = True, + norm_eps: float = 1e-5, + attention_type: str = "default", + caption_channels: int = None, + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv + linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear + + # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` + # Define whether input is continuous or discrete depending on configuration + self.is_input_continuous = (in_channels is not None) and (patch_size is None) + self.is_input_vectorized = num_vector_embeds is not None + self.is_input_patches = in_channels is not None and patch_size is not None + + if norm_type == "layer_norm" and num_embeds_ada_norm is not None: + deprecation_message = ( + f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" + " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." + " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" + " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" + " would be very nice if you could open a Pull request for the `transformer/config.json` file" + ) + deprecate( + "norm_type!=num_embeds_ada_norm", + "1.0.0", + deprecation_message, + standard_warn=False, + ) + norm_type = "ada_norm" + + if self.is_input_continuous and self.is_input_vectorized: + raise ValueError( + f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" + " sure that either `in_channels` or `num_vector_embeds` is None." + ) + elif self.is_input_vectorized and self.is_input_patches: + raise ValueError( + f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" + " sure that either `num_vector_embeds` or `num_patches` is None." + ) + elif ( + not self.is_input_continuous + and not self.is_input_vectorized + and not self.is_input_patches + ): + raise ValueError( + f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" + f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." + ) + + # 2. Define input layers + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm( + num_groups=norm_num_groups, + num_channels=in_channels, + eps=1e-6, + affine=True, + ) + if use_linear_projection: + self.proj_in = linear_cls(in_channels, inner_dim) + else: + self.proj_in = conv_cls( + in_channels, inner_dim, kernel_size=1, stride=1, padding=0 + ) + + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + double_self_attention=double_self_attention, + upcast_attention=upcast_attention, + norm_type=norm_type, + norm_elementwise_affine=norm_elementwise_affine, + norm_eps=norm_eps, + attention_type=attention_type, + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + self.out_channels = in_channels if out_channels is None else out_channels + # TODO: should use out_channels for continuous projections + if use_linear_projection: + self.proj_out = linear_cls(inner_dim, in_channels) + else: + self.proj_out = conv_cls( + inner_dim, in_channels, kernel_size=1, stride=1, padding=0 + ) + + # 5. PixArt-Alpha blocks. + self.adaln_single = None + self.use_additional_conditions = False + if norm_type == "ada_norm_single": + self.use_additional_conditions = self.config.sample_size == 128 + # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use + # additional conditions until we find better name + self.adaln_single = AdaLayerNormSingle( + inner_dim, use_additional_conditions=self.use_additional_conditions + ) + + self.caption_projection = None + if caption_channels is not None: + self.caption_projection = CaptionProjection( + in_features=caption_channels, hidden_size=inner_dim + ) + + self.gradient_checkpointing = False + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + added_cond_kwargs: Dict[str, torch.Tensor] = None, + class_labels: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + ): + """ + The [`Transformer2DModel`] forward method. + + Args: + hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): + Input `hidden_states`. + encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + timestep ( `torch.LongTensor`, *optional*): + Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. + class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): + Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in + `AdaLayerZeroNorm`. + cross_attention_kwargs ( `Dict[str, Any]`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + attention_mask ( `torch.Tensor`, *optional*): + An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask + is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large + negative values to the attention scores corresponding to "discard" tokens. + encoder_attention_mask ( `torch.Tensor`, *optional*): + Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: + + * Mask `(batch, sequence_length)` True = keep, False = discard. + * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. + + If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format + above. This bias will be added to the cross-attention scores. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + + Returns: + If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a + `tuple` where the first element is the sample tensor. + """ + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. + # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. + # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None and attention_mask.ndim == 2: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: + encoder_attention_mask = ( + 1 - encoder_attention_mask.to(hidden_states.dtype) + ) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # Retrieve lora scale. + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + + # 1. Input + batch, _, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = ( + self.proj_in(hidden_states, scale=lora_scale) + if not USE_PEFT_BACKEND + else self.proj_in(hidden_states) + ) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( + batch, height * width, inner_dim + ) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( + batch, height * width, inner_dim + ) + hidden_states = ( + self.proj_in(hidden_states, scale=lora_scale) + if not USE_PEFT_BACKEND + else self.proj_in(hidden_states) + ) + + # 2. Blocks + if self.caption_projection is not None: + batch_size = hidden_states.shape[0] + encoder_hidden_states = self.caption_projection(encoder_hidden_states) + encoder_hidden_states = encoder_hidden_states.view( + batch_size, -1, hidden_states.shape[-1] + ) + + ref_feature = hidden_states.reshape(batch, height, width, inner_dim) + for block in self.transformer_blocks: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = ( + {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + attention_mask, + encoder_hidden_states, + encoder_attention_mask, + timestep, + cross_attention_kwargs, + class_labels, + **ckpt_kwargs, + ) + else: + hidden_states = block( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + timestep=timestep, + cross_attention_kwargs=cross_attention_kwargs, + class_labels=class_labels, + ) + + # 3. Output + if self.is_input_continuous: + if not self.use_linear_projection: + hidden_states = ( + hidden_states.reshape(batch, height, width, inner_dim) + .permute(0, 3, 1, 2) + .contiguous() + ) + hidden_states = ( + self.proj_out(hidden_states, scale=lora_scale) + if not USE_PEFT_BACKEND + else self.proj_out(hidden_states) + ) + else: + hidden_states = ( + self.proj_out(hidden_states, scale=lora_scale) + if not USE_PEFT_BACKEND + else self.proj_out(hidden_states) + ) + hidden_states = ( + hidden_states.reshape(batch, height, width, inner_dim) + .permute(0, 3, 1, 2) + .contiguous() + ) + + output = hidden_states + residual + if not return_dict: + return (output, ref_feature) + + return Transformer2DModelOutput(sample=output, ref_feature=ref_feature) diff --git a/src/models/transformer_3d.py b/src/models/transformer_3d.py new file mode 100644 index 0000000000000000000000000000000000000000..dc83bf4fb6289af01985a40b3e049547a17c36fe --- /dev/null +++ b/src/models/transformer_3d.py @@ -0,0 +1,175 @@ +from dataclasses import dataclass +from typing import Optional + +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models import ModelMixin +from diffusers.utils import BaseOutput +from diffusers.utils.import_utils import is_xformers_available +from einops import rearrange, repeat +from torch import nn + +from .attention import TemporalBasicTransformerBlock + + +@dataclass +class Transformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + + +class Transformer3DModel(ModelMixin, ConfigMixin): + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # Define input layers + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm( + num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True + ) + if use_linear_projection: + self.proj_in = nn.Linear(in_channels, inner_dim) + else: + self.proj_in = nn.Conv2d( + in_channels, inner_dim, kernel_size=1, stride=1, padding=0 + ) + + # Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + TemporalBasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + if use_linear_projection: + self.proj_out = nn.Linear(in_channels, inner_dim) + else: + self.proj_out = nn.Conv2d( + inner_dim, in_channels, kernel_size=1, stride=1, padding=0 + ) + + self.gradient_checkpointing = False + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + def forward( + self, + hidden_states, + encoder_hidden_states=None, + audio_cond_fea=None, + timestep=None, + return_dict: bool = True, + ): + # Input + assert ( + hidden_states.dim() == 5 + ), f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + if audio_cond_fea.shape[0] != hidden_states.shape[0]: + if len(audio_cond_fea.shape) == 3: + audio_cond_fea = rearrange( + audio_cond_fea, "b f c -> (b f) 1 c" + ) + elif len(audio_cond_fea.shape) == 4: + audio_cond_fea = rearrange( + audio_cond_fea, "b f n c -> (b f) n c" + ) + batch, channel, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( + batch, height * weight, inner_dim + ) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape( + batch, height * weight, inner_dim + ) + hidden_states = self.proj_in(hidden_states) + + # Blocks + for i, block in enumerate(self.transformer_blocks): + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + audio_cond_fea=audio_cond_fea, + timestep=timestep, + video_length=video_length, + ) + + # Output + if not self.use_linear_projection: + hidden_states = ( + hidden_states.reshape(batch, height, weight, inner_dim) + .permute(0, 3, 1, 2) + .contiguous() + ) + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = ( + hidden_states.reshape(batch, height, weight, inner_dim) + .permute(0, 3, 1, 2) + .contiguous() + ) + + output = hidden_states + residual + + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) + if not return_dict: + return (output,) + + return Transformer3DModelOutput(sample=output) diff --git a/src/models/unet_2d_blocks.py b/src/models/unet_2d_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..fd3d607f92f810baa053153a3c6192f6c2241f19 --- /dev/null +++ b/src/models/unet_2d_blocks.py @@ -0,0 +1,1074 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py +from typing import Any, Dict, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import Attention +from diffusers.models.dual_transformer_2d import DualTransformer2DModel +from diffusers.models.resnet import Downsample2D, ResnetBlock2D, Upsample2D +from diffusers.utils import is_torch_version, logging +from diffusers.utils.torch_utils import apply_freeu +from torch import nn + +from .transformer_2d import Transformer2DModel + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def get_down_block( + down_block_type: str, + num_layers: int, + in_channels: int, + out_channels: int, + temb_channels: int, + add_downsample: bool, + resnet_eps: float, + resnet_act_fn: str, + transformer_layers_per_block: int = 1, + num_attention_heads: Optional[int] = None, + resnet_groups: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + downsample_padding: Optional[int] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + attention_type: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: float = 1.0, + cross_attention_norm: Optional[str] = None, + attention_head_dim: Optional[int] = None, + downsample_type: Optional[str] = None, + dropout: float = 0.0, +): + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + down_block_type = ( + down_block_type[7:] + if down_block_type.startswith("UNetRes") + else down_block_type + ) + if down_block_type == "DownBlock2D": + return DownBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "CrossAttnDownBlock2D": + if cross_attention_dim is None: + raise ValueError( + "cross_attention_dim must be specified for CrossAttnDownBlock2D" + ) + return CrossAttnDownBlock2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + dropout=dropout, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type: str, + num_layers: int, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + add_upsample: bool, + resnet_eps: float, + resnet_act_fn: str, + resolution_idx: Optional[int] = None, + transformer_layers_per_block: int = 1, + num_attention_heads: Optional[int] = None, + resnet_groups: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + attention_type: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: float = 1.0, + cross_attention_norm: Optional[str] = None, + attention_head_dim: Optional[int] = None, + upsample_type: Optional[str] = None, + dropout: float = 0.0, +) -> nn.Module: + # If attn head dim is not defined, we default it to the number of heads + if attention_head_dim is None: + logger.warn( + f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}." + ) + attention_head_dim = num_attention_heads + + up_block_type = ( + up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + ) + if up_block_type == "UpBlock2D": + return UpBlock2D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif up_block_type == "CrossAttnUpBlock2D": + if cross_attention_dim is None: + raise ValueError( + "cross_attention_dim must be specified for CrossAttnUpBlock2D" + ) + return CrossAttnUpBlock2D( + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + dropout=dropout, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + ) + + raise ValueError(f"{up_block_type} does not exist.") + + +class AutoencoderTinyBlock(nn.Module): + """ + Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU + blocks. + + Args: + in_channels (`int`): The number of input channels. + out_channels (`int`): The number of output channels. + act_fn (`str`): + ` The activation function to use. Supported values are `"swish"`, `"mish"`, `"gelu"`, and `"relu"`. + + Returns: + `torch.FloatTensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to + `out_channels`. + """ + + def __init__(self, in_channels: int, out_channels: int, act_fn: str): + super().__init__() + act_fn = get_activation(act_fn) + self.conv = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), + act_fn, + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + act_fn, + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + ) + self.skip = ( + nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) + if in_channels != out_channels + else nn.Identity() + ) + self.fuse = nn.ReLU() + + def forward(self, x: torch.FloatTensor) -> torch.FloatTensor: + return self.fuse(self.conv(x) + self.skip(x)) + + +class UNetMidBlock2D(nn.Module): + """ + A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks. + + Args: + in_channels (`int`): The number of input channels. + temb_channels (`int`): The number of temporal embedding channels. + dropout (`float`, *optional*, defaults to 0.0): The dropout rate. + num_layers (`int`, *optional*, defaults to 1): The number of residual blocks. + resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks. + resnet_time_scale_shift (`str`, *optional*, defaults to `default`): + The type of normalization to apply to the time embeddings. This can help to improve the performance of the + model on tasks with long-range temporal dependencies. + resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks. + resnet_groups (`int`, *optional*, defaults to 32): + The number of groups to use in the group normalization layers of the resnet blocks. + attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks. + resnet_pre_norm (`bool`, *optional*, defaults to `True`): + Whether to use pre-normalization for the resnet blocks. + add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks. + attention_head_dim (`int`, *optional*, defaults to 1): + Dimension of a single attention head. The number of attention heads is determined based on this value and + the number of input channels. + output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor. + + Returns: + `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size, + in_channels, height, width)`. + + """ + + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", # default, spatial + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + attn_groups: Optional[int] = None, + resnet_pre_norm: bool = True, + add_attention: bool = True, + attention_head_dim: int = 1, + output_scale_factor: float = 1.0, + ): + super().__init__() + resnet_groups = ( + resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + ) + self.add_attention = add_attention + + if attn_groups is None: + attn_groups = ( + resnet_groups if resnet_time_scale_shift == "default" else None + ) + + # there is always at least one resnet + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + + if attention_head_dim is None: + logger.warn( + f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}." + ) + attention_head_dim = in_channels + + for _ in range(num_layers): + if self.add_attention: + attentions.append( + Attention( + in_channels, + heads=in_channels // attention_head_dim, + dim_head=attention_head_dim, + rescale_output_factor=output_scale_factor, + eps=resnet_eps, + norm_num_groups=attn_groups, + spatial_norm_dim=temb_channels + if resnet_time_scale_shift == "spatial" + else None, + residual_connection=True, + bias=True, + upcast_softmax=True, + _from_deprecated_attn_block=True, + ) + ) + else: + attentions.append(None) + + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + def forward( + self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None + ) -> torch.FloatTensor: + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + if attn is not None: + hidden_states = attn(hidden_states, temb=temb) + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class UNetMidBlock2DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + output_scale_factor: float = 1.0, + cross_attention_dim: int = 1280, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + upcast_attention: bool = False, + attention_type: str = "default", + ): + super().__init__() + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + resnet_groups = ( + resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + ) + + # support for variable transformer layers per block + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + # there is always at least one resnet + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + + for i in range(num_layers): + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale) + for attn, resnet in zip(self.attentions, self.resnets[1:]): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = ( + {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + ) + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + else: + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + hidden_states = resnet(hidden_states, temb, scale=lora_scale) + + return hidden_states + + +class CrossAttnDownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + downsample_padding: int = 1, + add_downsample: bool = True, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + attention_type: str = "default", + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + additional_residuals: Optional[torch.FloatTensor] = None, + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + output_states = () + + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + + blocks = list(zip(self.resnets, self.attentions)) + + for i, (resnet, attn) in enumerate(blocks): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = ( + {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + else: + hidden_states = resnet(hidden_states, temb, scale=lora_scale) + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + + # apply additional residuals to the output of the last pair of resnet and attention blocks + if i == len(blocks) - 1 and additional_residuals is not None: + hidden_states = hidden_states + additional_residuals + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states, scale=lora_scale) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + + +class DownBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_downsample: bool = True, + downsample_padding: int = 1, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + scale: float = 1.0, + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + output_states = () + + for resnet in self.resnets: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + use_reentrant=False, + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = resnet(hidden_states, temb, scale=scale) + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states, scale=scale) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnUpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + attention_type: str = "default", + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList( + [Upsample2D(out_channels, use_conv=True, out_channels=out_channels)] + ) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + upsample_size: Optional[int] = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + is_freeu_enabled = ( + getattr(self, "s1", None) + and getattr(self, "s2", None) + and getattr(self, "b1", None) + and getattr(self, "b2", None) + ) + + for resnet, attn in zip(self.resnets, self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # FreeU: Only operate on the first two stages + if is_freeu_enabled: + hidden_states, res_hidden_states = apply_freeu( + self.resolution_idx, + hidden_states, + res_hidden_states, + s1=self.s1, + s2=self.s2, + b1=self.b1, + b2=self.b2, + ) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = ( + {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + else: + hidden_states = resnet(hidden_states, temb, scale=lora_scale) + hidden_states, ref_feature = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler( + hidden_states, upsample_size, scale=lora_scale + ) + + return hidden_states + + +class UpBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList( + [Upsample2D(out_channels, use_conv=True, out_channels=out_channels)] + ) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + upsample_size: Optional[int] = None, + scale: float = 1.0, + ) -> torch.FloatTensor: + is_freeu_enabled = ( + getattr(self, "s1", None) + and getattr(self, "s2", None) + and getattr(self, "b1", None) + and getattr(self, "b2", None) + ) + + for resnet in self.resnets: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # FreeU: Only operate on the first two stages + if is_freeu_enabled: + hidden_states, res_hidden_states = apply_freeu( + self.resolution_idx, + hidden_states, + res_hidden_states, + s1=self.s1, + s2=self.s2, + b1=self.b1, + b2=self.b2, + ) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + use_reentrant=False, + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + else: + hidden_states = resnet(hidden_states, temb, scale=scale) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size, scale=scale) + + return hidden_states diff --git a/src/models/unet_2d_condition.py b/src/models/unet_2d_condition.py new file mode 100644 index 0000000000000000000000000000000000000000..2b77c45e2baac28e36cd60cdd478ee4fd6ce8634 --- /dev/null +++ b/src/models/unet_2d_condition.py @@ -0,0 +1,1308 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.utils.checkpoint +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import UNet2DConditionLoadersMixin +from diffusers.models.activations import get_activation +from diffusers.models.attention_processor import ( + ADDED_KV_ATTENTION_PROCESSORS, + CROSS_ATTENTION_PROCESSORS, + AttentionProcessor, + AttnAddedKVProcessor, + AttnProcessor, +) +from diffusers.models.embeddings import ( + GaussianFourierProjection, + ImageHintTimeEmbedding, + ImageProjection, + ImageTimeEmbedding, + PositionNet, + TextImageProjection, + TextImageTimeEmbedding, + TextTimeEmbedding, + TimestepEmbedding, + Timesteps, +) +from diffusers.models.modeling_utils import ModelMixin +from diffusers.utils import ( + USE_PEFT_BACKEND, + BaseOutput, + deprecate, + logging, + scale_lora_layers, + unscale_lora_layers, +) + +from .unet_2d_blocks import ( + UNetMidBlock2D, + UNetMidBlock2DCrossAttn, + get_down_block, + get_up_block, +) + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNet2DConditionOutput(BaseOutput): + """ + The output of [`UNet2DConditionModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. + """ + + sample: torch.FloatTensor = None + ref_features: Tuple[torch.FloatTensor] = None + + +class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): + r""" + A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample + shaped output. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented + for all models (such as downloading or saving). + + Parameters: + sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): + Height and width of input/output sample. + in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample. + out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. + center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. + flip_sin_to_cos (`bool`, *optional*, defaults to `False`): + Whether to flip the sin to cos in the time embedding. + freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. + down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): + The tuple of downsample blocks to use. + mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): + Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or + `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. + up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): + The tuple of upsample blocks to use. + only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): + Whether to include self-attention in the basic transformer blocks, see + [`~models.attention.BasicTransformerBlock`]. + block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): + The tuple of output channels for each block. + layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. + downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. + mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. + norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. + If `None`, normalization and activation layers is skipped in post-processing. + norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. + cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): + The dimension of the cross attention features. + transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for + [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], + [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. + reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling + blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for + [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], + [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. + encoder_hid_dim (`int`, *optional*, defaults to None): + If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` + dimension to `cross_attention_dim`. + encoder_hid_dim_type (`str`, *optional*, defaults to `None`): + If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text + embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. + attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. + num_attention_heads (`int`, *optional*): + The number of attention heads. If not defined, defaults to `attention_head_dim` + resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config + for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. + class_embed_type (`str`, *optional*, defaults to `None`): + The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, + `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. + addition_embed_type (`str`, *optional*, defaults to `None`): + Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or + "text". "text" will use the `TextTimeEmbedding` layer. + addition_time_embed_dim: (`int`, *optional*, defaults to `None`): + Dimension for the timestep embeddings. + num_class_embeds (`int`, *optional*, defaults to `None`): + Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing + class conditioning with `class_embed_type` equal to `None`. + time_embedding_type (`str`, *optional*, defaults to `positional`): + The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. + time_embedding_dim (`int`, *optional*, defaults to `None`): + An optional override for the dimension of the projected time embedding. + time_embedding_act_fn (`str`, *optional*, defaults to `None`): + Optional activation function to use only once on the time embeddings before they are passed to the rest of + the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`. + timestep_post_act (`str`, *optional*, defaults to `None`): + The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. + time_cond_proj_dim (`int`, *optional*, defaults to `None`): + The dimension of `cond_proj` layer in the timestep embedding. + conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`, + *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`, + *optional*): The dimension of the `class_labels` input when + `class_embed_type="projection"`. Required when `class_embed_type="projection"`. + class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time + embeddings with the class embeddings. + mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): + Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If + `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the + `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False` + otherwise. + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D", + ), + mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", + up_block_types: Tuple[str] = ( + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + ), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: Union[int, Tuple[int]] = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + dropout: float = 0.0, + act_fn: str = "silu", + norm_num_groups: Optional[int] = 32, + norm_eps: float = 1e-5, + cross_attention_dim: Union[int, Tuple[int]] = 1280, + transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, + reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None, + encoder_hid_dim: Optional[int] = None, + encoder_hid_dim_type: Optional[str] = None, + attention_head_dim: Union[int, Tuple[int]] = 8, + num_attention_heads: Optional[Union[int, Tuple[int]]] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + addition_embed_type: Optional[str] = None, + addition_time_embed_dim: Optional[int] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + resnet_skip_time_act: bool = False, + resnet_out_scale_factor: int = 1.0, + time_embedding_type: str = "positional", + time_embedding_dim: Optional[int] = None, + time_embedding_act_fn: Optional[str] = None, + timestep_post_act: Optional[str] = None, + time_cond_proj_dim: Optional[int] = None, + conv_in_kernel: int = 3, + conv_out_kernel: int = 3, + projection_class_embeddings_input_dim: Optional[int] = None, + attention_type: str = "default", + class_embeddings_concat: bool = False, + mid_block_only_cross_attention: Optional[bool] = None, + cross_attention_norm: Optional[str] = None, + addition_embed_type_num_heads=64, + ): + super().__init__() + + self.sample_size = sample_size + + if num_attention_heads is not None: + raise ValueError( + "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." + ) + + # If `num_attention_heads` is not defined (which is the case for most models) + # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. + # The reason for this behavior is to correct for incorrectly named variables that were introduced + # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 + # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking + # which is why we correct for the naming here. + num_attention_heads = num_attention_heads or attention_head_dim + + # Check inputs + if len(down_block_types) != len(up_block_types): + raise ValueError( + f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(only_cross_attention, bool) and len( + only_cross_attention + ) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len( + down_block_types + ): + raise ValueError( + f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len( + down_block_types + ): + raise ValueError( + f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." + ) + + if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len( + down_block_types + ): + raise ValueError( + f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(layers_per_block, int) and len(layers_per_block) != len( + down_block_types + ): + raise ValueError( + f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." + ) + if ( + isinstance(transformer_layers_per_block, list) + and reverse_transformer_layers_per_block is None + ): + for layer_number_per_block in transformer_layers_per_block: + if isinstance(layer_number_per_block, list): + raise ValueError( + "Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet." + ) + + # input + conv_in_padding = (conv_in_kernel - 1) // 2 + self.conv_in = nn.Conv2d( + in_channels, + block_out_channels[0], + kernel_size=conv_in_kernel, + padding=conv_in_padding, + ) + + # time + if time_embedding_type == "fourier": + time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 + if time_embed_dim % 2 != 0: + raise ValueError( + f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}." + ) + self.time_proj = GaussianFourierProjection( + time_embed_dim // 2, + set_W_to_weight=False, + log=False, + flip_sin_to_cos=flip_sin_to_cos, + ) + timestep_input_dim = time_embed_dim + elif time_embedding_type == "positional": + time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 + + self.time_proj = Timesteps( + block_out_channels[0], flip_sin_to_cos, freq_shift + ) + timestep_input_dim = block_out_channels[0] + else: + raise ValueError( + f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." + ) + + self.time_embedding = TimestepEmbedding( + timestep_input_dim, + time_embed_dim, + act_fn=act_fn, + post_act_fn=timestep_post_act, + cond_proj_dim=time_cond_proj_dim, + ) + + if encoder_hid_dim_type is None and encoder_hid_dim is not None: + encoder_hid_dim_type = "text_proj" + self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) + logger.info( + "encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined." + ) + + if encoder_hid_dim is None and encoder_hid_dim_type is not None: + raise ValueError( + f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." + ) + + if encoder_hid_dim_type == "text_proj": + self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) + elif encoder_hid_dim_type == "text_image_proj": + # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)` + self.encoder_hid_proj = TextImageProjection( + text_embed_dim=encoder_hid_dim, + image_embed_dim=cross_attention_dim, + cross_attention_dim=cross_attention_dim, + ) + elif encoder_hid_dim_type == "image_proj": + # Kandinsky 2.2 + self.encoder_hid_proj = ImageProjection( + image_embed_dim=encoder_hid_dim, + cross_attention_dim=cross_attention_dim, + ) + elif encoder_hid_dim_type is not None: + raise ValueError( + f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." + ) + else: + self.encoder_hid_proj = None + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding( + timestep_input_dim, time_embed_dim, act_fn=act_fn + ) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + elif class_embed_type == "projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" + ) + # The projection `class_embed_type` is the same as the timestep `class_embed_type` except + # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings + # 2. it projects from an arbitrary input dimension. + # + # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. + # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. + # As a result, `TimestepEmbedding` can be passed arbitrary vectors. + self.class_embedding = TimestepEmbedding( + projection_class_embeddings_input_dim, time_embed_dim + ) + elif class_embed_type == "simple_projection": + if projection_class_embeddings_input_dim is None: + raise ValueError( + "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" + ) + self.class_embedding = nn.Linear( + projection_class_embeddings_input_dim, time_embed_dim + ) + else: + self.class_embedding = None + + if addition_embed_type == "text": + if encoder_hid_dim is not None: + text_time_embedding_from_dim = encoder_hid_dim + else: + text_time_embedding_from_dim = cross_attention_dim + + self.add_embedding = TextTimeEmbedding( + text_time_embedding_from_dim, + time_embed_dim, + num_heads=addition_embed_type_num_heads, + ) + elif addition_embed_type == "text_image": + # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much + # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use + # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)` + self.add_embedding = TextImageTimeEmbedding( + text_embed_dim=cross_attention_dim, + image_embed_dim=cross_attention_dim, + time_embed_dim=time_embed_dim, + ) + elif addition_embed_type == "text_time": + self.add_time_proj = Timesteps( + addition_time_embed_dim, flip_sin_to_cos, freq_shift + ) + self.add_embedding = TimestepEmbedding( + projection_class_embeddings_input_dim, time_embed_dim + ) + elif addition_embed_type == "image": + # Kandinsky 2.2 + self.add_embedding = ImageTimeEmbedding( + image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim + ) + elif addition_embed_type == "image_hint": + # Kandinsky 2.2 ControlNet + self.add_embedding = ImageHintTimeEmbedding( + image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim + ) + elif addition_embed_type is not None: + raise ValueError( + f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'." + ) + + if time_embedding_act_fn is None: + self.time_embed_act = None + else: + self.time_embed_act = get_activation(time_embedding_act_fn) + + self.down_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + if mid_block_only_cross_attention is None: + mid_block_only_cross_attention = only_cross_attention + + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if mid_block_only_cross_attention is None: + mid_block_only_cross_attention = False + + if isinstance(num_attention_heads, int): + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + if isinstance(cross_attention_dim, int): + cross_attention_dim = (cross_attention_dim,) * len(down_block_types) + + if isinstance(layers_per_block, int): + layers_per_block = [layers_per_block] * len(down_block_types) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len( + down_block_types + ) + + if class_embeddings_concat: + # The time embeddings are concatenated with the class embeddings. The dimension of the + # time embeddings passed to the down, middle, and up blocks is twice the dimension of the + # regular time embeddings + blocks_time_embed_dim = time_embed_dim * 2 + else: + blocks_time_embed_dim = time_embed_dim + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block[i], + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=blocks_time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim[i], + num_attention_heads=num_attention_heads[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] + if attention_head_dim[i] is not None + else output_channel, + dropout=dropout, + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock2DCrossAttn": + self.mid_block = UNetMidBlock2DCrossAttn( + transformer_layers_per_block=transformer_layers_per_block[-1], + in_channels=block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + dropout=dropout, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim[-1], + num_attention_heads=num_attention_heads[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn": + raise NotImplementedError(f"Unsupport mid_block_type: {mid_block_type}") + elif mid_block_type == "UNetMidBlock2D": + self.mid_block = UNetMidBlock2D( + in_channels=block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + dropout=dropout, + num_layers=0, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_groups=norm_num_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + add_attention=False, + ) + elif mid_block_type is None: + self.mid_block = None + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_num_attention_heads = list(reversed(num_attention_heads)) + reversed_layers_per_block = list(reversed(layers_per_block)) + reversed_cross_attention_dim = list(reversed(cross_attention_dim)) + reversed_transformer_layers_per_block = ( + list(reversed(transformer_layers_per_block)) + if reverse_transformer_layers_per_block is None + else reverse_transformer_layers_per_block + ) + only_cross_attention = list(reversed(only_cross_attention)) + + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[ + min(i + 1, len(block_out_channels) - 1) + ] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=reversed_layers_per_block[i] + 1, + transformer_layers_per_block=reversed_transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=blocks_time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resolution_idx=i, + resnet_groups=norm_num_groups, + cross_attention_dim=reversed_cross_attention_dim[i], + num_attention_heads=reversed_num_attention_heads[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + attention_type=attention_type, + resnet_skip_time_act=resnet_skip_time_act, + resnet_out_scale_factor=resnet_out_scale_factor, + cross_attention_norm=cross_attention_norm, + attention_head_dim=attention_head_dim[i] + if attention_head_dim[i] is not None + else output_channel, + dropout=dropout, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if norm_num_groups is not None: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], + num_groups=norm_num_groups, + eps=norm_eps, + ) + + self.conv_act = get_activation(act_fn) + + else: + self.conv_norm_out = None + self.conv_act = None + self.conv_norm_out = None + + conv_out_padding = (conv_out_kernel - 1) // 2 + # self.conv_out = nn.Conv2d( + # block_out_channels[0], + # out_channels, + # kernel_size=conv_out_kernel, + # padding=conv_out_padding, + # ) + + if attention_type in ["gated", "gated-text-image"]: + positive_len = 768 + if isinstance(cross_attention_dim, int): + positive_len = cross_attention_dim + elif isinstance(cross_attention_dim, tuple) or isinstance( + cross_attention_dim, list + ): + positive_len = cross_attention_dim[0] + + feature_type = "text-only" if attention_type == "gated" else "text-image" + self.position_net = PositionNet( + positive_len=positive_len, + out_dim=cross_attention_dim, + feature_type=feature_type, + ) + + @property + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor( + return_deprecated_lora=True + ) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor( + self, + processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], + _remove_lora=False, + ): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor, _remove_lora=_remove_lora) + else: + module.set_processor( + processor.pop(f"{name}.processor"), _remove_lora=_remove_lora + ) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + if all( + proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS + for proc in self.attn_processors.values() + ): + processor = AttnAddedKVProcessor() + elif all( + proc.__class__ in CROSS_ATTENTION_PROCESSORS + for proc in self.attn_processors.values() + ): + processor = AttnProcessor() + else: + raise ValueError( + f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" + ) + + self.set_attn_processor(processor, _remove_lora=True) + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module splits the input tensor in slices to compute attention in + several steps. This is useful for saving some memory in exchange for a small decrease in speed. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If + `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_sliceable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_sliceable_dims(module) + + num_sliceable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_sliceable_layers * [1] + + slice_size = ( + num_sliceable_layers * [slice_size] + if not isinstance(slice_size, list) + else slice_size + ) + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice( + module: torch.nn.Module, slice_size: List[int] + ): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + def enable_freeu(self, s1, s2, b1, b2): + r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497. + + The suffixes after the scaling factors represent the stage blocks where they are being applied. + + Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that + are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. + + Args: + s1 (`float`): + Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to + mitigate the "oversmoothing effect" in the enhanced denoising process. + s2 (`float`): + Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to + mitigate the "oversmoothing effect" in the enhanced denoising process. + b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. + b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. + """ + for i, upsample_block in enumerate(self.up_blocks): + setattr(upsample_block, "s1", s1) + setattr(upsample_block, "s2", s2) + setattr(upsample_block, "b1", b1) + setattr(upsample_block, "b2", b2) + + def disable_freeu(self): + """Disables the FreeU mechanism.""" + freeu_keys = {"s1", "s2", "b1", "b2"} + for i, upsample_block in enumerate(self.up_blocks): + for k in freeu_keys: + if ( + hasattr(upsample_block, k) + or getattr(upsample_block, k, None) is not None + ): + setattr(upsample_block, k, None) + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + timestep_cond: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[UNet2DConditionOutput, Tuple]: + r""" + The [`UNet2DConditionModel`] forward method. + + Args: + sample (`torch.FloatTensor`): + The noisy input tensor with the following shape `(batch, channel, height, width)`. + timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. + encoder_hidden_states (`torch.FloatTensor`): + The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. + class_labels (`torch.Tensor`, *optional*, defaults to `None`): + Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. + timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`): + Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed + through the `self.time_embedding` layer to obtain the timestep embeddings. + attention_mask (`torch.Tensor`, *optional*, defaults to `None`): + An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask + is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large + negative values to the attention scores corresponding to "discard" tokens. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + added_cond_kwargs: (`dict`, *optional*): + A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that + are passed along to the UNet blocks. + down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): + A tuple of tensors that if specified are added to the residuals of down unet blocks. + mid_block_additional_residual: (`torch.Tensor`, *optional*): + A tensor that if specified is added to the residual of the middle unet block. + encoder_attention_mask (`torch.Tensor`): + A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If + `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, + which adds large negative values to the attention scores corresponding to "discard" tokens. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the [`AttnProcessor`]. + added_cond_kwargs: (`dict`, *optional*): + A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that + are passed along to the UNet blocks. + down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*): + additional residuals to be added to UNet long skip connections from down blocks to up blocks for + example from ControlNet side model(s) + mid_block_additional_residual (`torch.Tensor`, *optional*): + additional residual to be added to UNet mid block output, for example from ControlNet side model + down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*): + additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s) + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise + a `tuple` is returned where the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + for dim in sample.shape[-2:]: + if dim % default_overall_up_factor != 0: + # Forward upsample size to force interpolation output size. + forward_upsample_size = True + break + + # ensure attention_mask is a bias, and give it a singleton query_tokens dimension + # expects mask of shape: + # [batch, key_tokens] + # adds singleton query_tokens dimension: + # [batch, 1, key_tokens] + # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: + # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) + # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) + if attention_mask is not None: + # assume that mask is expressed as: + # (1 = keep, 0 = discard) + # convert mask into a bias that can be added to attention scores: + # (keep = +0, discard = -10000.0) + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # convert encoder_attention_mask to a bias the same way we do for attention_mask + if encoder_attention_mask is not None: + encoder_attention_mask = ( + 1 - encoder_attention_mask.to(sample.dtype) + ) * -10000.0 + encoder_attention_mask = encoder_attention_mask.unsqueeze(1) + + # 0. center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + emb = self.time_embedding(t_emb, timestep_cond) + aug_emb = None + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError( + "class_labels should be provided when num_class_embeds > 0" + ) + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # there might be better ways to encapsulate this. + class_labels = class_labels.to(dtype=sample.dtype) + + class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) + + if self.config.class_embeddings_concat: + emb = torch.cat([emb, class_emb], dim=-1) + else: + emb = emb + class_emb + + if self.config.addition_embed_type == "text": + aug_emb = self.add_embedding(encoder_hidden_states) + elif self.config.addition_embed_type == "text_image": + # Kandinsky 2.1 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" + ) + + image_embs = added_cond_kwargs.get("image_embeds") + text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states) + aug_emb = self.add_embedding(text_embs, image_embs) + elif self.config.addition_embed_type == "text_time": + # SDXL - style + if "text_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" + ) + text_embeds = added_cond_kwargs.get("text_embeds") + if "time_ids" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" + ) + time_ids = added_cond_kwargs.get("time_ids") + time_embeds = self.add_time_proj(time_ids.flatten()) + time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) + add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(emb.dtype) + aug_emb = self.add_embedding(add_embeds) + elif self.config.addition_embed_type == "image": + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" + ) + image_embs = added_cond_kwargs.get("image_embeds") + aug_emb = self.add_embedding(image_embs) + elif self.config.addition_embed_type == "image_hint": + # Kandinsky 2.2 - style + if ( + "image_embeds" not in added_cond_kwargs + or "hint" not in added_cond_kwargs + ): + raise ValueError( + f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" + ) + image_embs = added_cond_kwargs.get("image_embeds") + hint = added_cond_kwargs.get("hint") + aug_emb, hint = self.add_embedding(image_embs, hint) + sample = torch.cat([sample, hint], dim=1) + + emb = emb + aug_emb if aug_emb is not None else emb + + if self.time_embed_act is not None: + emb = self.time_embed_act(emb) + + if ( + self.encoder_hid_proj is not None + and self.config.encoder_hid_dim_type == "text_proj" + ): + encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) + elif ( + self.encoder_hid_proj is not None + and self.config.encoder_hid_dim_type == "text_image_proj" + ): + # Kadinsky 2.1 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + + image_embeds = added_cond_kwargs.get("image_embeds") + encoder_hidden_states = self.encoder_hid_proj( + encoder_hidden_states, image_embeds + ) + elif ( + self.encoder_hid_proj is not None + and self.config.encoder_hid_dim_type == "image_proj" + ): + # Kandinsky 2.2 - style + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + image_embeds = added_cond_kwargs.get("image_embeds") + encoder_hidden_states = self.encoder_hid_proj(image_embeds) + elif ( + self.encoder_hid_proj is not None + and self.config.encoder_hid_dim_type == "ip_image_proj" + ): + if "image_embeds" not in added_cond_kwargs: + raise ValueError( + f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" + ) + image_embeds = added_cond_kwargs.get("image_embeds") + image_embeds = self.encoder_hid_proj(image_embeds).to( + encoder_hidden_states.dtype + ) + encoder_hidden_states = torch.cat( + [encoder_hidden_states, image_embeds], dim=1 + ) + + # 2. pre-process + sample = self.conv_in(sample) + + # 2.5 GLIGEN position net + if ( + cross_attention_kwargs is not None + and cross_attention_kwargs.get("gligen", None) is not None + ): + cross_attention_kwargs = cross_attention_kwargs.copy() + gligen_args = cross_attention_kwargs.pop("gligen") + cross_attention_kwargs["gligen"] = { + "objs": self.position_net(**gligen_args) + } + + # 3. down + lora_scale = ( + cross_attention_kwargs.get("scale", 1.0) + if cross_attention_kwargs is not None + else 1.0 + ) + if USE_PEFT_BACKEND: + # weight the lora layers by setting `lora_scale` for each PEFT layer + scale_lora_layers(self, lora_scale) + + is_controlnet = ( + mid_block_additional_residual is not None + and down_block_additional_residuals is not None + ) + # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets + is_adapter = down_intrablock_additional_residuals is not None + # maintain backward compatibility for legacy usage, where + # T2I-Adapter and ControlNet both use down_block_additional_residuals arg + # but can only use one or the other + if ( + not is_adapter + and mid_block_additional_residual is None + and down_block_additional_residuals is not None + ): + deprecate( + "T2I should not use down_block_additional_residuals", + "1.3.0", + "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \ + and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \ + for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ", + standard_warn=False, + ) + down_intrablock_additional_residuals = down_block_additional_residuals + is_adapter = True + + down_block_res_samples = (sample,) + tot_referece_features = () + for downsample_block in self.down_blocks: + if ( + hasattr(downsample_block, "has_cross_attention") + and downsample_block.has_cross_attention + ): + # For t2i-adapter CrossAttnDownBlock2D + additional_residuals = {} + if is_adapter and len(down_intrablock_additional_residuals) > 0: + additional_residuals[ + "additional_residuals" + ] = down_intrablock_additional_residuals.pop(0) + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + **additional_residuals, + ) + else: + sample, res_samples = downsample_block( + hidden_states=sample, temb=emb, scale=lora_scale + ) + if is_adapter and len(down_intrablock_additional_residuals) > 0: + sample += down_intrablock_additional_residuals.pop(0) + + down_block_res_samples += res_samples + + if is_controlnet: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip( + down_block_res_samples, down_block_additional_residuals + ): + down_block_res_sample = ( + down_block_res_sample + down_block_additional_residual + ) + new_down_block_res_samples = new_down_block_res_samples + ( + down_block_res_sample, + ) + + down_block_res_samples = new_down_block_res_samples + + # 4. mid + if self.mid_block is not None: + if ( + hasattr(self.mid_block, "has_cross_attention") + and self.mid_block.has_cross_attention + ): + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + cross_attention_kwargs=cross_attention_kwargs, + encoder_attention_mask=encoder_attention_mask, + ) + else: + sample = self.mid_block(sample, emb) + + # To support T2I-Adapter-XL + if ( + is_adapter + and len(down_intrablock_additional_residuals) > 0 + and sample.shape == down_intrablock_additional_residuals[0].shape + ): + sample += down_intrablock_additional_residuals.pop(0) + + if is_controlnet: + sample = sample + mid_block_additional_residual + + # 5. up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[ + : -len(upsample_block.resnets) + ] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if ( + hasattr(upsample_block, "has_cross_attention") + and upsample_block.has_cross_attention + ): + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + upsample_size=upsample_size, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + ) + else: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + upsample_size=upsample_size, + scale=lora_scale, + ) + + # 6. post-process + # if self.conv_norm_out: + # sample = self.conv_norm_out(sample) + # sample = self.conv_act(sample) + # sample = self.conv_out(sample) + + if USE_PEFT_BACKEND: + # remove `lora_scale` from each PEFT layer + unscale_lora_layers(self, lora_scale) + + if not return_dict: + return (sample,) + + return UNet2DConditionOutput(sample=sample) diff --git a/src/models/unet_3d.py b/src/models/unet_3d.py new file mode 100644 index 0000000000000000000000000000000000000000..7622bed5779aea24dd6d4fe52b42513a7850732c --- /dev/null +++ b/src/models/unet_3d.py @@ -0,0 +1,668 @@ +# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet_blocks.py + +from collections import OrderedDict +from dataclasses import dataclass +from os import PathLike +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.utils.checkpoint +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.attention_processor import AttentionProcessor +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from diffusers.models.modeling_utils import ModelMixin +from diffusers.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, logging +from safetensors.torch import load_file + +from .resnet import InflatedConv3d, InflatedGroupNorm +from .unet_3d_blocks import UNetMidBlock3DCrossAttn, get_down_block, get_up_block + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNet3DConditionOutput(BaseOutput): + sample: torch.FloatTensor + + +class UNet3DConditionModel(ModelMixin, ConfigMixin): + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ), + mid_block_type: str = "UNetMidBlock3DCrossAttn", + up_block_types: Tuple[str] = ( + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + ), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: int = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: int = 32, + norm_eps: float = 1e-5, + cross_attention_dim: int = 1280, + attention_head_dim: Union[int, Tuple[int]] = 8, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + use_inflated_groupnorm=False, + # Additional + use_motion_module=False, + motion_module_resolutions=(1, 2, 4, 8), + motion_module_mid_block=False, + motion_module_decoder_only=False, + motion_module_type=None, + motion_module_kwargs={}, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + ): + super().__init__() + + self.sample_size = sample_size + time_embed_dim = block_out_channels[0] * 4 + + # input + self.conv_in = InflatedConv3d( + in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1) + ) + + # time + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + else: + self.class_embedding = None + + self.down_blocks = nn.ModuleList([]) + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + res = 2**i + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block, + in_channels=input_channel, + out_channels=output_channel, + temb_channels=time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module + and (res in motion_module_resolutions) + and (not motion_module_decoder_only), + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock3DCrossAttn": + self.mid_block = UNetMidBlock3DCrossAttn( + in_channels=block_out_channels[-1], + temb_channels=time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module and motion_module_mid_block, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + # count how many layers upsample the videos + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_attention_head_dim = list(reversed(attention_head_dim)) + only_cross_attention = list(reversed(only_cross_attention)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + res = 2 ** (3 - i) + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[ + min(i + 1, len(block_out_channels) - 1) + ] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=layers_per_block + 1, + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=reversed_attention_head_dim[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module + and (res in motion_module_resolutions), + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if use_inflated_groupnorm: + self.conv_norm_out = InflatedGroupNorm( + num_channels=block_out_channels[0], + num_groups=norm_num_groups, + eps=norm_eps, + ) + else: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], + num_groups=norm_num_groups, + eps=norm_eps, + ) + self.conv_act = nn.SiLU() + self.conv_out = InflatedConv3d( + block_out_channels[0], out_channels, kernel_size=3, padding=1 + ) + + @property + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "set_processor"): + processors[f"{name}.processor"] = module.processor + + for sub_name, child in module.named_children(): + if "temporal_transformer" not in sub_name: + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + if "temporal_transformer" not in name: + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module will split the input tensor in slices, to compute attention + in several steps. This is useful to save some memory in exchange for a small speed decrease. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If + `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_slicable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_slicable_dims(module) + + num_slicable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_slicable_layers * [1] + + slice_size = ( + num_slicable_layers * [slice_size] + if not isinstance(slice_size, list) + else slice_size + ) + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice( + module: torch.nn.Module, slice_size: List[int] + ): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor + def set_attn_processor( + self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]] + ): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + if "temporal_transformer" not in sub_name: + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + if "temporal_transformer" not in name: + fn_recursive_attn_processor(name, module, processor) + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + pose_cond_fea: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[UNet3DConditionOutput, Tuple]: + r""" + Args: + sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor + timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps + encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When + returning a tuple, the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # prepare attention_mask + if attention_mask is not None: + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # time + timesteps = timestep + if not torch.is_tensor(timesteps): + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=self.dtype) + emb = self.time_embedding(t_emb) + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError( + "class_labels should be provided when num_class_embeds > 0" + ) + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) + emb = emb + class_emb + + # pre-process + sample = self.conv_in(sample) + if pose_cond_fea is not None: + sample = sample + pose_cond_fea + + # down + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + if ( + hasattr(downsample_block, "has_cross_attention") + and downsample_block.has_cross_attention + ): + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + ) + else: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + ) + + down_block_res_samples += res_samples + + if down_block_additional_residuals is not None: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip( + down_block_res_samples, down_block_additional_residuals + ): + down_block_res_sample = ( + down_block_res_sample + down_block_additional_residual + ) + new_down_block_res_samples += (down_block_res_sample,) + + down_block_res_samples = new_down_block_res_samples + + # mid + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + ) + + if mid_block_additional_residual is not None: + sample = sample + mid_block_additional_residual + + # up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[ + : -len(upsample_block.resnets) + ] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if ( + hasattr(upsample_block, "has_cross_attention") + and upsample_block.has_cross_attention + ): + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + upsample_size=upsample_size, + attention_mask=attention_mask, + ) + else: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + upsample_size=upsample_size, + encoder_hidden_states=encoder_hidden_states, + ) + + # post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if not return_dict: + return (sample,) + + return UNet3DConditionOutput(sample=sample) + + @classmethod + def from_pretrained_2d( + cls, + pretrained_model_path: PathLike, + motion_module_path: PathLike, + subfolder=None, + unet_additional_kwargs=None, + mm_zero_proj_out=False, + ): + pretrained_model_path = Path(pretrained_model_path) + motion_module_path = Path(motion_module_path) + if subfolder is not None: + pretrained_model_path = pretrained_model_path.joinpath(subfolder) + logger.info( + f"loaded temporal unet's pretrained weights from {pretrained_model_path} ..." + ) + + config_file = pretrained_model_path / "config.json" + if not (config_file.exists() and config_file.is_file()): + raise RuntimeError(f"{config_file} does not exist or is not a file") + + unet_config = cls.load_config(config_file) + unet_config["_class_name"] = cls.__name__ + unet_config["down_block_types"] = [ + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ] + unet_config["up_block_types"] = [ + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + ] + unet_config["mid_block_type"] = "UNetMidBlock3DCrossAttn" + + model = cls.from_config(unet_config, **unet_additional_kwargs) + # load the vanilla weights + if pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME).exists(): + logger.debug( + f"loading safeTensors weights from {pretrained_model_path} ..." + ) + state_dict = load_file( + pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME), device="cpu" + ) + + elif pretrained_model_path.joinpath(WEIGHTS_NAME).exists(): + logger.debug(f"loading weights from {pretrained_model_path} ...") + state_dict = torch.load( + pretrained_model_path.joinpath(WEIGHTS_NAME), + map_location="cpu", + weights_only=True, + ) + else: + raise FileNotFoundError(f"no weights file found in {pretrained_model_path}") + + # load the motion module weights + if motion_module_path.exists() and motion_module_path.is_file(): + if motion_module_path.suffix.lower() in [".pth", ".pt", ".ckpt"]: + logger.info(f"Load motion module params from {motion_module_path}") + motion_state_dict = torch.load( + motion_module_path, map_location="cpu", weights_only=True + ) + elif motion_module_path.suffix.lower() == ".safetensors": + motion_state_dict = load_file(motion_module_path, device="cpu") + else: + raise RuntimeError( + f"unknown file format for motion module weights: {motion_module_path.suffix}" + ) + if mm_zero_proj_out: + logger.info(f"Zero initialize proj_out layers in motion module...") + new_motion_state_dict = OrderedDict() + for k in motion_state_dict: + if "proj_out" in k: + continue + new_motion_state_dict[k] = motion_state_dict[k] + motion_state_dict = new_motion_state_dict + + # merge the state dicts + state_dict.update(motion_state_dict) + + # load the weights into the model + m, u = model.load_state_dict(state_dict, strict=False) + logger.debug(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};") + + params = [ + p.numel() if "temporal" in n else 0 for n, p in model.named_parameters() + ] + logger.info(f"Loaded {sum(params) / 1e6}M-parameter motion module") + + return model diff --git a/src/models/unet_3d_blocks.py b/src/models/unet_3d_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..732e018976896b3b955f8b9bae0f6820f8b64921 --- /dev/null +++ b/src/models/unet_3d_blocks.py @@ -0,0 +1,873 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py + +import pdb + +import torch +from torch import nn + +from .motion_module import get_motion_module + +# from .motion_module import get_motion_module +from .resnet import Downsample3D, ResnetBlock3D, Upsample3D +from .transformer_3d import Transformer3DModel + + +def get_down_block( + down_block_type, + num_layers, + in_channels, + out_channels, + temb_channels, + add_downsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + downsample_padding=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, +): + down_block_type = ( + down_block_type[7:] + if down_block_type.startswith("UNetRes") + else down_block_type + ) + if down_block_type == "DownBlock3D": + return DownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + elif down_block_type == "CrossAttnDownBlock3D": + if cross_attention_dim is None: + raise ValueError( + "cross_attention_dim must be specified for CrossAttnDownBlock3D" + ) + return CrossAttnDownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type, + num_layers, + in_channels, + out_channels, + prev_output_channel, + temb_channels, + add_upsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, +): + up_block_type = ( + up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + ) + if up_block_type == "UpBlock3D": + return UpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + elif up_block_type == "CrossAttnUpBlock3D": + if cross_attention_dim is None: + raise ValueError( + "cross_attention_dim must be specified for CrossAttnUpBlock3D" + ) + return CrossAttnUpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + raise ValueError(f"{up_block_type} does not exist.") + + +class UNetMidBlock3DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + output_scale_factor=1.0, + cross_attention_dim=1280, + dual_cross_attention=False, + use_linear_projection=False, + upcast_attention=False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + resnet_groups = ( + resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + ) + + # there is always at least one resnet + resnets = [ + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ] + attentions = [] + motion_modules = [] + + for _ in range(num_layers): + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + in_channels // attn_num_head_channels, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=in_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + def forward( + self, + hidden_states, + temb=None, + encoder_hidden_states=None, + audio_cond_fea = None, + attention_mask=None, + ): + hidden_states = self.resnets[0](hidden_states, temb) + for attn, resnet, motion_module in zip( + self.attentions, self.resnets[1:], self.motion_modules + ): + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + audio_cond_fea = audio_cond_fea + ).sample + hidden_states = ( + motion_module( + hidden_states, temb, encoder_hidden_states=encoder_hidden_states + ) + if motion_module is not None + else hidden_states + ) + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class CrossAttnDownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + downsample_padding=1, + add_downsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + temb=None, + encoder_hidden_states=None, + audio_cond_fea=None, + attention_mask=None, + ): + output_states = () + + for i, (resnet, attn, motion_module) in enumerate( + zip(self.resnets, self.attentions, self.motion_modules) + ): + # self.gradient_checkpointing = False + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + audio_cond_fea, + )[0] + + # add motion module + hidden_states = ( + motion_module( + hidden_states, temb, encoder_hidden_states=encoder_hidden_states + ) + if motion_module is not None + else hidden_states + ) + + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + audio_cond_fea = audio_cond_fea, + ).sample + + # add motion module + hidden_states = ( + motion_module( + hidden_states, temb, encoder_hidden_states=encoder_hidden_states + ) + if motion_module is not None + else hidden_states + ) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class DownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_downsample=True, + downsample_padding=1, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + motion_modules = [] + + # use_motion_module = False + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None): + output_states = () + + for resnet, motion_module in zip(self.resnets, self.motion_modules): + # print(f"DownBlock3D {self.gradient_checkpointing = }") + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states.requires_grad_(), + temb, + encoder_hidden_states, + ) + else: + hidden_states = resnet(hidden_states, temb) + + # add motion module + hidden_states = ( + motion_module( + hidden_states, temb, encoder_hidden_states=encoder_hidden_states + ) + if motion_module is not None + else hidden_states + ) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnUpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + add_upsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + use_motion_module=None, + use_inflated_groupnorm=None, + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_upsample: + self.upsamplers = nn.ModuleList( + [Upsample3D(out_channels, use_conv=True, out_channels=out_channels)] + ) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + encoder_hidden_states=None, + audio_cond_fea=None, + upsample_size=None, + attention_mask=None, + ): + for i, (resnet, attn, motion_module) in enumerate( + zip(self.resnets, self.attentions, self.motion_modules) + ): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + audio_cond_fea = audio_cond_fea + ).sample + if motion_module is not None: + # print("hidden_states shape1:", hidden_states.shape) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states.requires_grad_(), + temb, + encoder_hidden_states, + ) + + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + audio_cond_fea = audio_cond_fea + ).sample + # print("hidden_states shape1:", hidden_states.shape) + # add motion module + hidden_states = ( + motion_module( + hidden_states, temb, encoder_hidden_states=encoder_hidden_states + ) + if motion_module is not None + else hidden_states + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class UpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_upsample=True, + use_inflated_groupnorm=None, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + motion_modules = [] + + # use_motion_module = False + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_upsample: + self.upsamplers = nn.ModuleList( + [Upsample3D(out_channels, use_conv=True, out_channels=out_channels)] + ) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + upsample_size=None, + encoder_hidden_states=None, + ): + for resnet, motion_module in zip(self.resnets, self.motion_modules): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + # print(f"UpBlock3D {self.gradient_checkpointing = }") + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states.requires_grad_(), + temb, + encoder_hidden_states, + ) + else: + # print("hidden_states shape1:", hidden_states.shape) + hidden_states = resnet(hidden_states, temb) + # print("hidden_states shape2:", hidden_states.shape) + hidden_states = ( + motion_module( + hidden_states, temb, encoder_hidden_states=encoder_hidden_states + ) + if motion_module is not None + else hidden_states + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states diff --git a/src/models/unet_3d_echo.py b/src/models/unet_3d_echo.py new file mode 100644 index 0000000000000000000000000000000000000000..25f3eb0d3933f1e69c33f6a41ec883141b766760 --- /dev/null +++ b/src/models/unet_3d_echo.py @@ -0,0 +1,689 @@ +#!/usr/bin/env python +# -*- coding: UTF-8 -*- +''' +@Project :EchoMimic +@File :unet_3d_echo.py +@Author :juzhen.czy +@Date :2024/3/4 17:50 +''' +# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet_blocks.py + +from collections import OrderedDict +from dataclasses import dataclass +from os import PathLike +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.utils.checkpoint +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.attention_processor import AttentionProcessor +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from diffusers.models.modeling_utils import ModelMixin +from diffusers.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, logging +from safetensors.torch import load_file + +from .resnet import InflatedConv3d, InflatedGroupNorm +from .unet_3d_blocks import UNetMidBlock3DCrossAttn, get_down_block, get_up_block + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNet3DConditionOutput(BaseOutput): + sample: torch.FloatTensor + + +class EchoUNet3DConditionModel(ModelMixin, ConfigMixin): + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ), + mid_block_type: str = "UNetMidBlock3DCrossAttn", + up_block_types: Tuple[str] = ( + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + ), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: int = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: int = 32, + norm_eps: float = 1e-5, + cross_attention_dim: int = 1280, + attention_head_dim: Union[int, Tuple[int]] = 8, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + use_inflated_groupnorm=False, + # Additional + use_motion_module=False, + motion_module_resolutions=(1, 2, 4, 8), + motion_module_mid_block=False, + motion_module_decoder_only=False, + motion_module_type=None, + motion_module_kwargs={}, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + ): + super().__init__() + + self.sample_size = sample_size + time_embed_dim = block_out_channels[0] * 4 + + # input + self.conv_in = InflatedConv3d( + in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1) + ) + + # time + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + else: + self.class_embedding = None + + self.down_blocks = nn.ModuleList([]) + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + res = 2**i + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block, + in_channels=input_channel, + out_channels=output_channel, + temb_channels=time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module + and (res in motion_module_resolutions) + and (not motion_module_decoder_only), + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock3DCrossAttn": + self.mid_block = UNetMidBlock3DCrossAttn( + in_channels=block_out_channels[-1], + temb_channels=time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module and motion_module_mid_block, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + self.unet_use_temporal_attention = unet_use_temporal_attention + # count how many layers upsample the videos + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_attention_head_dim = list(reversed(attention_head_dim)) + only_cross_attention = list(reversed(only_cross_attention)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + res = 2 ** (3 - i) + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[ + min(i + 1, len(block_out_channels) - 1) + ] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=layers_per_block + 1, + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=reversed_attention_head_dim[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module + and (res in motion_module_resolutions), + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if use_inflated_groupnorm: + self.conv_norm_out = InflatedGroupNorm( + num_channels=block_out_channels[0], + num_groups=norm_num_groups, + eps=norm_eps, + ) + else: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], + num_groups=norm_num_groups, + eps=norm_eps, + ) + self.conv_act = nn.SiLU() + self.conv_out = InflatedConv3d( + block_out_channels[0], out_channels, kernel_size=3, padding=1 + ) + + @property + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "set_processor"): + processors[f"{name}.processor"] = module.processor + + for sub_name, child in module.named_children(): + if "temporal_transformer" not in sub_name: + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + if "temporal_transformer" not in name: + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module will split the input tensor in slices, to compute attention + in several steps. This is useful to save some memory in exchange for a small speed decrease. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If + `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_slicable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_slicable_dims(module) + + num_slicable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_slicable_layers * [1] + + slice_size = ( + num_slicable_layers * [slice_size] + if not isinstance(slice_size, list) + else slice_size + ) + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice( + module: torch.nn.Module, slice_size: List[int] + ): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor + def set_attn_processor( + self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]] + ): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + if "temporal_transformer" not in sub_name: + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + if "temporal_transformer" not in name: + fn_recursive_attn_processor(name, module, processor) + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + audio_cond_fea: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + face_musk_fea: Optional[torch.Tensor] = None, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[UNet3DConditionOutput, Tuple]: + r""" + Args: + sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor + timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps + encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When + returning a tuple, the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # prepare attention_mask + if attention_mask is not None: + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # time + timesteps = timestep + if not torch.is_tensor(timesteps): + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=self.dtype) + emb = self.time_embedding(t_emb) + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError( + "class_labels should be provided when num_class_embeds > 0" + ) + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) + emb = emb + class_emb + + # pre-process + sample = self.conv_in(sample) + if face_musk_fea is not None: + # print(sample.shape, face_musk_fea.shape) + sample = sample + face_musk_fea + + # down + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + if ( + hasattr(downsample_block, "has_cross_attention") + and downsample_block.has_cross_attention + ): + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + audio_cond_fea=audio_cond_fea, + attention_mask=attention_mask, + ) + else: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + ) + + down_block_res_samples += res_samples + + if down_block_additional_residuals is not None: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip( + down_block_res_samples, down_block_additional_residuals + ): + down_block_res_sample = ( + down_block_res_sample + down_block_additional_residual + ) + new_down_block_res_samples += (down_block_res_sample,) + + down_block_res_samples = new_down_block_res_samples + + # mid + sample = self.mid_block( + sample, + emb, + encoder_hidden_states=encoder_hidden_states, + audio_cond_fea=audio_cond_fea, + attention_mask=attention_mask, + ) + + if mid_block_additional_residual is not None: + sample = sample + mid_block_additional_residual + + # up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[ + : -len(upsample_block.resnets) + ] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if ( + hasattr(upsample_block, "has_cross_attention") + and upsample_block.has_cross_attention + ): + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + audio_cond_fea=audio_cond_fea, + upsample_size=upsample_size, + attention_mask=attention_mask, + ) + else: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + upsample_size=upsample_size, + encoder_hidden_states=encoder_hidden_states, + ) + + # post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if not return_dict: + return (sample,) + + return UNet3DConditionOutput(sample=sample) + + @classmethod + def from_pretrained_2d( + cls, + pretrained_model_path: PathLike, + motion_module_path: PathLike, + subfolder=None, + unet_additional_kwargs=None, + mm_zero_proj_out=False, + ): + pretrained_model_path = Path(pretrained_model_path) + motion_module_path = Path(motion_module_path) + if subfolder is not None: + pretrained_model_path = pretrained_model_path.joinpath(subfolder) + logger.info( + f"loaded temporal unet's pretrained weights from {pretrained_model_path} ..." + ) + + config_file = pretrained_model_path / "config.json" + if not (config_file.exists() and config_file.is_file()): + raise RuntimeError(f"{config_file} does not exist or is not a file") + + unet_config = cls.load_config(config_file) + unet_config["_class_name"] = cls.__name__ + unet_config["down_block_types"] = [ + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ] + unet_config["up_block_types"] = [ + "UpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + "CrossAttnUpBlock3D", + ] + unet_config["mid_block_type"] = "UNetMidBlock3DCrossAttn" + + model = cls.from_config(unet_config, **unet_additional_kwargs) + # load the vanilla weights + if pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME).exists(): + logger.debug( + f"loading safeTensors weights from {pretrained_model_path} ..." + ) + state_dict = load_file( + pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME), device="cpu" + ) + + elif pretrained_model_path.joinpath(WEIGHTS_NAME).exists(): + logger.debug(f"loading weights from {pretrained_model_path} ...") + state_dict = torch.load( + pretrained_model_path.joinpath(WEIGHTS_NAME), + map_location="cpu", + weights_only=True, + ) + else: + raise FileNotFoundError(f"no weights file found in {pretrained_model_path}") + + # load the motion module weights + if motion_module_path.exists() and motion_module_path.is_file(): + if motion_module_path.suffix.lower() in [".pth", ".pt", ".ckpt"]: + logger.info(f"Load motion module params from {motion_module_path}") + motion_state_dict = torch.load( + motion_module_path, map_location="cpu", weights_only=True + ) + elif motion_module_path.suffix.lower() == ".safetensors": + motion_state_dict = load_file(motion_module_path, device="cpu") + else: + raise RuntimeError( + f"unknown file format for motion module weights: {motion_module_path.suffix}" + ) + if mm_zero_proj_out: + logger.info(f"Zero initialize proj_out layers in motion module...") + new_motion_state_dict = OrderedDict() + for k in motion_state_dict: + if "proj_out" in k: + continue + new_motion_state_dict[k] = motion_state_dict[k] + motion_state_dict = new_motion_state_dict + + # merge the state dicts + state_dict.update(motion_state_dict) + + init_state_dict = model.state_dict() + + for key in init_state_dict: + if state_dict.get(key) is not None: + if state_dict.get(key).shape == init_state_dict[key].shape: + init_state_dict[key] = state_dict.get(key) + # else: + # print(key, " tensor shape is not matched with origin SD.") + # load the weights into the model + m, u = model.load_state_dict(init_state_dict, strict=False) + logger.debug(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};") + + params = [ + p.numel() if "temporal" in n else 0 for n, p in model.named_parameters() + ] + logger.info(f"Loaded {sum(params) / 1e6}M-parameter motion module") + + return model diff --git a/src/models/whisper/audio2feature.py b/src/models/whisper/audio2feature.py new file mode 100644 index 0000000000000000000000000000000000000000..112824982574d18f68467a33f5c01ba8526953c4 --- /dev/null +++ b/src/models/whisper/audio2feature.py @@ -0,0 +1,133 @@ +import os +from .whisper import load_model +import numpy as np +import time +import sys +sys.path.append("..") + +class Audio2Feature(): + def __init__(self, + whisper_model_type="tiny", + model_path="./models/whisper/tiny.pt", + device="cuda"): + self.whisper_model_type = whisper_model_type + self.model = load_model(model_path, device=device) # + + def get_sliced_feature(self, + feature_array, + vid_idx, + audio_feat_length=[2,2], + fps=25): + """ + Get sliced features based on a given index + :param feature_array: + :param start_idx: the start index of the feature + :param audio_feat_length: + :return: + """ + length = len(feature_array) + selected_feature = [] + selected_idx = [] + + center_idx = int(vid_idx*50/fps) + left_idx = center_idx-audio_feat_length[0]*2 + right_idx = center_idx + (audio_feat_length[1]+1)*2 + + for idx in range(left_idx,right_idx): + idx = max(0, idx) + idx = min(length-1, idx) + x = feature_array[idx] + selected_feature.append(x) + selected_idx.append(idx) + + selected_feature = np.concatenate(selected_feature, axis=0) + # print("before reshape:", selected_feature.shape) + selected_feature = selected_feature.reshape(-1, 384)# 50*384 + return selected_feature,selected_idx + + def get_sliced_feature_sparse(self,feature_array, vid_idx, audio_feat_length= [2,2],fps = 25): + """ + Get sliced features based on a given index + :param feature_array: + :param start_idx: the start index of the feature + :param audio_feat_length: + :return: + """ + length = len(feature_array) + selected_feature = [] + selected_idx = [] + + for dt in range(-audio_feat_length[0],audio_feat_length[1]+1): + left_idx = int((vid_idx+dt)*50/fps) + if left_idx<1 or left_idx>length-1: + left_idx = max(0, left_idx) + left_idx = min(length-1, left_idx) + + x = feature_array[left_idx] + x = x[np.newaxis,:,:] + x = np.repeat(x, 2, axis=0) + selected_feature.append(x) + selected_idx.append(left_idx) + selected_idx.append(left_idx) + else: + x = feature_array[left_idx-1:left_idx+1] + selected_feature.append(x) + selected_idx.append(left_idx-1) + selected_idx.append(left_idx) + selected_feature = np.concatenate(selected_feature, axis=0) + selected_feature = selected_feature.reshape(-1, 384)# 50*384 + return selected_feature,selected_idx + + + def feature2chunks(self,feature_array,fps,audio_feat_length = [2,2]): + whisper_chunks = [] + whisper_idx_multiplier = 50./fps + i = 0 + print(f"video in {fps} FPS, audio idx in 50FPS") + while 1: + start_idx = int(i * whisper_idx_multiplier) + selected_feature,selected_idx = self.get_sliced_feature(feature_array= feature_array,vid_idx = i,audio_feat_length=audio_feat_length,fps=fps) + #print(f"i:{i},selected_idx {selected_idx}") + whisper_chunks.append(selected_feature) + i += 1 + if start_idx>len(feature_array): + break + + return np.array(whisper_chunks) + + def audio2feat(self,audio_path): + # get the sample rate of the audio + result = self.model.transcribe(audio_path) + embed_list = [] + for emb in result['segments']: + encoder_embeddings = emb['encoder_embeddings'] + encoder_embeddings = encoder_embeddings.transpose(0,2,1,3) + encoder_embeddings = encoder_embeddings.squeeze(0) + start_idx = int(emb['start']) + end_idx = int(emb['end']) + emb_end_idx = int((end_idx - start_idx)/2) + embed_list.append(encoder_embeddings[:emb_end_idx]) + concatenated_array = np.concatenate(embed_list, axis=0) + return concatenated_array + +def load_audio_model(model_path, device): + audio_processor = Audio2Feature(model_path=model_path, device=device) + return audio_processor + +if __name__ == "__main__": + audio_processor = Audio2Feature(model_path="../../models/whisper/whisper_tiny.pt") + audio_path = "./test.mp3" + array = audio_processor.audio2feat(audio_path) + print(array.shape) + fps = 25 + whisper_idx_multiplier = 50./fps + + i = 0 + print(f"video in {fps} FPS, audio idx in 50FPS") + while 1: + start_idx = int(i * whisper_idx_multiplier) + selected_feature,selected_idx = audio_processor.get_sliced_feature(feature_array= array,vid_idx = i,audio_feat_length=[2,2],fps=fps) + print(f"video idx {i},\t audio idx {selected_idx},\t shape {selected_feature.shape}") + i += 1 + if start_idx>len(array): + break diff --git a/src/models/whisper/whisper/__init__.py b/src/models/whisper/whisper/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b9255534ceae3014481780ab0571b4edb6523212 --- /dev/null +++ b/src/models/whisper/whisper/__init__.py @@ -0,0 +1,116 @@ +import hashlib +import io +import os +import urllib +import warnings +from typing import List, Optional, Union + +import torch +from tqdm import tqdm + +from .audio import load_audio, log_mel_spectrogram, pad_or_trim +from .decoding import DecodingOptions, DecodingResult, decode, detect_language +from .model import Whisper, ModelDimensions +from .transcribe import transcribe + + +_MODELS = { + "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", + "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", + "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt", + "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt", + "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", + "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt", + "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt", + "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", + "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt", + "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt", + "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", + "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", +} + + +def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: + os.makedirs(root, exist_ok=True) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, os.path.basename(url)) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + model_bytes = open(download_target, "rb").read() + if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: + return model_bytes if in_memory else download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + model_bytes = open(download_target, "rb").read() + if hashlib.sha256(model_bytes).hexdigest() != expected_sha256: + raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.") + + return model_bytes if in_memory else download_target + + +def available_models() -> List[str]: + """Returns the names of available models""" + return list(_MODELS.keys()) + + +def load_model(name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False) -> Whisper: + """ + Load a Whisper ASR model + + Parameters + ---------- + name : str + one of the official model names listed by `whisper.available_models()`, or + path to a model checkpoint containing the model dimensions and the model state_dict. + device : Union[str, torch.device] + the PyTorch device to put the model into + download_root: str + path to download the model files; by default, it uses "~/.cache/whisper" + in_memory: bool + whether to preload the model weights into host memory + + Returns + ------- + model : Whisper + The Whisper ASR model instance + """ + + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if download_root is None: + download_root = os.getenv( + "XDG_CACHE_HOME", + os.path.join(os.path.expanduser("~"), ".cache", "whisper") + ) + + if name in _MODELS: + checkpoint_file = _download(_MODELS[name], download_root, in_memory) + elif os.path.isfile(name): + checkpoint_file = open(name, "rb").read() if in_memory else name + else: + raise RuntimeError(f"Model {name} not found; available models = {available_models()}") + + with (io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")) as fp: + checkpoint = torch.load(fp, map_location=device) + del checkpoint_file + + dims = ModelDimensions(**checkpoint["dims"]) + model = Whisper(dims) + model.load_state_dict(checkpoint["model_state_dict"]) + + return model.to(device) diff --git a/src/models/whisper/whisper/__main__.py b/src/models/whisper/whisper/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..bc9b04a39283f71082f46ec8bb11fe15f8cf75b2 --- /dev/null +++ b/src/models/whisper/whisper/__main__.py @@ -0,0 +1,4 @@ +from .transcribe import cli + + +cli() diff --git a/src/models/whisper/whisper/assets/gpt2/merges.txt b/src/models/whisper/whisper/assets/gpt2/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..6636bda4a1fd7a63653dffb22683b8162c8de956 --- /dev/null +++ b/src/models/whisper/whisper/assets/gpt2/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 - Trained by `huggingface/tokenizers` +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t +Ġ re +v e +Ġ e +r o +l y +Ġb e +Ġ g +Ġ T +c t +Ġ S +i d +o t +Ġ I +u t +e t +Ġ A +Ġ is +Ġ on +i m +a m +o w +a y +a d +s e +Ġth at +Ġ C +i g +Ġf or +a c +Ġ y +v er +u r +Ġ u +l d +Ġs t +Ġ M +' s +Ġ he +Ġ it +at ion +it h +i r +c e +Ġy ou +i l +Ġ B +Ġw h +o l +Ġ P +Ġw ith +Ġ 1 +t er +c h +Ġa s +Ġw e +Ġ ( +n d +i ll +Ġ D +i f +Ġ 2 +a g +er s +k e +Ġ " +Ġ H +e m +Ġc on +Ġ W +Ġ R +he r +Ġw as +Ġ r +o d +Ġ F +u l +at e +Ġa t +r i +p p +o re +ĠT he +Ġs e +u s +Ġp ro +Ġh a +u m +Ġa re +Ġd e +a in +an d +Ġo r +ig h +es t +is t +a b +r om +Ġ N +t h +Ġc om +Ġ G +u n +o p +0 0 +Ġ L +Ġn ot +es s +Ġe x +Ġ v +re s +Ġ E +e w +it y +an t +Ġb y +e l +o s +or t +o c +q u +Ġf rom +Ġha ve +Ġs u +i ve +ou ld +Ġs h +Ġth is +n t +r a +p e +igh t +ar t +m ent +Ġa l +u st +en d +- - +al l +Ġ O +ac k +Ġc h +Ġ le +i es +re d +ar d +â Ģ +ou t +Ġ J +Ġa b +e ar +i v +al ly +ou r +o st +g h +p t +Ġp l +as t +Ġc an +a k +om e +u d +T he +Ġh is +Ġd o +Ġg o +Ġh as +g e +' t +Ġ U +r ou +Ġs a +Ġ j +Ġb ut +Ġw or +Ġa ll +e ct +Ġ k +am e +Ġw ill +o k +Ġw he +Ġthe y +id e +0 1 +f f +ic h +p l +t her +Ġt r +. . +Ġin t +i e +u re +ag e +Ġn e +i al +a p +in e +ic e +Ġm e +Ġo ut +an s +on e +on g +ion s +Ġwh o +Ġ K +Ġu p +Ġthe ir +Ġa d +Ġ 3 +Ġu s +at ed +ou s +Ġm ore +u e +o g +ĠS t +in d +i ke +Ġs o +im e +p er +. 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"auer": 18120, "ĠSaudi": 18121, "yeong": 18122, "Ġcurry": 18123, "ĠForest": 18124, "Ñĭл": 18125, "Ġfifteen": 18126, "Ġbolts": 18127, "Ġ{\\": 18128, "¬´": 18129, "Ġsettlement": 18130, "Ġlange": 18131, "Ġbam": 18132, "Get": 18133, "íķĻ": 18134, "Ġswap": 18135, "ĠKhan": 18136, "Ġcommence": 18137, "Ġquarantine": 18138, "Ġscored": 18139, "çĸ": 18140, "Ġ1950": 18141, "Ġthicker": 18142, "Ġsûr": 18143, "åı£": 18144, "ĠLarry": 18145, "Ġallez": 18146, "ìĭľëĬĶ": 18147, "Ġgü": 18148, "Ġspectacular": 18149, "//": 18150, "both": 18151, "Ġstats": 18152, "妳": 18153, "ĠNancy": 18154, "Ġbunu": 18155, "Ġcrust": 18156, "Ġactivated": 18157, "Ġê·¸ëŀ": 18158, "outhe": 18159, "Ġports": 18160, "Ġneural": 18161, "Ġjaw": 18162, "Ġobservations": 18163, "Ġvoit": 18164, "aban": 18165, "ải": 18166, "¦¬ë¥¼": 18167, "omes": 18168, "à¯ĭ": 18169, "qui": 18170, "Ġkindness": 18171, "Ðij": 18172, "Ġ41": 18173, "Ġmoderate": 18174, "Ġangels": 18175, "ĠTamb": 18176, "èt": 18177, "Ġchlor": 18178, "ĠBilly": 18179, "ì²ĺë": 18180, "acon": 18181, "Ġselecting": 18182, "ĠDelta": 18183, "Ġnull": 18184, "denly": 18185, "Ġciud": 18186, "Ġtendency": 18187, "Ġbreakdown": 18188, "Ġmint": 18189, "ÑĦоÑĢм": 18190, "orph": 18191, "Ġdawn": 18192, "spr": 18193, "ĠWILL": 18194, "ächlich": 18195, "Ġpuppy": 18196, "700": 18197, "Ġத": 18198, "Ġfails": 18199, "ĠConc": 18200, "Ġrelatives": 18201, "Ġinviting": 18202, "Ġautonom": 18203, "Ġcomposed": 18204, "Ġunity": 18205, "Ġdecis": 18206, "Ġaccessories": 18207, "ĠCass": 18208, "Ġbist": 18209, "ĠTip": 18210, "째": 18211, "Ġpunt": 18212, "Ġráp": 18213, "éĢ²": 18214, "ANK": 18215, "ãģļ": 18216, "exist": 18217, "Ġcompatible": 18218, "Ġner": 18219, "ĠемÑĥ": 18220, "Ġaplic": 18221, "Ġbapt": 18222, "Ġfailing": 18223, "ĠTamam": 18224, "Ġoscill": 18225, "Ġletzten": 18226, "Ġrepeatedly": 18227, "Ġjungle": 18228, "ĠPush": 18229, "hai": 18230, "Ġη": 18231, "Ġdeadly": 18232, "Ñıж": 18233, "wiÄħ": 18234, "ĠCommon": 18235, "ĠÎķ": 18236, "Ġskate": 18237, "TC": 18238, "ĠMini": 18239, "Ġhobby": 18240, "ần": 18241, "Ġroutes": 18242, "Ġamigos": 18243, "Ġconjun": 18244, "Ġpartnerships": 18245, "Ġnovo": 18246, "Ġaver": 18247, "Ġpouvez": 18248, "bridge": 18249, "Ġpreoc": 18250, "him": 18251, "Ġturb": 18252, "Ġsob": 18253, "ĠSnap": 18254, "Ġì°¸": 18255, "minute": 18256, "Ġtraject": 18257, "ujÄĻ": 18258, "Ġeager": 18259, "Ġregulatory": 18260, "Ġbanking": 18261, "bling": 18262, "ÑĪÑĮ": 18263, "aż": 18264, "Ġbizarre": 18265, "itated": 18266, "dire": 18267, "Ġthreatened": 18268, "Ġshining": 18269, "Ġnesse": 18270, "Ġcorps": 18271, "ĠÑģÑĥ": 18272, "Ġteles": 18273, "Ġtemp": 18274, "tem": 18275, "Ġкан": 18276, "Ġfever": 18277, "New": 18278, "Ġheavier": 18279, "ĠSah": 18280, "bud": 18281, "Ġoutros": 18282, "Ġì°¾": 18283, "Ġëªħ": 18284, "arring": 18285, "Ġê´ľì°®": 18286, "ĠNap": 18287, "Ġsemin": 18288, "ĠThan": 18289, "ifs": 18290, "Ġdesen": 18291, "ĠÑĤакое": 18292, "Ġloses": 18293, "ĠBalt": 18294, "kon": 18295, "ĠнапÑĢ": 18296, "Ġvois": 18297, "ĠMoscow": 18298, "Ġchairs": 18299, "his": 18300, "Ġrefugees": 18301, "kg": 18302, "Ġkole": 18303, "į¨": 18304, "аÑģибо": 18305, "¦½": 18306, "ĠUniverse": 18307, "ĠDirect": 18308, "Ġcheating": 18309, "ĠCin": 18310, "Ġpatri": 18311, "Ġadvise": 18312, "ĠNether": 18313, "Ġprimeiro": 18314, "Ġmentioning": 18315, "nut": 18316, "56": 18317, "arı": 18318, "Ġpetite": 18319, "bled": 18320, "Ġpensar": 18321, "icio": 18322, "IND": 18323, "Ġveteran": 18324, "Ġladder": 18325, "Ġconsequence": 18326, "ожал": 18327, "ĠBurn": 18328, "Ġrug": 18329, "ĠMade": 18330, "Ġgit": 18331, "\"...": 18332, "Ġcompetitors": 18333, "Ġprzed": 18334, "Ġapparent": 18335, "ĠArgentina": 18336, "ĠWorking": 18337, "Ġcollaborate": 18338, "woman": 18339, "Ġretain": 18340, "Ġleurs": 18341, "Ġdashboard": 18342, "×Ļ×ĵ": 18343, "ĠEarly": 18344, "BM": 18345, "ĠеÑij": 18346, "олог": 18347, "Ġsatisfying": 18348, "Ġoftentimes": 18349, "Ġmapping": 18350, "ünkü": 18351, "arth": 18352, "fold": 18353, "Ġlaunching": 18354, "Ġaura": 18355, "Ġprecision": 18356, "works": 18357, "God": 18358, "Ġstrap": 18359, "ĠImper": 18360, "Ġrivers": 18361, "Ġ|": 18362, "Ġcuer": 18363, "regon": 18364, "Ġarrival": 18365, "каÑħ": 18366, "ĠMiami": 18367, "анÑĭ": 18368, "Ġsurvivors": 18369, "ĠSenior": 18370, "David": 18371, "Ġestado": 18372, "Ġsectors": 18373, "Ġpopping": 18374, "Ġchim": 18375, "ayı": 18376, "Ġkunnen": 18377, "Ġgallery": 18378, "Ġsunlight": 18379, "esehen": 18380, "Ġyelling": 18381, "ĠMein": 18382, "ĠPhoenix": 18383, "Ġmano": 18384, "Ġhistoria": 18385, "Ġoccurring": 18386, "欸": 18387, "ì¸": 18388, "ади": 18389, "å¾ħ": 18390, "Ġinstitutional": 18391, "ĠTut": 18392, "ç²": 18393, "Ġslaves": 18394, "ãģ©ãģĨ": 18395, "Ġforgiveness": 18396, "Ġtwin": 18397, "ĠHyun": 18398, "нÑĮ": 18399, "ĠKomm": 18400, "andra": 18401, "shot": 18402, "ssä": 18403, "ĠÑĨе": 18404, "atta": 18405, "Ġexpense": 18406, "ĠGPU": 18407, "ĠPast": 18408, "ribly": 18409, "ĠëŃIJìķ¼": 18410, "Ġгода": 18411, "Ġrespir": 18412, "æĿ±": 18413, "ĠQueens": 18414, "hops": 18415, "Ġsérie": 18416, "Ġpref": 18417, "Ġcomed": 18418, "Ġplut": 18419, "ĠOverall": 18420, "ĠãģĿ": 18421, "Ġcush": 18422, "Ġringing": 18423, "Ġincorrect": 18424, "ĠÑģÑĤÑĢ": 18425, "Ġgeometry": 18426, "Ġadvertis": 18427, "ĠШ": 18428, "Ġreviewed": 18429, "ãģĤãģĤ": 18430, "Ġdozens": 18431, "Ġdetermination": 18432, "ĠPhill": 18433, "Ġcontributed": 18434, "ĠCit": 18435, "Ġpassengers": 18436, "Ġcôté": 18437, "Ġrever": 18438, "Ġtechnological": 18439, "Ġallen": 18440, "Ġraining": 18441, "avi": 18442, "Ġsalty": 18443, "Ġtyping": 18444, "ĠÑĤе": 18445, "Ġtilt": 18446, "Ġì¹ĺ": 18447, "ĠоÑĢ": 18448, "ĠпÑĢÑıм": 18449, "Ġrou": 18450, "Ġarena": 18451, "arat": 18452, "åĪ«": 18453, "HHHH": 18454, "Ġmanufacturers": 18455, "ĠEdward": 18456, "Ġtuck": 18457, "Ġblows": 18458, "ingo": 18459, "ĠMarc": 18460, "ìķĦìĦľ": 18461, "Mich": 18462, "ĠClean": 18463, "è´": 18464, "esto": 18465, "ĠPack": 18466, "Ġshaft": 18467, "BRUNO": 18468, "Ġaven": 18469, "uur": 18470, "ÑģколÑĮко": 18471, "ê´Ģ": 18472, "Ġautomated": 18473, "Ġventure": 18474, "Ġsurveillance": 18475, "ĠGrow": 18476, "ĠEmer": 18477, "ĠдоÑĢ": 18478, "Ġinvestor": 18479, "ĠYok": 18480, "Ġlatter": 18481, "ĠNI": 18482, "Ġfunctioning": 18483, "ĠHamilton": 18484, "Ġ51": 18485, "Ġmurdered": 18486, "Ġanchor": 18487, "Ġcuc": 18488, "ĠSCP": 18489, "ĠMadam": 18490, "Ġconstraints": 18491, "Ġbarn": 18492, "anken": 18493, "Ġë§İìĿĢ": 18494, "ĠMotor": 18495, "ĠDoing": 18496, "Ġamen": 18497, "etts": 18498, "Ġinstructor": 18499, "egt": 18500, "ako": 18501, "Ġposture": 18502, "ivia": 18503, "ĠPolish": 18504, "Ġдва": 18505, "Ġcolorful": 18506, "Ġelbow": 18507, "Ġparle": 18508, "Ġpasser": 18509, "Ġcondem": 18510, "ortal": 18511, "Ġfertil": 18512, "اد": 18513, "ĠColomb": 18514, "Ġalignment": 18515, "Ġastronaut": 18516, "ĠMut": 18517, "Ġsalmon": 18518, "Ġstructured": 18519, "ŀר": 18520, "Ġclicks": 18521, "Ġmiej": 18522, "æĶ¿": 18523, "ãģĦãĤĦ": 18524, "ĠRound": 18525, "Ġrainbow": 18526, "ĠVA": 18527, "ãģĶãģĸ": 18528, "ì§Ī": 18529, "otz": 18530, ",": 21732, "Ġchords": 21733, "ĠSanders": 21734, "Ġë¶Ħë": 21735, "Ben": 21736, "Ġdarüber": 21737, "ilians": 21738, "Ġordering": 21739, "ĠManh": 21740, "Ġkilogram": 21741, "ĠkarÅŁ": 21742, "Ġgrasp": 21743, "Ġghosts": 21744, "alen": 21745, "ĠJedi": 21746, "Ġбли": 21747, "Ġdownloaded": 21748, "Ġconducting": 21749, "ĠHak": 21750, "Ġresearcher": 21751, "ilan": 21752, "good": 21753, "ĠHannah": 21754, "ĠdÃ¼ÅŁÃ¼n": 21755, "ĠMessiah": 21756, "uity": 21757, "iona": 21758, "Ġprobable": 21759, "ĠYE": 21760, "Ġindependently": 21761, "Ġbuffer": 21762, "burn": 21763, "ourd": 21764, "ĠMcK": 21765, "Ġlingu": 21766, "ujemy": 21767, "еÑĢÑĤ": 21768, "Ġintuitive": 21769, "Ġcracks": 21770, "appropri": 21771, "nty": 21772, "Ġgeen": 21773, "Ġlend": 21774, "Ġcertification": 21775, "IDS": 21776, "unter": 21777, "pees": 21778, "Ġtrump": 21779, "Ġbankrupt": 21780, "Ġfeas": 21781, "èĹ": 21782, "Ġduż": 21783, "æ¸ħ": 21784, "Ġviruses": 21785, "Ġ58": 21786, "god": 21787, "Ġжел": 21788, "Ġstalk": 21789, "Ind": 21790, "achi": 21791, "ĠCF": 21792, "ĠCond": 21793, "Ġsanct": 21794, "Ġconten": 21795, "Ġfreed": 21796, "ĠRT": 21797, "Ġmentors": 21798, "족": 21799, "Ġportable": 21800, "ĠPaulo": 21801, "rane": 21802, "HAHA": 21803, "ĠSection": 21804, "çĨ": 21805, "hyun": 21806, "ĠÎŃÏĩ": 21807, "ĠPub": 21808, "ĠIndepend": 21809, "Ġcompounds": 21810, "ĠÑģÑĭ": 21811, "Ġmessaging": 21812, "Ġdedication": 21813, "Ġnoticing": 21814, "Ġdevoted": 21815, "ÑİÑĤÑģÑı": 21816, "Ġsnakes": 21817, "Ġbattlefield": 21818, "pers": 21819, "Ġdela": 21820, "92": 21821, "Ġhai": 21822, "illä": 21823, "érer": 21824, "every": 21825, "Ġresponsive": 21826, "×Ļ×ķ": 21827, "opf": 21828, "éī": 21829, "Ĭ¸": 21830, "Because": 21831, "Ġtourism": 21832, "Ġê·¸ê²Į": 21833, "×ķצ": 21834, "Ġcans": 21835, "stüt": 21836, "Ġdonne": 21837, "ĠDios": 21838, "ĠUber": 21839, "actory": 21840, "Ġoriented": 21841, "ĠHerm": 21842, "Ġpatron": 21843, "urf": 21844, "bei": 21845, "Ġprograma": 21846, "ĠOhh": 21847, "gener": 21848, "Ġfist": 21849, "ĠWendy": 21850, "Ġanda": 21851, "Ġguessed": 21852, "Ġfreak": 21853, "ä¸Ńåľĭ": 21854, "ĠKings": 21855, "chool": 21856, "Ġoffline": 21857, "ĠIndiana": 21858, "ĠAlliance": 21859, "Ġ53": 21860, "Ġparticul": 21861, "ĠFocus": 21862, "Ġinhabit": 21863, "Ġê°ĻìĿĢëį°": 21864, "ĠMcG": 21865, "owski": 21866, "ĠìĿ´ê±´": 21867, "ĠpaÅĦst": 21868, "они": 21869, "itta": 21870, "Ġconfirmation": 21871, "ĠBrooklyn": 21872, "Ġnoodle": 21873, "fund": 21874, "itud": 21875, "Ġgrandparents": 21876, "Ġbarbecue": 21877, "ειÏĤ": 21878, "Ġá": 21879, "Ġballot": 21880, "ĠVeter": 21881, "Ġpipes": 21882, "igious": 21883, "ĠGraph": 21884, "ested": 21885, "Ġë¸Įë": 21886, "ĠKE": 21887, "ãģ¡ãĤĩãģ£ãģ¨": 21888, "Ġeins": 21889, "Ġhatred": 21890, "ãģijãģ©": 21891, "Ġdang": 21892, "eeee": 21893, "Ġarchae": 21894, "ĠJesse": 21895, "Ġdetected": 21896, "Ġseni": 21897, "burgh": 21898, "Ġdisplacement": 21899, "Ġdop": 21900, "Ġconditioning": 21901, "ĠнеÑģколÑĮко": 21902, "Ġdisturbing": 21903, "PH": 21904, "Ġthinner": 21905, "Ġwounded": 21906, "ĠCuando": 21907, "Ġcushion": 21908, "Ġwhites": 21909, "Ġpreferences": 21910, "Ġì¤Ģë¹Ħ": 21911, "Ġkaż": 21912, "ĠGate": 21913, "ĠPath": 21914, "dles": 21915, "à¸Ħร": 21916, "imore": 21917, "Ġë³´ìŬ": 21918, "Ġdisciplines": 21919, "á»ı": 21920, "Ġmesma": 21921, "ĠìĥĪë": 21922, "Ġìĭ¬": 21923, "Ġging": 21924, "Ġumbrella": 21925, "IGHT": 21926, "Ġpension": 21927, "Ġcombining": 21928, "SS": 21929, "Ġrectangle": 21930, "á»ĩt": 21931, "Ġproxim": 21932, "ĠCow": 21933, "¸Į": 21934, "Ġintentional": 21935, "æķĻ": 21936, "Ġdecid": 21937, "ĠÑģкаж": 21938, "ĠUma": 21939, "iasm": 21940, "buz": 21941, "Ġdebris": 21942, "Ġcass": 21943, "ĠProp": 21944, "iska": 21945, "ëł¥": 21946, "esterol": 21947, "ussian": 21948, "ìĿ´ëŀij": 21949, "Ġunlimited": 21950, "Ġadmire": 21951, "Ġtightly": 21952, "Ġgenome": 21953, "ĠJunior": 21954, "venir": 21955, "gus": 21956, "ĠcÄĥ": 21957, "ĠVlad": 21958, "ĠíĤ": 21959, "Ġrelativ": 21960, "inci": 21961, "Ġaunque": 21962, "ĠBoys": 21963, "ÑĨион": 21964, "ĠSwiss": 21965, "Ġphysicians": 21966, "Ġíıī": 21967, 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"ĠTiger": 22025, "ĠONE": 22026, "Ġneurons": 22027, "owanie": 22028, "ÄĹ": 22029, "ĠShar": 22030, "ĠLandes": 22031, "Ġconferences": 22032, "該": 22033, "Ġcredential": 22034, "Ġlime": 22035, "inee": 22036, "xit": 22037, "pay": 22038, "Ġincons": 22039, "Ġ>>:": 22040, "èªį": 22041, "Ġíŀĺë": 22042, "Ġlesser": 22043, "Ġspill": 22044, "Ġpremise": 22045, "Ġ365": 22046, "ĠHost": 22047, "Ġtomar": 22048, "×IJ׾": 22049, "ë²Ī": 22050, "ĠWhats": 22051, "Ġlightweight": 22052, "ĠMap": 22053, "fia": 22054, "ellschaft": 22055, "Ġvendors": 22056, "uesto": 22057, "ĠMister": 22058, "ĠÐŁÑĢи": 22059, "åı³": 22060, "hma": 22061, "Ġintentionally": 22062, "ĠTang": 22063, "éĹ®": 22064, "Ġidentification": 22065, "Ġetcetera": 22066, "ĠNee": 22067, "ĠÑĤÑĢи": 22068, "ê·¸": 22069, "Ġcryptocur": 22070, "Ġinhale": 22071, "Ġaddict": 22072, "åIJĦä½į": 22073, "Ġmau": 22074, "ĠÑĤакаÑı": 22075, "Ġë²Ħ": 22076, "Ġcomprar": 22077, "iedzieÄĩ": 22078, "ĠоÑĤно": 22079, "Ġbeginner": 22080, "ĠмÑĥж": 22081, "Ġobsc": 22082, 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"ĠAH": 25888, "Ġpew": 25889, "Ġdictionary": 25890, "Take": 25891, "shirts": 25892, "ĠHugh": 25893, "ĠعÙĦÙĬ": 25894, "ĠPic": 25895, "Ġenrolled": 25896, "Ġjednak": 25897, "Ġofferings": 25898, "Ġcoraz": 25899, "Life": 25900, "Ġ!!!": 25901, "Ġcler": 25902, "ĠVideos": 25903, "ĠRodrig": 25904, "ĠIdent": 25905, "ĠPos": 25906, "ĠStage": 25907, "ĠRace": 25908, "Ġenact": 25909, "ãģĦãģ¾ãģĹãģŁ": 25910, "ĠGy": 25911, "ĠHispan": 25912, "Ġdefence": 25913, "ĠCampbell": 25914, "matic": 25915, "Ġrelev": 25916, "Ġpeach": 25917, "Ħ¸ìļĶ": 25918, "Ġparadise": 25919, "Ġceremon": 25920, "Ġannoyed": 25921, "æĮĩ": 25922, "lax": 25923, "Ġexploit": 25924, "Ġclause": 25925, "eker": 25926, "ĠBloom": 25927, "nant": 25928, "ateurs": 25929, "Ġheights": 25930, "Even": 25931, "Ñģон": 25932, "Ġoutrage": 25933, "ĠVietnamese": 25934, "ãģ¯ãģ¯": 25935, "TR": 25936, "Ġeer": 25937, "Ġcannon": 25938, "ĠComb": 25939, "IJë§Į": 25940, "è»Ĭ": 25941, "Ġê²ĥëıĦ": 25942, "Ġaccomplishments": 25943, "ĠAnalytics": 25944, "Ġshaping": 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"emas": 26062, "Ġãģ§ãģĻ": 26063, "ĠmajÄħ": 26064, "Ġwebinars": 26065, "Ġcannabis": 26066, "Ġdamals": 26067, "ĠNorthwest": 26068, "Ġpada": 26069, "Ġcrowds": 26070, "Ġfutures": 26071, "Ġän": 26072, "Ġcivilians": 26073, "ĠSachen": 26074, "æį": 26075, "Ġtraces": 26076, "Ġë¨¹ê³ł": 26077, "QU": 26078, "é¡ĺãģĦ": 26079, "ĠIF": 26080, "anın": 26081, "ìĤ´": 26082, "Ġbiblical": 26083, "ĠVed": 26084, "Ġstoring": 26085, "ÑĢавлÑı": 26086, "æĩī該": 26087, "Ġnast": 26088, "Ġdö": 26089, "ÑĢоп": 26090, "elia": 26091, "Ġsideways": 26092, "ĠUnderstand": 26093, "ĠQur": 26094, "Ġperpend": 26095, "ĠMillionen": 26096, "Ġwatermelon": 26097, "ĠDivine": 26098, "ultur": 26099, "abord": 26100, "Ġsuccesses": 26101, "Ġhombre": 26102, "Ġcarp": 26103, "Ġsuscept": 26104, "ungkin": 26105, "Ġkij": 26106, "ulus": 26107, "اج": 26108, "Ġnotch": 26109, "Ġpolynomial": 26110, "å¹²": 26111, "å©": 26112, "Ġúnico": 26113, "Ġtelescope": 26114, "Ġpolitique": 26115, "kiem": 26116, "ĠÎŃνα": 26117, "Ġaggregate": 26118, "ĠGeoff": 26119, "Ġtril": 26120, "ĠGRA": 26121, "Ġsubscriber": 26122, "imet": 26123, "ĠдоллаÑĢ": 26124, "oping": 26125, "Ġtherapeut": 26126, "ĠCancer": 26127, "Ġparade": 26128, "Ġirrig": 26129, "âĻªâĻª": 26130, "Ġclearer": 26131, "Ġbog": 26132, "ĠMaur": 26133, "าà¸ĩ": 26134, "ĠShanghai": 26135, "achte": 26136, "ĠKol": 26137, "elujah": 26138, "Ġhav": 26139, "ĠCrime": 26140, "sek": 26141, "Ġë¡ľ": 26142, "ienna": 26143, "ĠGor": 26144, "èĽ": 26145, "ĠпоÑĤÑĢ": 26146, "ĠкажеÑĤÑģÑı": 26147, "ĠLift": 26148, "ĠSort": 26149, "ĠPsal": 26150, "Ġping": 26151, "ĵĿ": 26152, "phis": 26153, "ĠFUCK": 26154, "ĠSyn": 26155, "Ġbamboo": 26156, "¬ìĺģ": 26157, "cuts": 26158, "Ġmmm": 26159, "Ġfunktioniert": 26160, "Ġ_": 26161, "ÃŃcio": 26162, "Stop": 26163, "Ġimaginary": 26164, "Ġnotamment": 26165, "ĠInitiative": 26166, "ãĥ¥": 26167, "ĠKurt": 26168, "Ġloosen": 26169, "Ġbuscar": 26170, "çģ«": 26171, "Ġzelf": 26172, "Ġprops": 26173, "åĽī": 26174, "Ġmoeten": 26175, "Ġmilli": 26176, "Ġhalls": 26177, "ĠMatch": 26178, "Ġbrackets": 26179, "ĠCou": 26180, "æ¦Ĥ": 26181, "ĠÐľÐ°ÑĢ": 26182, "ISA": 26183, "Ġcigarette": 26184, "Ġcompetitions": 26185, "ĠMIN": 26186, "Ġbehö": 26187, "voor": 26188, "Ġust": 26189, "ĠZi": 26190, "ĠOcc": 26191, "ulates": 26192, "Ġballoons": 26193, "Ġpronto": 26194, "ĠMiy": 26195, "ĠFile": 26196, "ĠклаÑģÑģ": 26197, "нÑĥл": 26198, "Ġcereal": 26199, "Ġincrement": 26200, "Ġrefined": 26201, "åı¦å¤ĸ": 26202, "prising": 26203, "ĠRF": 26204, "Ġrespectful": 26205, "Ġloot": 26206, "asket": 26207, "Ġdeixa": 26208, "ingle": 26209, "Ġfunciona": 26210, "ĠRevel": 26211, "Ġsober": 26212, "Ġperforms": 26213, "ĠGentle": 26214, "ãĤ¨": 26215, "Ġrecipient": 26216, "ĠHause": 26217, "Ġëĥ": 26218, "From": 26219, "Ġministers": 26220, "Ġparadox": 26221, "å°±æĺ¯èªª": 26222, "Ġtasting": 26223, "Ġ×Ķ×Ĺ": 26224, "Ġreuse": 26225, "ĠLane": 26226, "ĠÑģовеÑĢÑĪ": 26227, "Ġremembers": 26228, "Ġfeminist": 26229, "Ġcommitments": 26230, "Ġprojected": 26231, "Ġgaz": 26232, "iyoruz": 26233, "Ġobligations": 26234, "Ro": 26235, "zar": 26236, "Ġchw": 26237, "ĠJAM": 26238, "ĠbÄĻdÄħ": 26239, "aspberry": 26240, "ĠмеÑģÑĤо": 26241, "ë²ķ": 26242, "Ġregulated": 26243, "Ġwicht": 26244, "ĠTrevor": 26245, "Ġsecondly": 26246, "ĠIhre": 26247, "elsh": 26248, "Ġreporters": 26249, "ÑĤоÑĢа": 26250, "oyo": 26251, "GI": 26252, "Ġinterconnect": 26253, "éIJĺ": 26254, "OSH": 26255, "æŃ²": 26256, "Ġbrass": 26257, "Ġignoring": 26258, "ä»ĬæĹ¥": 26259, "infect": 26260, "Ġprojekt": 26261, "oret": 26262, "ÏĦαν": 26263, "ĠÑĤип": 26264, "Ġmutta": 26265, "Ġunboxing": 26266, "Ħ°": 26267, "å¡Ĭ": 26268, "Ġadvised": 26269, "ĠDenver": 26270, "Ġseverely": 26271, "ĠMhm": 26272, "Ġflipped": 26273, "Ġpien": 26274, "Ġkommun": 26275, "ĠFRE": 26276, "Ġà®ĩà®°": 26277, "ainted": 26278, "Ġknives": 26279, "Ġhabl": 26280, "Ġgeworden": 26281, "arettes": 26282, "CS": 26283, "ĠмаленÑĮ": 26284, "Ġgalax": 26285, "Ġninete": 26286, "ê±°ëĤĺ": 26287, "Ġsis": 26288, "Ġadvisory": 26289, "Ġdrilling": 26290, "ĠWouldn": 26291, "ünf": 26292, "gestellt": 26293, "ĠHelen": 26294, "Ġ×ŀ×IJ": 26295, "apolis": 26296, "Ġrzeczy": 26297, "Ġterra": 26298, "Ġhep": 26299, "Ġalgún": 26300, "ikk": 26301, "Ġastronom": 26302, "ĠStarbucks": 26303, "kÄħ": 26304, "Ġpatrol": 26305, "Ġì½Ķ": 26306, "Ġgon": 26307, "ĠãĢIJ": 26308, "Ġsonst": 26309, "Ġencounters": 26310, "Ġretrou": 26311, "Ġsharks": 26312, "Ġdor": 26313, "ĠRever": 26314, "Ġevapor": 26315, "Ġreservoir": 26316, "Ġalleged": 26317, "uler": 26318, "Ġverm": 26319, "Ġcommerce": 26320, "Ġfitted": 26321, "gem": 26322, "Ġtactical": 26323, "Ġlith": 26324, "éīĦå¡Ķ": 26325, "had": 26326, "è®Ĭ": 26327, "Ġcarbohyd": 26328, "Ġlengths": 26329, "ιο": 26330, "Ġdemographic": 26331, "Rob": 26332, "ĠSkin": 26333, "ccoli": 26334, "Ġsimplified": 26335, "Ġreadily": 26336, "ĠCum": 26337, "adesh": 26338, "ĠDÃ¥": 26339, "usst": 26340, "igne": 26341, "eton": 26342, "Ġmenor": 26343, "qi": 26344, "OOM": 26345, "à¸Ńà¸Ļ": 26346, "Ġpsychiat": 26347, "Ġeighty": 26348, "Ġмилли": 26349, "ĠTob": 26350, "edo": 26351, "網": 26352, "ĠÄijến": 26353, "Ġcircuits": 26354, "ĠLAUGH": 26355, "icism": 26356, "emor": 26357, "Ġregener": 26358, "egree": 26359, "Ġbureauc": 26360, "ĠAlber": 26361, "ä¹ĭå¾Į": 26362, "ĠWor": 26363, "夫": 26364, "Ġresin": 26365, "ĠbyÅĤy": 26366, "ĠIG": 26367, "à¯į,": 26368, "Ġ78": 26369, "Ġweeds": 26370, "ĠMyth": 26371, "93": 26372, "æ¿": 26373, "ĠëĤĺìĻĶ": 26374, "év": 26375, "á½": 26376, "ören": 26377, "çar": 26378, "ĠPAUL": 26379, "Ġdisadvant": 26380, "Ġpositioning": 26381, "Ġcocktail": 26382, "Ġagrees": 26383, "nn": 26384, "ĠSally": 26385, "Ms": 26386, "Ġinherent": 26387, "Ġmonetary": 26388, "Ġnatur": 26389, "ĠNh": 26390, "ĠImport": 26391, "Ġleben": 26392, "Ġwi": 26393, "ussy": 26394, "Ġobes": 26395, "Ġwandering": 26396, "Ġìĭłë": 26397, "Äħda": 26398, "etchup": 26399, "Ġdisposal": 26400, "ĠJA": 26401, "ĠCer": 26402, "zilla": 26403, "Ġvirgin": 26404, "ĠSlide": 26405, "andel": 26406, "Ġrighteousness": 26407, "ĠΣ": 26408, "Ġideia": 26409, "ä½łå¥½": 26410, "иÑĢоваÑĤÑĮ": 26411, "ר×IJ": 26412, "Comment": 26413, 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26471, "ĠпÑĢиÑĩ": 26472, "ĠHeavy": 26473, "Ġswords": 26474, "Ġmanipulation": 26475, "Ġfunk": 26476, "ĠvÃ¥r": 26477, "ĠTaliban": 26478, "Ġë°¥": 26479, "Ġacne": 26480, "ürü": 26481, "Ġdeswegen": 26482, "ĠDust": 26483, "Ġsilic": 26484, "Ġhooks": 26485, "Ġblij": 26486, "Ġpetits": 26487, "Ġfilme": 26488, "ĠBereich": 26489, "ĠSaid": 26490, "Ġimposed": 26491, "Ġdiary": 26492, "ĠгоÑĢ": 26493, "ĠGates": 26494, "Ġalta": 26495, "å¸Į": 26496, "Ġchcia": 26497, "pleasant": 26498, "Ġë°Ŀ": 26499, "Ġmożemy": 26500, "ĠAustria": 26501, "Ġbroker": 26502, "Ġsucked": 26503, "èĢĥ": 26504, "Ġcompartment": 26505, "Ġclone": 26506, "Ġ×Ķ×¢": 26507, "ĠDanke": 26508, "Ġnochmal": 26509, "езд": 26510, "Ġadrenal": 26511, "Ġkleinen": 26512, "ãģ¾ãģĹãĤĩãģĨ": 26513, "Ġsubsequently": 26514, "Ġdecentral": 26515, "Ġgenetics": 26516, "Ġê´ij": 26517, "Ġmonitors": 26518, "ĠApplic": 26519, "ĠReporter": 26520, "wert": 26521, "Ġwiem": 26522, "ĠMovement": 26523, "Ġinterviewing": 26524, "Ġhairs": 26525, "Ġpuò": 26526, "ĠChelsea": 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"ĠCrim": 30691, "ût": 30692, "78": 30693, "Ġsincerely": 30694, "raz": 30695, "Ġë³µ": 30696, "Ġarriv": 30697, "Ġconception": 30698, "ĠPersian": 30699, "Ġsjäl": 30700, "Ġstarring": 30701, "ĠìķĦ무": 30702, "ĠForever": 30703, "еÑģÑĤÑĮ": 30704, "Ġveil": 30705, "Ġsubtit": 30706, "odka": 30707, "ĠоÑĤноÑĪ": 30708, "Ġcooks": 30709, "енÑı": 30710, "Kay": 30711, "Ġniños": 30712, "ĠPhone": 30713, "Ġstitching": 30714, "Ġfingerprint": 30715, "é¢ĺ": 30716, "λά": 30717, "Ġdedicate": 30718, "ĠLob": 30719, "Ġblacks": 30720, "ĠBle": 30721, "bout": 30722, "ĠÄijang": 30723, "Ġeks": 30724, "Ġsquash": 30725, "ĠKü": 30726, "odi": 30727, "ĠnÆ°á»Ľc": 30728, "Ġvoyage": 30729, "Ġplayful": 30730, "ĠØ¥ÙĦÙī": 30731, "anic": 30732, "Ġcondemn": 30733, "ĠBöyle": 30734, "ĠPolize": 30735, "ãĤ¿ãĥ¼": 30736, "Ġayuda": 30737, "Ġpam": 30738, "à¹Ħà¸Ľ": 30739, "ĠKathy": 30740, "един": 30741, "нова": 30742, "Ġbrig": 30743, "eger": 30744, "Ġeagle": 30745, "Ġvisions": 30746, "ĠíķŃìĥģ": 30747, "Ġshitty": 30748, "Ġhott": 30749, "ĠBritt": 30750, "utors": 30751, "ENTE": 30752, "æĽ²": 30753, "Ġphon": 30754, "ĠBing": 30755, "ĠподдеÑĢж": 30756, "spring": 30757, "æĸ¯": 30758, "etten": 30759, "Ġpilgr": 30760, "Ġediyor": 30761, "енÑĤÑĭ": 30762, "aggio": 30763, "Ġjul": 30764, "Ġcomprend": 30765, "teil": 30766, "Ġز": 30767, "Ġperformers": 30768, "Ġinfamous": 30769, "ĠMK": 30770, "çª": 30771, "æ³ģ": 30772, "otle": 30773, "eff": 30774, "ĠHash": 30775, "Ġcoward": 30776, "ĠBRA": 30777, "ĠDD": 30778, "Ġcomida": 30779, "Ġplata": 30780, "Ġflap": 30781, "ĠMehr": 30782, "ribution": 30783, "ĠYemen": 30784, "Ġmysteries": 30785, "ĠÄ°yi": 30786, "Ġstell": 30787, "Ġeyeliner": 30788, "Ġdeles": 30789, "Ġnailed": 30790, "Ġillnesses": 30791, "Ġstacks": 30792, "Ġtrabajar": 30793, "flower": 30794, "ciu": 30795, "Ġcrude": 30796, "Ġsubstantially": 30797, "Ġhomem": 30798, "Ġnephew": 30799, "Ġstamps": 30800, "Ġcarbs": 30801, "ÑĮÑĤе": 30802, "mooth": 30803, "Ġtunnels": 30804, "acie": 30805, "æ³¢": 30806, "ĠSeñ": 30807, "ĠHera": 30808, "ĠìķĦëĭĪìĹIJìļĶ": 30809, "ĠWyoming": 30810, "ĠHDMI": 30811, "ĠLis": 30812, "ución": 30813, "Ġsteer": 30814, "оÑİ": 30815, "иÑĤа": 30816, "NT": 30817, "Ġìĸ¼êµ´": 30818, "Ġpalms": 30819, "Ġneon": 30820, "ованиÑı": 30821, "Ġfiltering": 30822, "Ġjouer": 30823, "ĠHö": 30824, "ĠнеÑģ": 30825, "ê²łìĸ´ìļĶ": 30826, "Ġ81": 30827, "Ġstoryline": 30828, "Ġprzep": 30829, "Ġthanking": 30830, "ĠBoeing": 30831, "Ġsoftly": 30832, "jem": 30833, "алÑĮнÑĭÑħ": 30834, "Ġflashlight": 30835, "ĠпÑĥ": 30836, "ĠWOMAN": 30837, "ắc": 30838, "ÃŃch": 30839, "Ġluxurious": 30840, "Ġwün": 30841, "Ġimpactful": 30842, "Ġconson": 30843, "reu": 30844, "irring": 30845, "ifter": 30846, "Ġconstituents": 30847, "èIJ½": 30848, "Ġ94": 30849, "ĠTou": 30850, "gom": 30851, "ĠìĥĿê°ģìĿĦ": 30852, "Ġstereotypes": 30853, "Ġmożli": 30854, "åĪĨ享": 30855, "Ĥ¨": 30856, "Ġpencils": 30857, "ĠÑģлож": 30858, "Ġihrem": 30859, "ĠBesch": 30860, "ĠKoh": 30861, "ĠEntscheid": 30862, "Ġlek": 30863, "Ġförs": 30864, "Ġtotalmente": 30865, "Ġlively": 30866, "Ġentropy": 30867, "Ġdiscern": 30868, "ĠÐĹна": 30869, "Ġdov": 30870, "Ġmythology": 30871, "è¨ĺå¾Ĺ": 30872, "apanese": 30873, "Ġapproximate": 30874, "аÑĤив": 30875, "ifiable": 30876, "ĠSeo": 30877, "åĢĴ": 30878, "´ìĭ¬íŀĪ": 30879, "Ġìĺ·": 30880, "Ġtemporal": 30881, "ĠiT": 30882, "Ġestat": 30883, "ким": 30884, "Ġsprink": 30885, "Ġgrund": 30886, "Ġinfantry": 30887, "Ġschaffen": 30888, "ç´Ħ": 30889, "Ġank": 30890, "riages": 30891, "ĠYeon": 30892, "ĠMoroc": 30893, "Ġinvasive": 30894, "ģĶ": 30895, "Ġparenting": 30896, "ĠRis": 30897, "ibile": 30898, "Ġmods": 30899, "å½¢": 30900, "ĠпÑĢовеÑĢ": 30901, "ĠThing": 30902, "ĠWherever": 30903, "Ġacknowledging": 30904, "Ġpawn": 30905, "ummer": 30906, "orb": 30907, "69": 30908, "Ġretrouve": 30909, "Ġrelies": 30910, "ĠHighway": 30911, "Ġawe": 30912, "ãģ§ãģĻãģĭ": 30913, "itaire": 30914, "Ġapplicant": 30915, "Ġaisle": 30916, "worm": 30917, "Ġpayload": 30918, "Ġcarre": 30919, "ĠBach": 30920, "æł¼": 30921, "Ġì¹ľêµ¬ë": 30922, "ние": 30923, "ĠitÃŃs": 30924, "onnaise": 30925, "sol": 30926, "èı¯": 30927, "algia": 30928, "Ġrocking": 30929, "Ġbesten": 30930, "rites": 30931, "^^": 30932, "иной": 30933, "Ġbaixo": 30934, "Ġ기ìĸµ": 30935, "оÑĤÑĢи": 30936, "sim": 30937, "Ġincarn": 30938, "ëĭ¤ìĿĮ": 30939, "Ġlick": 30940, "sided": 30941, "Ġ71": 30942, "forder": 30943, "Ġresonance": 30944, "Ġtegen": 30945, "Ġmetaph": 30946, "owser": 30947, "Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ðŁ¤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çĻ¾": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 32258, "Ġalleen": 32259, "éĢĻä¸Ģ": 32260, 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34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, 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34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, "arella": 34704, "Äĥng": 34705, "ramento": 34706, "Ġnutritional": 34707, "Ġdictators": 34708, "Ġafin": 34709, "Ġfuzzy": 34710, "ĠGina": 34711, "ót": 34712, "ĠExtremadura": 34713, "Ġdemonstrations": 34714, "ĠMontgomery": 34715, "íķ´ìĦ¤": 34716, "ĠGandhi": 34717, "ãĥĿ": 34718, "ç½®": 34719, "Ġreunion": 34720, "ĠjakiÅĽ": 34721, "ĠZug": 34722, "OUGH": 34723, "lifting": 34724, "Ġà²": 34725, "á¹Ľá¹£": 34726, "eb": 34727, "ĠWOW": 34728, "ĠShiva": 34729, "ometry": 34730, "Ġwildly": 34731, "Ġtended": 34732, "Ġmegap": 34733, "ì²ĺ": 34734, "Ġnause": 34735, "Ġgerek": 34736, "ãĥĭ": 34737, "ĠMarcel": 34738, "Ġneste": 34739, "خر": 34740, "Ġfeh": 34741, "åĨħ": 34742, "suspenseful": 34743, "ĠWrestle": 34744, "ĠPalestinians": 34745, "ĠGORD": 34746, "iyet": 34747, "ĠÑĢади": 34748, 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34806, "ĠAntrag": 34807, "ĠAMD": 34808, "ĠCrypt": 34809, "Ġë©Ķ": 34810, "ĠFelipe": 34811, "ĠCOB": 34812, "elu": 34813, "Ġselfies": 34814, "ĠSanti": 34815, "chutz": 34816, "ĠУкÑĢаÑĹ": 34817, "gesamt": 34818, "Ġflock": 34819, "jaz": 34820, "plain": 34821, "Ġwrinkles": 34822, "Ġreais": 34823, "Ġpaljon": 34824, "Ġempowerment": 34825, "Ġattendees": 34826, "ppa": 34827, "Ġneden": 34828, "онÑĭ": 34829, "Ġtimeframe": 34830, "ĠCherry": 34831, "Ġidée": 34832, "Ġgag": 34833, "Ġdonkey": 34834, "Ġông": 34835, "ĠHare": 34836, "éļĽ": 34837, "ĠKara": 34838, "Ġacompan": 34839, "places": 34840, "imientos": 34841, "ĠHamm": 34842, "би": 34843, "uben": 34844, "iliyor": 34845, "Ġthirst": 34846, "Ġkry": 34847, "ĠGeorgetown": 34848, "׳×Ķ": 34849, "Ġorch": 34850, "Ġheartbeat": 34851, "Ġtransformations": 34852, "estones": 34853, "ĠKH": 34854, "Ġcartoons": 34855, "Ġanci": 34856, "Ġworthless": 34857, "Ġtailored": 34858, "pu": 34859, "Americans": 34860, "Ġpiles": 34861, "ĠMonkey": 34862, "Ġbasin": 34863, "ĠTemper": 34864, "ĠPaint": 34865, "Ġpunching": 34866, "Ġbaik": 34867, "ĠOakland": 34868, "vre": 34869, "ÅŁallah": 34870, "ydd": 34871, "Ġcasually": 34872, "odu": 34873, "Ġcoded": 34874, "ĠNorwegian": 34875, "ĠVince": 34876, "Ġpremature": 34877, "ĠPromise": 34878, "екÑģÑĤ": 34879, "Ġdevastated": 34880, "ĠPremium": 34881, "ĠParam": 34882, "ĠÃĸyle": 34883, "umuz": 34884, "PO": 34885, "rators": 34886, "Ġlamps": 34887, "Ġterritorial": 34888, "Ġbackbone": 34889, "listed": 34890, "DY": 34891, "ĠاÙĦر": 34892, "Ġpursued": 34893, "ĠCommons": 34894, "Ġ곡": 34895, "locks": 34896, "edor": 34897, "Ġconceived": 34898, "gere": 34899, "Ġdisappearing": 34900, "ĠSull": 34901, "ĠìĹ°ë": 34902, "Ġhoffe": 34903, "Ġdetox": 34904, "íĶĮ": 34905, "Ġretir": 34906, "ĠëģĿëĤ": 34907, "Ġpergunta": 34908, "ĠBOY": 34909, "ç²¾": 34910, "Ġpenn": 34911, "æĿ¥äºĨ": 34912, "hés": 34913, "hon": 34914, "Ġcatastrophic": 34915, "Ġaust": 34916, "Ġtorso": 34917, "Ġìĸ´ëĬIJ": 34918, "ĠìĤ¬ëŀĮëĵ¤ìĿ´": 34919, "Ġmarvelous": 34920, 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35094, "ĠTransportation": 35095, "ĠMaker": 35096, "Ġjohn": 35097, "Ġrichest": 35098, "Ġtraumat": 35099, "Ġliegen": 35100, "´ë¥¼": 35101, "è¿ĻéĩĮ": 35102, "Ġunrest": 35103, "ĠStraw": 35104, "æĭľæĭľ": 35105, "Ġcoma": 35106, "ĠKristen": 35107, "ĠÐļонеÑĩно": 35108, "ĠBryce": 35109, "ĠÑıкÑĸ": 35110, "Ġpearls": 35111, "ĠпонимаÑİ": 35112, "Ġadditions": 35113, "Ġasympt": 35114, "ĠменÑĮÑĪе": 35115, "Ġscans": 35116, "Child": 35117, "ĠHide": 35118, "кÑĥÑİ": 35119, "etas": 35120, "Ġdank": 35121, "Ġpleas": 35122, "Ġessays": 35123, "Ġjets": 35124, "åħĴ": 35125, "Ġвед": 35126, "Ġpositives": 35127, "hof": 35128, "-)": 35129, "zzo": 35130, "Ġstarters": 35131, "Ġsmiled": 35132, "Ġ1944": 35133, "quiera": 35134, "Ġrok": 35135, "Ġpuesto": 35136, "Nico": 35137, "Ġsimulations": 35138, "Ġà¶": 35139, "Ġintrigued": 35140, "ĠOverwatch": 35141, "åĸĤ": 35142, "sigh": 35143, "bai": 35144, "Ġë§IJê³ł": 35145, "idé": 35146, "Ġcrabs": 35147, "áºŃp": 35148, "ĠIraqi": 35149, "ìĿ´ë¥¼": 35150, "ÑĤÑı": 35151, "ĠSophia": 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36303, "ARIN": 36304, "Ġprogressing": 36305, "ĠGir": 36306, "Ġpenetrate": 36307, "ĠStorage": 36308, "eight": 36309, "ĠÑĤÑĢÑĥ": 36310, "ĠdonÃŃt": 36311, "Ġsizin": 36312, "Ġoutdated": 36313, "ĠнаÑĪи": 36314, "Ġaffir": 36315, "Ġspoons": 36316, "Ġoni": 36317, "Ġflank": 36318, "ĠGol": 36319, "hã": 36320, "Ġpéri": 36321, "Ġhonorable": 36322, "ĠBreathe": 36323, "scenes": 36324, "Ġobviamente": 36325, "икÑģ": 36326, "Ġש×ŀ×": 36327, "Ġsmoothie": 36328, "ŀĪë": 36329, "Ġdime": 36330, "ĠíĸĪìĸ´ìļĶ": 36331, "Ġappel": 36332, "ĠCatholics": 36333, "Ġsingles": 36334, "Ġlaten": 36335, "Ġçünkü": 36336, "ĠVader": 36337, "æıĽ": 36338, "Ġvardı": 36339, "ĠIstanbul": 36340, "gré": 36341, "ĠElsa": 36342, "ël": 36343, "Ġinvece": 36344, "Ġcrane": 36345, "Ġobe": 36346, "ĠShark": 36347, "Ġsmack": 36348, "Ġrestoring": 36349, ".\\": 36350, "Ġë¹łë": 36351, "Ġfaded": 36352, "umbers": 36353, "Singing": 36354, "Ġdepressing": 36355, "thest": 36356, "ĠWahr": 36357, "Ġmultitude": 36358, "ÑĢавÑģÑĤвÑĥйÑĤе": 36359, "rijk": 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"presented": 36589, "Ġprowad": 36590, "ĠTerr": 36591, "ĠPrinceton": 36592, "Ġmorgen": 36593, "Ġattracting": 36594, "ĠSigma": 36595, "igner": 36596, "ĠRechts": 36597, "ĠPeki": 36598, "Ġmethy": 36599, "Ġhamm": 36600, "Ġdireito": 36601, "Ġdelegation": 36602, "иваÑİÑĤ": 36603, "Ġgin": 36604, "Young": 36605, "Ġdependencies": 36606, "ĠBradley": 36607, "buds": 36608, "Ġfis": 36609, "Ġpytanie": 36610, "Ġinterconnected": 36611, "Ġembaixo": 36612, "ĠSas": 36613, "Ġruh": 36614, "ĠSicht": 36615, "Sur": 36616, "Ġsuperb": 36617, "ĠSabbath": 36618, "ĠDanger": 36619, "kol": 36620, "Ġhou": 36621, "supp": 36622, "ĠNacional": 36623, "Ġsuccession": 36624, "Ġvá": 36625, "ĠMaÃŁnahmen": 36626, "ĠJessie": 36627, "ĠIdaho": 36628, "forest": 36629, "ħĺ": 36630, "Ġ×ŀ×ĵ": 36631, "ĠØ£ÙĬ": 36632, "Ġsweetheart": 36633, "Ġneatly": 36634, "ĠEvangel": 36635, "곡": 36636, "ĠSuite": 36637, "ública": 36638, "ĠÑĥли": 36639, "ĠAnnouncer": 36640, "ligh": 36641, "Ġsensations": 36642, "Ġshelters": 36643, "Ġhart": 36644, 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"Ver": 36929, "Ġnorthwest": 36930, "ĠMaintenant": 36931, "ĠLakes": 36932, "аÑıв": 36933, "inté": 36934, "ì°½": 36935, "Ġгаз": 36936, "Ġgiorn": 36937, "Ġdigitally": 36938, "ĠCircuit": 36939, "ì¼Ģ": 36940, "ãĤĬãģ¾ãģĹãģŁ": 36941, "Ġcheerful": 36942, "ĠPeterson": 36943, "ĠDanish": 36944, "ativos": 36945, "Ġliken": 36946, "Ġharbor": 36947, "алиÑģÑĤ": 36948, "xe": 36949, "Ġcurls": 36950, "ĠRhod": 36951, "End": 36952, "ĠET": 36953, "Ġacquaint": 36954, "ĠKelvin": 36955, "Ġtrif": 36956, "ĠAway": 36957, "ìŀIJëĬĶ": 36958, "vs": 36959, "Ġpágina": 36960, "Ġinlet": 36961, "ĠSantos": 36962, "Ġìļ°ìĻĢ": 36963, "Ġyapıyorsun": 36964, "theme": 36965, "Ġsouff": 36966, "Ġinjected": 36967, "Ġpóźniej": 36968, "iverso": 36969, "amped": 36970, "Ġdaher": 36971, "Ġdagger": 36972, "ĠлÑİбим": 36973, "Ġtummy": 36974, "Ġenlightened": 36975, "cents": 36976, "ĠDah": 36977, "Ġcuest": 36978, "ä¾Ĩ說": 36979, "ILY": 36980, "Ġ×ijר": 36981, "Ġbanging": 36982, "ĠEmil": 36983, "ĠCler": 36984, "ĠBorder": 36985, "ижÑĥ": 36986, "Ġpresenters": 36987, "ĠSTUD": 36988, "coins": 36989, "ĠíĻį": 36990, "Ġperks": 36991, "Ġparap": 36992, "Ġcertaines": 36993, "ĠLore": 36994, "öst": 36995, "ĠMARTIN": 36996, "Ġbios": 36997, "Ġwhereby": 36998, "verts": 36999, "ĠMiranda": 37000, "Ġstip": 37001, "澤": 37002, "andez": 37003, "׼׾": 37004, "ujin": 37005, "Ġê¾": 37006, "Ġallergies": 37007, "plate": 37008, "Ġyapıl": 37009, "Ġundertake": 37010, "ĠëĤĺê°Ģ": 37011, "Part": 37012, "Ġkızım": 37013, "hguru": 37014, "ãģĤãģ¨": 37015, "ĠJohns": 37016, "Ġeyelashes": 37017, "Ġdrained": 37018, "ĠstÃ¥r": 37019, "ãģĤãĤĬãģ¾ãģĻ": 37020, "ĠJade": 37021, "Ġcalend": 37022, "film": 37023, "Ġmesa": 37024, "Ġludzie": 37025, "Ġattracts": 37026, "Ġjuices": 37027, "Ġкил": 37028, "Ġnieuwe": 37029, "Ġmencion": 37030, "Ġignition": 37031, "Ġbladder": 37032, "andaag": 37033, "ĠExtension": 37034, "íĤ¨": 37035, "feed": 37036, "ĠÙĪÙĩ": 37037, "Ġspun": 37038, "Ġtät": 37039, "оÑĢоÑĤ": 37040, "tyard": 37041, "ronics": 37042, "ĠHuge": 37043, "Ñĥжд": 37044, "string": 37045, "Ġunjust": 37046, "Ġprawn": 37047, "Ġfrosting": 37048, "Ġdisappearance": 37049, "iosa": 37050, "Ġcardi": 37051, "ĠPriest": 37052, "ĠcientÃŃfic": 37053, "åĵªè£¡": 37054, "ĠÐĴаÑģ": 37055, "Ġë¶Ģíĥģ": 37056, "Ġthieves": 37057, "Ġphysique": 37058, "ĠEugene": 37059, "Ġблиз": 37060, "Ġmonopoly": 37061, "Ġbiography": 37062, "ĠhoÅŁ": 37063, "Ġtö": 37064, "mac": 37065, "Ġshocks": 37066, "ìĦ¸ë": 37067, "hit": 37068, "Ġsnug": 37069, "Ġincl": 37070, "Ġdedic": 37071, "Ġultras": 37072, "ĠизвеÑģÑĤ": 37073, "Ġutilization": 37074, "ĠÑģовеÑĢÑĪенно": 37075, "Ġservi": 37076, "stag": 37077, "180": 37078, "Ġsewer": 37079, "ĠChoice": 37080, "Ġdischarged": 37081, "ĠJD": 37082, "олеÑĤ": 37083, "ĠкваÑĢÑĤи": 37084, "Ġtelescop": 37085, "ĠJeÅĽli": 37086, "ĠNana": 37087, "cale": 37088, "ĠÑĤон": 37089, "mmm": 37090, "äºĨåIJ§": 37091, "Ġgehabt": 37092, "ëĤł": 37093, "æĬķ": 37094, "à¸Ļà¸Ļ": 37095, "Ġether": 37096, "Ġzen": 37097, "Ġresearched": 37098, "ĠCzyli": 37099, "å®Įåħ¨": 37100, "workers": 37101, "Ġ경찰": 37102, "Ġsheriff": 37103, "allo": 37104, "Ġtipos": 37105, "Ġprosecution": 37106, "Ġfrogs": 37107, "Ġfalt": 37108, "jd": 37109, "ĠíĮĶ": 37110, "Ġfiltered": 37111, "ĠOft": 37112, "Ġìį": 37113, "Ġdisfr": 37114, "ĠMustang": 37115, "Ġwoah": 37116, "ĠREALLY": 37117, "Ġмогли": 37118, "Ġentrada": 37119, "ĠигÑĢа": 37120, "Ġmixes": 37121, "ĠавÑĤомоб": 37122, "ÐĻ": 37123, "Ġshin": 37124, "Ġparanormal": 37125, "Ġsomeplace": 37126, "Ġdishon": 37127, "etaan": 37128, "Ġfuerte": 37129, "Ù¹": 37130, "Ġdoom": 37131, "ìĪľ": 37132, "Ġexistential": 37133, "Ġbuld": 37134, "ĠSDK": 37135, "ĠпÑĢавда": 37136, "Ġturnover": 37137, "ĠìĹ¬ê¸°ìĹIJ": 37138, "Ġह": 37139, "Ġmodeled": 37140, "Ġbugün": 37141, "Ġexperimentation": 37142, "Ġmornings": 37143, "Ġmedo": 37144, "Stevie": 37145, "Ġplayable": 37146, "Ġairlines": 37147, "gments": 37148, "Ġ기ë¶Ħ": 37149, "ĠTomb": 37150, "ĠMVP": 37151, "AUDIENCE": 37152, "Ġcheckout": 37153, "Ġpasst": 37154, "Ġbeispiel": 37155, "ĠLinks": 37156, "heavy": 37157, "Ġquestionable": 37158, "Ġìĵ°ë": 37159, "Ġsill": 37160, "Ġmanipulated": 37161, "ĠLoren": 37162, "Ġìľ¼": 37163, "Ġverge": 37164, "ák": 37165, "IES": 37166, "Ġsabot": 37167, "ĠCustomer": 37168, "ależy": 37169, "Ġnominee": 37170, "ĠGad": 37171, "Ġnouvelles": 37172, "ĠSPE": 37173, "istling": 37174, "Ġoval": 37175, "обÑĢаж": 37176, "ifty": 37177, "éĩİ": 37178, "Ġbezel": 37179, "yet": 37180, "Ġfreight": 37181, "ĠHanım": 37182, "rÃŃa": 37183, "Ġzoning": 37184, "Ġindem": 37185, "ĠBü": 37186, "Ġfeminism": 37187, "Ġvoix": 37188, "Ġoficial": 37189, "Ġdiyorum": 37190, "»IJ": 37191, "Ġarose": 37192, "Ġparar": 37193, "ìĿ¸ì§Ģ": 37194, "ĠMartine": 37195, "ĠLect": 37196, "Ġrester": 37197, "Ġdrowning": 37198, "uya": 37199, "cida": 37200, "ĠAriel": 37201, "Ġ02": 37202, "Ġ×Ķ×Ķ": 37203, "ç´ł": 37204, "ĠWert": 37205, "ТÑĭ": 37206, "Ġwidow": 37207, "Ġparchment": 37208, "Ġcottage": 37209, "ĠXL": 37210, "ĠSlack": 37211, "ĠNES": 37212, "Ġrobe": 37213, "Ġgimm": 37214, "Ġcaminho": 37215, "ĠHarper": 37216, "Ġcitrus": 37217, "Ġfirefighters": 37218, "Ġdopamine": 37219, "elets": 37220, "Ġdemocrat": 37221, "ìłľë¡ľ": 37222, "Ġplayback": 37223, "oj": 37224, "ĠпÑĢок": 37225, "ĠSullivan": 37226, "semble": 37227, "ĠWorth": 37228, "ĠMustafa": 37229, "าร": 37230, "Ġmets": 37231, "éĸĢ": 37232, "лоÑģÑĮ": 37233, "Ġinertia": 37234, "Ġuniforms": 37235, "足": 37236, "ério": 37237, "×ķר×Ķ": 37238, "ént": 37239, "Ġà®Ĵ": 37240, "ĠÑģамÑĭÑħ": 37241, "Ġvoulais": 37242, "ĠZimmer": 37243, "ê²łë": 37244, "ĠноÑģ": 37245, "encias": 37246, "Ġrelación": 37247, "Ġ걸ë": 37248, "Ġfaction": 37249, "Ġgosp": 37250, "полож": 37251, "nap": 37252, "hak": 37253, "Ġproceedings": 37254, "ĠìĨĶ": 37255, "ìķĦëĭĪ": 37256, "ĠìŀIJ기": 37257, "Ġwerd": 37258, "Ġsof": 37259, "Ġschlim": 37260, "Ġflavored": 37261, "Ġquadratic": 37262, "ĠBoot": 37263, "Ġpublicity": 37264, "ĠCaro": 37265, "Ġ?\"": 37266, "ниÑĨа": 37267, "mania": 37268, "ĠSUR": 37269, "ĠBUR": 37270, "lance": 37271, "ética": 37272, "Ġzobaczy": 37273, "Ġtrio": 37274, "sama": 37275, "ĠtaÅŁ": 37276, "Ġasymm": 37277, "resser": 37278, "Ġتع": 37279, "ĠпеÑģ": 37280, "Ġbeginnings": 37281, "ladım": 37282, "ĠбÑĭÑģÑĤÑĢ": 37283, "Ġmoo": 37284, "ĠGeneva": 37285, "Ġåľ¨": 37286, "erus": 37287, "borah": 37288, "Ġrefusing": 37289, "bull": 37290, "ĠWaiting": 37291, "ĠIndividual": 37292, "Ġanonym": 37293, "imens": 37294, "Ġmedidas": 37295, "Ġfragrant": 37296, "Ġdirectement": 37297, "ĠìķĦë§Ī": 37298, "uria": 37299, "Ġspherical": 37300, "Ġabge": 37301, "ĠVictorian": 37302, "Ġspectacle": 37303, "ĠRodriguez": 37304, "Ġocup": 37305, "ĠNär": 37306, "marks": 37307, "ngulo": 37308, "ĠLuci": 37309, "Ġshouted": 37310, "Ġregulators": 37311, "ÄŁini": 37312, "Ġdisent": 37313, "ĠÑĢÑĭн": 37314, "ëĤ¨": 37315, "ĠìĤ´ë": 37316, "Ġproblèmes": 37317, "ĠFinger": 37318, "assemble": 37319, "Ġpear": 37320, "Ġdroite": 37321, "ĠEverywhere": 37322, "tam": 37323, "оÑĤив": 37324, "вой": 37325, "ordinate": 37326, "ĠLak": 37327, "ĠmỼi": 37328, "ĠTelevision": 37329, "Ġexponentially": 37330, "avas": 37331, "Ġblev": 37332, "ĠMT": 37333, "俺": 37334, "Connell": 37335, "ĠêµŃ민": 37336, "ĠÑģвоим": 37337, "Ġacha": 37338, "ĠDynasty": 37339, "Jin": 37340, "Ġtore": 37341, "Ġflor": 37342, "Ġмногие": 37343, "æ²Ĵäºĭ": 37344, "owan": 37345, "bah": 37346, "Ġì£Ħ": 37347, "ĠCela": 37348, "Ġìµľê·¼": 37349, "Ġpermettre": 37350, "Ġabras": 37351, "Ġverstehen": 37352, "Ġescort": 37353, "ĠThem": 37354, "ärke": 37355, "porter": 37356, "Ġkahkaha": 37357, "Ġhect": 37358, "Ġdau": 37359, "wah": 37360, "olve": 37361, "ĠAges": 37362, "schaft": 37363, "ĠStell": 37364, "nelle": 37365, "ĠEnsuite": 37366, "ĠÐĴÑģем": 37367, "Ġcréd": 37368, "ĠPP": 37369, "lords": 37370, "grunting": 37371, "Ġcontraction": 37372, "Got": 37373, "Ġacquiring": 37374, "Ġsopr": 37375, "Ġpoisonous": 37376, "RNA": 37377, "Ġanar": 37378, "ĠHof": 37379, "')": 37380, "Ġremarkably": 37381, "Ġinternacional": 37382, "ücke": 37383, "inqu": 37384, "Ġduy": 37385, "Ġbeasts": 37386, "ĠLAN": 37387, "Ġprecedent": 37388, "ĠRPM": 37389, "åij¨": 37390, "Ġselon": 37391, "Ġmorte": 37392, "Ġcomeçou": 37393, "Ñıла": 37394, "Ġinterpreting": 37395, "ĠBurke": 37396, "ÑĤÑĢа": 37397, "ĠìĿ´ëŁ¬": 37398, "Ġpessim": 37399, "ĠNok": 37400, "íĮĿ": 37401, "Female": 37402, "Ġìĭ¤í": 37403, "ĻĢ": 37404, "Ġstimulation": 37405, "Ġslick": 37406, "Ġê°ĢëĬĶ": 37407, "Ġказ": 37408, "ĠHBO": 37409, "Ġpapier": 37410, "Ġkönnten": 37411, "Ñĥбли": 37412, "ĠConstant": 37413, "SPEAKING": 37414, "ĠktórÄħ": 37415, "Ġcosmetics": 37416, "ĠTrend": 37417, "Ġrobbery": 37418, "Ġtitt": 37419, "Ġgjort": 37420, "Ġdietary": 37421, "łĮ": 37422, "ĠKirby": 37423, "ĠпÑĢимеÑĢно": 37424, "Ġqualification": 37425, "Ġìķī": 37426, "Ġcabinets": 37427, "Ġhttp": 37428, "ĠErica": 37429, "義": 37430, "Ġdisadvantages": 37431, "Ġchattering": 37432, "yz": 37433, "feit": 37434, "Ġguild": 37435, "ĠETF": 37436, "ĠDragons": 37437, "ĠHERE": 37438, "venth": 37439, "ÙĦاÙħ": 37440, "Ġmarché": 37441, "Dam": 37442, "Ġphoton": 37443, "Ġestable": 37444, "Mag": 37445, "Ġolhar": 37446, "Ġcoupling": 37447, "ĠHilfe": 37448, "ĠWizard": 37449, "Ġмало": 37450, "help": 37451, "ĠlÃŃnea": 37452, "Ġì«": 37453, "Ġstandalone": 37454, "Ġmorale": 37455, "Ġzweite": 37456, "ãĤĪãĤįãģĹãģı": 37457, "ährt": 37458, "Ġdotted": 37459, "Ġdripping": 37460, "ĠFlag": 37461, "éĿĴ": 37462, "rocket": 37463, "rategy": 37464, "irim": 37465, "Ġíķĺë©´ìĦľ": 37466, "Ġsogenan": 37467, "ĠUno": 37468, "ĠSchutz": 37469, "Ġestilo": 37470, "ĠSubs": 37471, "ĠDaisy": 37472, "ÐĿеÑĤ": 37473, "'...": 37474, "Ġplatinum": 37475, "Ġbirl": 37476, "ĠSovi": 37477, "Ġviolate": 37478, "ÑĥеÑĤÑģÑı": 37479, "rill": 37480, "Ġtraz": 37481, "Ġsnip": 37482, "Ġcumpl": 37483, "à¸Ńà¸ģ": 37484, "Ġcuk": 37485, "éħĴ": 37486, "ĠParlament": 37487, "Ġhypert": 37488, "Ġpulp": 37489, "Ġtongues": 37490, "atto": 37491, "Ġbusca": 37492, "ihn": 37493, "ERO": 37494, "ĠÙĬع": 37495, "Ġvarias": 37496, "ĠMarian": 37497, "Ġbounded": 37498, "Ġpitching": 37499, "Ġdeficiency": 37500, "ĠBlessed": 37501, "ĠExerc": 37502, "uchs": 37503, "ĠnhÆ°ng": 37504, "æľ¬å½ĵ": 37505, "Ġraped": 37506, "hales": 37507, "Ġmala": 37508, "pic": 37509, "Ġ401": 37510, "ÅĽniej": 37511, "arina": 37512, "ëĵ¤ìĿĦ": 37513, "otti": 37514, "Ġдолго": 37515, "Ġtracker": 37516, "ĠShelby": 37517, "Ġvanished": 37518, "Ġbakery": 37519, "Kapı": 37520, "Jesus": 37521, "ĠKR": 37522, "JO": 37523, "ħ¸": 37524, "Ġdiscs": 37525, "ìĦ¯": 37526, "ì§Ģë": 37527, "×Ļצ": 37528, "emary": 37529, "Kendra": 37530, "Ġyük": 37531, "ückt": 37532, "Ġvaz": 37533, "Ġkup": 37534, "aktu": 37535, "ĠÑģпаÑģибо": 37536, "Ġaik": 37537, "Ġnursery": 37538, "Ġendangered": 37539, "êmement": 37540, "ematics": 37541, "Ġresponders": 37542, "ĠRepresentatives": 37543, "Ġsculptures": 37544, "igkeiten": 37545, "Ġdepl": 37546, "Ġinterpretations": 37547, "Ġdeadlines": 37548, "Ġ1942": 37549, "ÃĹ": 37550, "Ġsugars": 37551, "emu": 37552, "lively": 37553, "Ġrecreational": 37554, "Ġdistort": 37555, "Ġunderscore": 37556, "Ġunquote": 37557, "Ġsafest": 37558, "Ġswollen": 37559, "Ġanalyses": 37560, "Ġcommencé": 37561, "妹": 37562, "andin": 37563, "ĠХоÑĢоÑĪо": 37564, "Ġdiarr": 37565, "ãģ¾ãģģ": 37566, "ziest": 37567, "Ġtoothbrush": 37568, "éł»éģĵ": 37569, "uations": 37570, "Ġcade": 37571, "Ġbacklash": 37572, "hind": 37573, "Ġrisque": 37574, "zess": 37575, "ĠìĿ´ìķ¼ê¸°": 37576, "Ġesperar": 37577, "Ġtranslations": 37578, "ioned": 37579, "groans": 37580, "ĠпÑĥÑĤ": 37581, "Ġgenetically": 37582, "éĢł": 37583, "Ġhappiest": 37584, "Ġwerk": 37585, "atoon": 37586, "Ġmusi": 37587, "Ġfunção": 37588, "ĠìŀħëĭĪëĭ¤": 37589, "ĠÑĢай": 37590, "Ġbevor": 37591, "BLANK": 37592, "Ġrepentance": 37593, "Put": 37594, "Ġpotrzeb": 37595, "Ġsala": 37596, "Ġcampa": 37597, "WER": 37598, "ĠdecÃŃa": 37599, "Ġsécurité": 37600, "ĠAppreciate": 37601, "Ñĩи": 37602, "ĠRandom": 37603, "ë³Ħ": 37604, "kah": 37605, "Ġmöj": 37606, "Ġsäger": 37607, "Ġ×Ļ׼×ķ׾": 37608, "Ġ190": 37609, "xtures": 37610, "Eu": 37611, "Ġgä": 37612, "Ġ×ijת": 37613, "ĠCroat": 37614, "apo": 37615, "PLE": 37616, "Ġpersistence": 37617, "åĬ©": 37618, "Ġblends": 37619, "Ġtreffen": 37620, "ĠSantiago": 37621, "ydia": 37622, "aldo": 37623, "ĠTensorFlow": 37624, "ĠDual": 37625, "ãĥľ": 37626, "Ġchiff": 37627, "ìĹ´": 37628, "Ġcontracted": 37629, "Ġsegreg": 37630, "ĠFairy": 37631, "Ġwisely": 37632, "Ġvulnerabilities": 37633, "Ġhandheld": 37634, "Ġgadgets": 37635, "ĠboÅŁ": 37636, "ĠPopular": 37637, "Ġcurvature": 37638, "문": 37639, "ĠMARY": 37640, "ìĿ´ìĬ": 37641, "Ġformulation": 37642, "Ġcelery": 37643, "Ġblurry": 37644, "ĠTS": 37645, "alez": 37646, "Ġws": 37647, "Ġprogramm": 37648, "ĠStack": 37649, "ĠJIM": 37650, "овали": 37651, "ıll": 37652, "Ġpère": 37653, "ĠKanye": 37654, "ĠDelaware": 37655, "Ġãģł": 37656, "Ġdaunting": 37657, "ĠбеÑģ": 37658, "ĠStupid": 37659, "big": 37660, "fficial": 37661, "Ġprecipitation": 37662, "Ġplung": 37663, "ục": 37664, "burse": 37665, "Ġdarle": 37666, "Ġcripp": 37667, "Ġpioneer": 37668, "Ġdisput": 37669, "Ġsean": 37670, "ãģĵãĤĵãģª": 37671, "Ġresistor": 37672, "Ġallein": 37673, "ipples": 37674, "arel": 37675, "Ġendors": 37676, "zust": 37677, "ĠÑĢебÑıÑĤа": 37678, "eded": 37679, "Ġì¹´ë©Ķë": 37680, "Ġlleva": 37681, "Ġkennt": 37682, "Ġбал": 37683, "ĠDocument": 37684, "ĠKnights": 37685, "Ġbuckle": 37686, "Ġìī¬": 37687, "Ġalk": 37688, "ĠEveryday": 37689, "atters": 37690, "Ġtoilets": 37691, "Ġjugar": 37692, "ĠìŀĪì§Ģ": 37693, "Ġgenauso": 37694, "ĠLandesregierung": 37695, "ãģ£ãģ±": 37696, "ije": 37697, "Ġtrailers": 37698, "ĠTigers": 37699, "Ġgitti": 37700, "Ġforgiving": 37701, "Ġconcurrent": 37702, "ĠVu": 37703, "ĠíĬ¹íŀĪ": 37704, "ĠBROWN": 37705, "ounded": 37706, "\";": 37707, "Ġtremb": 37708, "Ġtiet": 37709, "ĠÑĢежим": 37710, "Ġnutshell": 37711, "елиÑĩ": 37712, "Ġlosers": 37713, "ricting": 37714, "Ġredeem": 37715, "defined": 37716, "Nice": 37717, "Ġbroadband": 37718, "KO": 37719, "Ġteasing": 37720, "Ġpartisan": 37721, "ıma": 37722, "Ġìŀ¬ë¯¸": 37723, "ĠJourney": 37724, "Ġslopes": 37725, "uning": 37726, "grunts": 37727, "Ġtäll": 37728, "Ġuncovered": 37729, "ĠmyÅĽlÄĻ": 37730, "ĠEsther": 37731, "äºİ": 37732, "ĠHealthy": 37733, "Ġë°ij": 37734, "rée": 37735, "Ġpolarization": 37736, "Ġflav": 37737, "Ġcambiar": 37738, "Ġyr": 37739, "ĠRanch": 37740, "Ġsplits": 37741, "Ġtrouvé": 37742, "åľĭ家": 37743, "Ġrecorder": 37744, "Ġdépart": 37745, "ÙĪب": 37746, "ĠKry": 37747, "Ġinteressant": 37748, "Ġederim": 37749, "ÅĽwiad": 37750, "ilateral": 37751, "wright": 37752, "Ġpourra": 37753, "êter": 37754, "Ġcamel": 37755, "áŀ": 37756, "Ġrapidement": 37757, "Ġmej": 37758, "Ġstiffness": 37759, "ADAS": 37760, "Ġdiffers": 37761, "Ġalot": 37762, "ĠSig": 37763, "ÑıÑĤелÑĮ": 37764, "Ġabstraction": 37765, "åľĺ": 37766, "Ġkeiner": 37767, "grupp": 37768, "ĠSherlock": 37769, "íĺĶ": 37770, "Ġcite": 37771, "Ġoverflow": 37772, "Ġtại": 37773, "úcar": 37774, "bula": 37775, "Ġconjunto": 37776, "ĠCI": 37777, "Ġmoderator": 37778, "Ġindirectly": 37779, "Ġalleine": 37780, "âĤ": 37781, "ÑĪиб": 37782, "Ġбаб": 37783, "Ġdanach": 37784, "Ġ1939": 37785, "Ġpromet": 37786, "Ġdestinations": 37787, "ĠIllust": 37788, "ικÏĮ": 37789, "Ġsabes": 37790, "Ġheh": 37791, "ĠGesetzent": 37792, "ĠMiz": 37793, "енко": 37794, "ĠMys": 37795, "Ь": 37796, "ĠJudaism": 37797, "Ġmustache": 37798, "Ġstimmt": 37799, "ĠGaza": 37800, "Ġvolte": 37801, "Ġnuo": 37802, "Ġmón": 37803, "ĠComput": 37804, "ูà¹Ī": 37805, "ĠRadi": 37806, "Ġexceptionally": 37807, "Ġassumes": 37808, "éĸĭå¿ĥ": 37809, "ãģĪãģ°": 37810, "inform": 37811, "Ġshrine": 37812, "æĵĬ": 37813, "Ġimplication": 37814, "ĠFitz": 37815, "æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "éĢ±": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æѦ": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, "record": 38500, "Ġrecherche": 38501, "atisfied": 38502, "Ġpozi": 38503, "Ġcelebrations": 38504, "isexual": 38505, "ĠROB": 38506, "thirds": 38507, "ĠFortune": 38508, "ĠÑĤой": 38509, "Ġbranded": 38510, "loo": 38511, "Ġdud": 38512, "Ġrandomized": 38513, "Ġcombin": 38514, "ä¸ĢäºĽ": 38515, "ieran": 38516, "czenia": 38517, "įãĥ«": 38518, "Ġcurator": 38519, "Ġartery": 38520, "ĠÑĥÑĪ": 38521, "ĠÑĩиÑĤ": 38522, "Ġsubsidies": 38523, "Ġblossom": 38524, "ĠTwilight": 38525, "Ġhyvä": 38526, "ĠPompe": 38527, "ĠCisco": 38528, "ĠÐŁÑĢо": 38529, "Ġbiri": 38530, "Ġgern": 38531, "Ġrebuilt": 38532, "Ġwcze": 38533, "Ġbenefici": 38534, "Ġdrummer": 38535, "Ġsolids": 38536, "Ġdiyorsun": 38537, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ": 38538, "lated": 38539, "Ġmuddy": 38540, "Ġholog": 38541, "Ġclaps": 38542, "ĠRings": 38543, "ĠOkey": 38544, "ĠBrave": 38545, "Ġvaluation": 38546, "Ġmigrant": 38547, "Ġintermitt": 38548, "Ġeigene": 38549, "iliary": 38550, "ãĥ¼ãĥĪ": 38551, "markt": 38552, "kr": 38553, "ĠRib": 38554, "á»Ļi": 38555, "Ġaccusations": 38556, "Ġarab": 38557, "wash": 38558, "ĠBardzo": 38559, "Ġugh": 38560, "esters": 38561, "ophren": 38562, "Ġalimentos": 38563, "ĠUz": 38564, "ÖĤ": 38565, "Ġ650": 38566, "ĠпÑĢиеÑħ": 38567, "FI": 38568, "Ġsampai": 38569, "Ġparlé": 38570, "hesion": 38571, "Ġsır": 38572, "Ġapparatus": 38573, "Ġcorrelated": 38574, "ĠPrincipal": 38575, "Ġcorr": 38576, "ĠOfficial": 38577, "иÑĩеÑģкие": 38578, "Ġterminals": 38579, "Should": 38580, "Ġvacun": 38581, "Ġstellt": 38582, "Ġmooi": 38583, "etzung": 38584, "ĠкÑĢа": 38585, "Ġdai": 38586, "Ġпож": 38587, "Team": 38588, "ĠPPE": 38589, "ĠÐŀÑģ": 38590, "ĠLeah": 38591, "ĠIvy": 38592, "yst": 38593, "Ġuhhh": 38594, "Ġnighttime": 38595, "Ġtrendy": 38596, "Ġsecurities": 38597, "Ġcontinents": 38598, "Ġfirsthand": 38599, "ĠVeron": 38600, "ĠëĤ®": 38601, "Ġbrowsing": 38602, "ĠCada": 38603, "tro": 38604, "Ġtramp": 38605, "reib": 38606, "Ġerstmal": 38607, "irler": 38608, "Ġpsic": 38609, "Ġgetir": 38610, "ĠNP": 38611, "Ġdzieci": 38612, "обÑĢаз": 38613, "Ġmagician": 38614, "Ġscrutiny": 38615, "Ġslab": 38616, "ĠOT": 38617, "isty": 38618, "iries": 38619, "orest": 38620, "Ġtasked": 38621, "Ġmorally": 38622, "ìķ¼ì§Ģ": 38623, "ustered": 38624, "Ġfools": 38625, "Ġirrespons": 38626, "Ġeinf": 38627, "Ġviá»ĩc": 38628, "Ġscor": 38629, "Ġpillows": 38630, "ĠGegen": 38631, "Ġtutte": 38632, "Ġquarterly": 38633, "Ġdidnt": 38634, "ĠGym": 38635, "ĠEther": 38636, "ĠØ«": 38637, "лиÑĪком": 38638, "Ġsignaling": 38639, "ĠNode": 38640, "ĠDoncs": 38641, "Ġyah": 38642, "ĠKanal": 38643, "Ġfading": 38644, "etin": 38645, "Ġinfluencers": 38646, "Ġmedals": 38647, "Ġengineered": 38648, "Ġfermented": 38649, "ê²łì§Ģë§Į": 38650, "ĠBeethoven": 38651, "×ŀש": 38652, "inental": 38653, "ĠìķĮ볤": 38654, "ütfen": 38655, "alnya": 38656, "Ġovere": 38657, "Ġdenkt": 38658, "акÑĤеÑĢ": 38659, "Ġâĺ": 38660, "Ġnecesit": 38661, "Ġgenerators": 38662, "grass": 38663, "ĠподÑĥм": 38664, "lieÃŁen": 38665, "Bar": 38666, "ľëıĻ": 38667, "ĠдеÑĤей": 38668, "Ġsucking": 38669, "Ġstencil": 38670, "Ġprimo": 38671, "ĠBreath": 38672, "strom": 38673, "Ġimmensely": 38674, "Ġappreh": 38675, "ìłķìĿ´": 38676, "Pop": 38677, "Ġjong": 38678, "ĠGiul": 38679, "ĠADHD": 38680, "Ġhören": 38681, "Ġelo": 38682, "ivent": 38683, "Ġrus": 38684, "Ġoutrageous": 38685, "Ġmastered": 38686, "Ġ커": 38687, "ÙĪÙģ": 38688, "ipes": 38689, "ĠRudy": 38690, "Jacob": 38691, "Ġbullish": 38692, "Ġtapped": 38693, "Ġfaud": 38694, "izophren": 38695, "ĠÑģоÑħ": 38696, "ĠDarling": 38697, "Ġ1963": 38698, "ĠPrevention": 38699, "²Ķ": 38700, "Ġabdominal": 38701, "stones": 38702, "Ġavaient": 38703, "á»ķi": 38704, "make": 38705, "Ġsare": 38706, "ĠInstant": 38707, "кам": 38708, "Ġkeeper": 38709, "Ġblankets": 38710, "ãģ§ãģĹãĤĩãģĨ": 38711, "Ġsweats": 38712, "ĠMinneapolis": 38713, "åħ¨éĥ¨": 38714, "Ġgenommen": 38715, "Ġfasten": 38716, "ĠBrussels": 38717, "åij¼": 38718, "Ġcafeter": 38719, "Ġabsorbing": 38720, "Ġhago": 38721, "ĠElmo": 38722, "Ġgusto": 38723, "ĠYap": 38724, "Música": 38725, "Ġtert": 38726, "Ġbanda": 38727, "Ġmily": 38728, "Ġthereafter": 38729, "ĠStockholm": 38730, "ĠCarson": 38731, "Ġcalibration": 38732, "avaÅŁ": 38733, "ansa": 38734, "ikke": 38735, "Ġforesee": 38736, "Ġqualche": 38737, "Ġdeste": 38738, "æ¤": 38739, "ünüz": 38740, "Ġforge": 38741, "Dis": 38742, "esten": 38743, "Ġδια": 38744, "Ġencaps": 38745, "ĠGespr": 38746, "Ġchercher": 38747, "ickets": 38748, "ÑĤоÑĢÑĭ": 38749, "Cr": 38750, "ĠТакже": 38751, "Ġrabbits": 38752, "ĠDot": 38753, "heiten": 38754, "Ġcausal": 38755, "ĠFoster": 38756, "ajÄħc": 38757, "Ġbereit": 38758, "Ġayudar": 38759, "é«Ļ": 38760, "ãģ³": 38761, "song": 38762, "comb": 38763, "Ġfringe": 38764, "Ġcybersecurity": 38765, "Ġ뾨": 38766, "Ġkier": 38767, "Ġbeschäft": 38768, "ĠконÑĨе": 38769, "Ġfacilit": 38770, "ĠNamen": 38771, "Ġbilateral": 38772, "tx": 38773, "ĠWissenschaft": 38774, "Ġnuances": 38775, "Ġripping": 38776, "Ġfy": 38777, "ĠSicherheit": 38778, "ĠGhana": 38779, "olon": 38780, "Ġtopped": 38781, "ĠMorocco": 38782, "Ġradial": 38783, "ĠLEE": 38784, "ĠAndreas": 38785, "edd": 38786, "ĠìĹ´ë": 38787, "ĠAirlines": 38788, "ãģĵãĤį": 38789, "Ġvalores": 38790, "ê·ľ": 38791, "Hy": 38792, "ĠзадаÑĩ": 38793, "ĠKendall": 38794, "ĠÑħаÑĢ": 38795, "ĠVamp": 38796, "Ġpython": 38797, "Ġmanageable": 38798, "ĠGente": 38799, "oise": 38800, "iciary": 38801, "Ġimposs": 38802, "ĠBunny": 38803, "iesta": 38804, "Andrew": 38805, "Ġsert": 38806, "ĠCec": 38807, "zzarella": 38808, "Ġautomobile": 38809, "ĠTiere": 38810, "allows": 38811, "åĨĨ": 38812, "Ġë°Ģ": 38813, "ĠScorp": 38814, "ĠJelly": 38815, "agara": 38816, "ĠStretch": 38817, "Ġredef": 38818, "Ġexacerb": 38819, "ĠSHA": 38820, "éf": 38821, "orsa": 38822, "Ġflawed": 38823, "ĠNoel": 38824, "?!?": 38825, "Ġprocent": 38826, "Ġmenstru": 38827, "ĠпÑĢоÑĩ": 38828, "Ġinfants": 38829, "ðŁİµ": 38830, "pause": 38831, "ĠRacing": 38832, "Ġ1948": 38833, "Ġsuperintendent": 38834, "idores": 38835, "idy": 38836, "brahim": 38837, "Ġunlucky": 38838, "Ġperk": 38839, "anci": 38840, "Ġë§ĮëĤĺ": 38841, "ĠÐľÐ¾Ñģкв": 38842, "Ġfinans": 38843, "Ġdiferencia": 38844, "łĪìĿ´": 38845, "éħį": 38846, "ORY": 38847, "ĠTac": 38848, "ÛĮا": 38849, "Ġdesem": 38850, "Ġважно": 38851, "ĠJU": 38852, "ĠìŀĪìŀĸìķĦìļĶ": 38853, "ĠÎĿ": 38854, "Ġinformations": 38855, "ĠHEL": 38856, "hst": 38857, "ĠпоговоÑĢ": 38858, "Ġvoiture": 38859, "Ġreus": 38860, "ändig": 38861, "ĠпоÑħож": 38862, "jing": 38863, "Ġdru": 38864, "altra": 38865, "Ġproduits": 38866, "Ġkite": 38867, "Ġeyeball": 38868, "ĠBelt": 38869, "ĠRestaurant": 38870, "Ġgamb": 38871, "Ġporridge": 38872, "itters": 38873, "Ġconverts": 38874, "Ġyardım": 38875, "Ġmáximo": 38876, "wirtschaft": 38877, "ĠíķĺëĤĺë": 38878, "Ġì¤Ģ": 38879, "Ġiceberg": 38880, "Ġvorbei": 38881, "Ġ256": 38882, "ocratic": 38883, "Ġreckless": 38884, "onner": 38885, "Ġmús": 38886, "Ġlogically": 38887, "ĠPrison": 38888, "ĠNetz": 38889, "Ġvacant": 38890, "Ġnimmt": 38891, "ĠHARR": 38892, "Ġзов": 38893, "ĠDee": 38894, "ringe": 38895, "niest": 38896, "ĠRules": 38897, "ìĬ¤ëŁ½": 38898, "cussions": 38899, "Ġfloral": 38900, "Ġconstrained": 38901, "Ġdifferentiation": 38902, "ĠQuebec": 38903, "ĠÛģÛĮÚº": 38904, "Ġpública": 38905, "itel": 38906, "Ġaccommodations": 38907, "ĠGrü": 38908, "íľ": 38909, "Ġpickles": 38910, "иÑĩеÑģкиÑħ": 38911, "Ġcommissions": 38912, "ĠBaek": 38913, "ĠçocuÄŁ": 38914, "ĠMedium": 38915, "Ġperiodically": 38916, "Ġwonderfully": 38917, "Ġstaffing": 38918, "ìĽIJë": 38919, "rire": 38920, "fle": 38921, "ĠMcL": 38922, "ĠÑĤеп": 38923, "ĠпеÑĢек": 38924, "нолог": 38925, "Ġíģ¬ê²Į": 38926, "çĻ¼çı¾": 38927, "Ġprosperous": 38928, "ĠSpiritual": 38929, "ĠChick": 38930, "DIA": 38931, "ĠÐŁÑĢивеÑĤ": 38932, "ĠperÃŃ": 38933, "ÑĮÑİÑĤ": 38934, "Ġconsultants": 38935, "ĠEarl": 38936, "ä»Ĭå¹´": 38937, "Ġruining": 38938, "оÑĢе": 38939, "Ġpenser": 38940, "Ġtakiej": 38941, "Ġstrengthened": 38942, "ĠLiquid": 38943, "онеÑĨ": 38944, "аваÑĤÑĮ": 38945, "Ġcamer": 38946, "Ġdisagreement": 38947, "Ġbathing": 38948, "ĠYosh": 38949, "aal": 38950, "prechen": 38951, "RISADAS": 38952, "Ġsuperstar": 38953, "æģŃ": 38954, "лÑıÑĤÑĮ": 38955, "Ġnib": 38956, "ĠTherm": 38957, "ĠDANIEL": 38958, "Ġpaw": 38959, "Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 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39050, "akat": 39051, "Ġtrench": 39052, "Ġproceeded": 39053, "ĠCoin": 39054, "Ġderecho": 39055, "ĠRede": 39056, "æ¯Ľ": 39057, "аннÑĭй": 39058, "Ġincarcerated": 39059, "ĠRichmond": 39060, "Rock": 39061, "ĠPav": 39062, "ĠKarma": 39063, "uges": 39064, "Ġconteú": 39065, "ë¹Ħ": 39066, "Ġê·¸ë§Į": 39067, "ĠGone": 39068, "ĠwspóÅĤ": 39069, "ĠRahmen": 39070, "unken": 39071, "Ġì¤ijìļĶíķľ": 39072, "Ġib": 39073, "Ġattaching": 39074, "Hay": 39075, "Ġsuka": 39076, "ìį¹": 39077, "Ġpivotal": 39078, "ĠRespect": 39079, "ÃŃda": 39080, "IB": 39081, "ĠVerantwort": 39082, "wiet": 39083, "Ġforensic": 39084, "ÑĢиÑģÑĤ": 39085, "ĠпÑĢинÑĨипе": 39086, "Ġmarkings": 39087, "Ġkettle": 39088, "ĠOpera": 39089, "ĠDoctors": 39090, "Ġshredded": 39091, "Ġrecuer": 39092, "Ġvigil": 39093, "ĠFail": 39094, "Ġentrev": 39095, "ĠдÑĥÑĪ": 39096, "Ġoutbreaks": 39097, "èµ°åIJ§": 39098, "ĠÏĢο": 39099, "Ġrogue": 39100, "angled": 39101, "Ġyearly": 39102, "ĠCreed": 39103, "Ġwam": 39104, "Ġlotus": 39105, "ê³¼ë": 39106, "ãĢģãĢģ": 39107, 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39225, "Ġacompa": 39226, "Ġidé": 39227, "Ġboiler": 39228, "ĠThemen": 39229, "Ġavenue": 39230, "ĠMud": 39231, "ĠновÑĭе": 39232, "Ġwitnessing": 39233, "Ġlance": 39234, "ĠCHAN": 39235, "ĠBever": 39236, "تÙħ": 39237, "Ġchemotherapy": 39238, "King": 39239, "ĠbÄĻdÄĻ": 39240, "Ġatual": 39241, "Ġtive": 39242, "Ġtalkin": 39243, "Ġquedar": 39244, "ieÃŁ": 39245, "edel": 39246, "Ġìĸ´ìłľ": 39247, "Ġjogar": 39248, "Ġör": 39249, "Ġundertaking": 39250, "ĠStrength": 39251, "Ġmilhões": 39252, "ĠWine": 39253, "ĠMolt": 39254, "讲": 39255, "ãģijãĤĮ": 39256, "Ġundermine": 39257, "ĠArchives": 39258, "vana": 39259, "mercial": 39260, "MC": 39261, "Ġcaste": 39262, "пÑĢ": 39263, "Ġlegislators": 39264, "ulators": 39265, "ênio": 39266, "Ġëį°ë": 39267, "ĠÑħоÑĤиÑĤе": 39268, "Ġнек": 39269, "Ġsurn": 39270, "Ġconsci": 39271, "ĠPOW": 39272, "Ġculinary": 39273, "ĠKAT": 39274, "ĠFolks": 39275, "Ñĭваем": 39276, "Ġвок": 39277, "ãģijãĤĭ": 39278, "service": 39279, "pts": 39280, "Ġпобед": 39281, "æĺ¯åķĬ": 39282, "Ġtents": 39283, "Ġnord": 39284, "STE": 39285, "Ġrepublican": 39286, "Ġwyk": 39287, "Ġminions": 39288, "èĻķ": 39289, "Ġmemang": 39290, "jest": 39291, "Ġcomparative": 39292, "Ġtyle": 39293, "carbon": 39294, "bedingt": 39295, "ksen": 39296, "Ġnegativity": 39297, "Ġsjälv": 39298, "Ġdú": 39299, "æīĢæľī": 39300, "Ġrecalled": 39301, "cra": 39302, "ĠTada": 39303, "ĠÑĢÑĥки": 39304, "ĠопÑĢедел": 39305, "Ġprocrast": 39306, "Ġjogos": 39307, "ĠOo": 39308, "ĠHearts": 39309, "Ġéch": 39310, "ĠksiÄħż": 39311, "Ġcoarse": 39312, "ĠTube": 39313, "ĠGreens": 39314, "Ġén": 39315, "Ġdumbbell": 39316, "ĠÑĤи": 39317, "Ġquerer": 39318, "اØŃ": 39319, "Ïĥει": 39320, "ĠпÑĢавилÑĮно": 39321, "Ġпап": 39322, "Ġcompra": 39323, "Ġtér": 39324, "ĠAntes": 39325, "Ġoptimum": 39326, "Ġbiscuit": 39327, "κι": 39328, "aczego": 39329, "Ġìĭľê°ĦìĿ´": 39330, "ĠMarines": 39331, "vero": 39332, "Ġvaccinations": 39333, "Ġpetty": 39334, "riters": 39335, "Ġал": 39336, "country": 39337, "Ġcounters": 39338, "Ġattendant": 39339, "ĠHui": 39340, "ãģ¨ãģĦãģĨãģĵãģ¨ãģ§": 39341, "cka": 39342, "ÑģÑĤвеннÑĭй": 39343, "guy": 39344, "Ġtricked": 39345, "ĠRED": 39346, "Ġthrilling": 39347, "ÏĢοι": 39348, "Ġpiggy": 39349, "Ġanunci": 39350, "ORTER": 39351, "ĠValue": 39352, "Ġrond": 39353, "ĠADA": 39354, "Ġposer": 39355, "hores": 39356, "ĠRoland": 39357, "ĵ¯": 39358, "Ġnoir": 39359, "Ġש×IJ×": 39360, "ë°ľ": 39361, "iemand": 39362, "ĠпоÑĤеÑĢ": 39363, "ê³³": 39364, "Ġê±±": 39365, "Ġformatting": 39366, "ĠLed": 39367, "è§Ģçľ¾": 39368, "Ġkillers": 39369, "ĠÄijấy": 39370, "Ġhaar": 39371, "again": 39372, "!>[": 45687, "minster": 45688, "Ġвли": 45689, "Ġidentifier": 45690, "ĠLambda": 45691, "Ġtros": 45692, "Ġflawless": 45693, "Ġdetrimental": 45694, "Ġbunları": 45695, "War": 45696, "Ġregião": 45697, "羣çļĦæĺ¯": 45698, "ĠBike": 45699, "cessors": 45700, "Ġcùng": 45701, "ĠRN": 45702, "Ġê½ĥ": 45703, "Ġküçük": 45704, "ĠBeginning": 45705, "íĺ¸ë": 45706, "Ġgewe": 45707, "Ġdenote": 45708, "ĠAlberto": 45709, "Ġprobiot": 45710, "Ġode": 45711, "Ġmolar": 45712, 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45768, "éĻ³": 45769, "Ġretiring": 45770, "Ġvampires": 45771, "Ġmerde": 45772, "ĠChanging": 45773, "Ġtame": 45774, "Ġspokesperson": 45775, "Ġcay": 45776, "Ġflirting": 45777, "ĠGrö": 45778, "Ġwär": 45779, "Ġwyb": 45780, "Ġcoeur": 45781, "ạnh": 45782, "ĠìĻĢìĦľ": 45783, "Ġconnais": 45784, "ĠHundreds": 45785, "ĠBea": 45786, "ĠαÏĢ": 45787, "pruch": 45788, "Ġsociedade": 45789, "ĠWhilst": 45790, "ĠKait": 45791, "espace": 45792, "Ġchia": 45793, "ĠErm": 45794, "Ġë°Ķê¿": 45795, "Ġfences": 45796, "ĠMortal": 45797, "ê²ģ": 45798, "ĠгÑĢаÑĦ": 45799, "ĠHomeland": 45800, "ĠJUN": 45801, "isst": 45802, "Ġparlar": 45803, "Ġsporty": 45804, "éo": 45805, "Ġdeepen": 45806, "ĠBehavior": 45807, "éĢı": 45808, "åĵĪåĵĪåĵĪ": 45809, "Ġerrand": 45810, "Ġrotary": 45811, "ĠWellington": 45812, "Wind": 45813, "Ġmesela": 45814, "ảng": 45815, "iende": 45816, "Ġexcell": 45817, "ĠGenius": 45818, "ĠEduardo": 45819, "æľī人": 45820, "ĠÅŁunu": 45821, "ĠÄ°stanbul": 45822, "Ġproduto": 45823, "Ġãħİãħİ": 45824, "OFF": 45825, "Ġwollt": 45826, "çĪĨ": 45827, "Ġëī´ìĬ¤": 45828, "Ġlass": 45829, "Ġhertz": 45830, "Ġaromatic": 45831, "Ġзвон": 45832, "Ġautoc": 45833, "ĠLust": 45834, "Ġ112": 45835, "ĠÎĹ": 45836, "Ġreviewers": 45837, "Ġreceptive": 45838, "å°įäºĨ": 45839, "ând": 45840, "oglo": 45841, "ĠìķĦëĭĻ": 45842, "Ġngo": 45843, "ÑĸÑĤи": 45844, "Ã¥t": 45845, "cono": 45846, "Ġtekrar": 45847, "Ġì£¼ê³ł": 45848, "ĠgelmiÅŁ": 45849, "Ġbedtime": 45850, "ĠArgh": 45851, "ADA": 45852, "ĠгоÑĢода": 45853, "ĠÄĩ": 45854, "Ġalliances": 45855, "giggling": 45856, "Ġyerde": 45857, "Ġspies": 45858, "Ġgutes": 45859, "çi": 45860, "Ġalltid": 45861, "ĠLah": 45862, "ŀIJë": 45863, "ĠdokÅĤad": 45864, "ÙĪÙĬ": 45865, "Ġtoxicity": 45866, "Ġcancellation": 45867, "Ġ1958": 45868, "dro": 45869, "ĠìŀijìĿĢ": 45870, "ĠMotorola": 45871, "Ġmultin": 45872, "Ġenthusiasts": 45873, "ĠMighty": 45874, "ĠCoconut": 45875, ":ãĢĮ": 45876, "ĠPictures": 45877, "Ġsangre": 45878, "Ġblinking": 45879, "olesome": 45880, "ĠìĬ¤íĥĢìĿ¼": 45881, "FP": 45882, "Ġbooming": 45883, "ĠдеÑģÑıÑĤ": 45884, "Ġratchet": 45885, "Ġtimelines": 45886, "leness": 45887, "Ġcages": 45888, "ĠGoodnight": 45889, "ometimes": 45890, "Ġcunning": 45891, "ĠRisk": 45892, "uled": 45893, "dade": 45894, "Ġprata": 45895, "ĠgustarÃŃa": 45896, "amus": 45897, "ĠJinping": 45898, "Ġestrut": 45899, "Ġdescobrir": 45900, "ĠMÄģ": 45901, "ĠAllan": 45902, "ĠåĪĨ": 45903, "Ġ׾ק": 45904, "Ġpreserv": 45905, "ĠStrawberry": 45906, "Äı": 45907, "Lu": 45908, "Ġkro": 45909, "ĠReports": 45910, "ìħĶìķ¼": 45911, "Ġvalt": 45912, "Ġpouvait": 45913, "Ġappar": 45914, "ĠBone": 45915, "Ġpreferably": 45916, "ĠRepública": 45917, "å°±åĪ°": 45918, "Ġherzlich": 45919, "Ġchimney": 45920, "Ġçev": 45921, "Ġvisas": 45922, "Ġverr": 45923, "Ġcultivation": 45924, "ĠArmenia": 45925, "ĠвдÑĢÑĥг": 45926, "Ġcockro": 45927, "retched": 45928, "artz": 45929, "ĠлÑİдÑıм": 45930, "ĠpolÃŃticas": 45931, "ĠPanz": 45932, "ĠAKA": 45933, "ĠëĪĮ룬": 45934, "Ġerro": 45935, "Ġcamper": 45936, "Ġ102": 45937, "स": 45938, "done": 45939, "Ġhoard": 45940, "ĠÐŁÐ¾ÑĤом": 45941, "jeong": 45942, "Ġdesta": 45943, "pak": 45944, "Ġinim": 45945, "Ġgrowers": 45946, "ĠMessage": 45947, "Ġelector": 45948, "engage": 45949, "ĠForbes": 45950, "ĠCincinnati": 45951, "Ġdifférence": 45952, "df": 45953, "Ġspar": 45954, "Ġawaits": 45955, "ĠUSSR": 45956, "ĠRising": 45957, "ĠHoÅŁ": 45958, "Ġfooting": 45959, "Ġcondiciones": 45960, "ÑĤоÑĢов": 45961, "Ġclinician": 45962, "ĠDiskuss": 45963, "å£ĵ": 45964, "ר×Ĵ": 45965, "×¥": 45966, "iteit": 45967, "gren": 45968, "Ġcharisma": 45969, "Ġleuke": 45970, "Ġirritating": 45971, "Ġcirca": 45972, "ĠRhodes": 45973, "Ġpior": 45974, "Ġhandicap": 45975, "royable": 45976, "Ġvull": 45977, "OG": 45978, "ĠinÃŃcio": 45979, "ieri": 45980, "Ġsplashing": 45981, "Ġdemise": 45982, "Ġassistir": 45983, "ÑĩÑĤо": 45984, "Ġcovert": 45985, "ĠGud": 45986, "à¸ī": 45987, "klär": 45988, "ĠìŀIJ꾸": 45989, "Ġverändert": 45990, "ĠREM": 45991, "ĠConven": 45992, "atge": 45993, "Ġpierwsze": 45994, "Ġclergy": 45995, "lington": 45996, "liv": 45997, "VPN": 45998, "ĠÑģожал": 45999, "ĠHate": 46000, "ãģ¨ãģĵãĤį": 46001, "ÏĨο": 46002, "ĠRespons": 46003, "озд": 46004, "Ġetmek": 46005, "Ġchemin": 46006, "ÙħØ©": 46007, "Ġê°Ģ족": 46008, "Tre": 46009, "Ġumas": 46010, "ĠBurton": 46011, "Ġpatriarch": 46012, "ĠSmithsonian": 46013, "¥ĺ": 46014, "Moon": 46015, "Air": 46016, "Ġmedios": 46017, "Ġeraser": 46018, "Ġwollten": 46019, "Ġpareil": 46020, "ĠBillie": 46021, "æĬ½": 46022, "еÑĢÑĤв": 46023, "Ġparlament": 46024, "Ġagony": 46025, "ĠQUE": 46026, "sequently": 46027, "Another": 46028, "ĠWhew": 46029, "ĠAnnual": 46030, "Ġseben": 46031, "ìĥģìĿĦ": 46032, "values": 46033, "ŀľë§Į": 46034, "Ġsinon": 46035, "ereal": 46036, "ĠEnlight": 46037, "ĠChemistry": 46038, "ĠCatalunya": 46039, "Ġdoctr": 46040, "anton": 46041, "Ġstuk": 46042, "ĠPlate": 46043, "ĠKardashian": 46044, "Ġfilos": 46045, "ĠWet": 46046, "ĠпопÑĭÑĤ": 46047, "Ġunknowns": 46048, "ĠSchon": 46049, "ĠBaldwin": 46050, "Ġtelescopes": 46051, "ĠGucci": 46052, "oxide": 46053, "ĠConservative": 46054, "ìĦ±ìĿĦ": 46055, "Ġhinaus": 46056, "Power": 46057, "Ġê±´ê°ķ": 46058, "Ġprevail": 46059, "orman": 46060, "machine": 46061, "Ġ1946": 46062, "Ġunbel": 46063, "Ġschaut": 46064, "Ġpiel": 46065, "eenth": 46066, "Ġobjectively": 46067, "Ġchakra": 46068, "audio": 46069, "Ġchicos": 46070, "ĠVault": 46071, "å°Ī": 46072, "Ġmedicinal": 46073, "ĠTail": 46074, "While": 46075, "Ġasphalt": 46076, "Ġfroze": 46077, "ĠEK": 46078, "unching": 46079, "nosis": 46080, "2015": 46081, "ĠGri": 46082, "Ġoddly": 46083, "ĠMär": 46084, "ĠAeg": 46085, "colo": 46086, "Par": 46087, "Ġëĵ¤ìĸ´ë": 46088, "Ġvinden": 46089, "ĠOVER": 46090, "Ġiced": 46091, "Ġscorp": 46092, "Ġhac": 46093, "qualified": 46094, "ĠÑĥвидеÑĤÑĮ": 46095, "ermo": 46096, "HEN": 46097, "Ġsoi": 46098, "Ġmultiples": 46099, "Ġlayouts": 46100, "Ġblindness": 46101, "ĠBowser": 46102, "ĠподÑĤ": 46103, "ĠÃİ": 46104, "ventional": 46105, "Ġmata": 46106, "madı": 46107, "Ġgeez": 46108, "Ġcadence": 46109, "Ġważne": 46110, "ĠChristie": 46111, "venge": 46112, "Call": 46113, 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46170, "Ġréussi": 46171, "Ġhypertension": 46172, "ĠManual": 46173, "Ġprophetic": 46174, "Ġhandc": 46175, "ÑĮе": 46176, "Ġrefrain": 46177, "ĠSquid": 46178, "ìŀ¡": 46179, "Ġкоман": 46180, "ällen": 46181, "Ġllegó": 46182, "Ġbash": 46183, "iony": 46184, "ĠÑģклад": 46185, "Ġкаб": 46186, "Ġcareless": 46187, "ĠPool": 46188, "Ġtrás": 46189, "Ġfils": 46190, "ĠSchr": 46191, "Ġsprawd": 46192, "ĠMonaten": 46193, "Ġunforgettable": 46194, "ĠCotton": 46195, "Ġinconvenient": 46196, "ĠRX": 46197, "oris": 46198, "Ġhumbled": 46199, "ת×Ĺ": 46200, "Ġآپ": 46201, "ĠincreÃŃ": 46202, "ĠKommentare": 46203, "èĪĴ": 46204, "ración": 46205, "Ġvantage": 46206, "ĠSeal": 46207, "ĠìĿ´ê±°ë¥¼": 46208, "Ġjoue": 46209, "ãģĿãģĨãģ§ãģĻãģŃ": 46210, "Ġìĺ¤ëŀĺ": 46211, "ĠиÑģпÑĭÑĤ": 46212, "oben": 46213, "Ġgrate": 46214, "Ġcontrole": 46215, "ĠPercy": 46216, "ÅĤada": 46217, "Ġsimultaneous": 46218, "Ġprototy": 46219, "ĠgroÃŁer": 46220, "Ġbewusst": 46221, "inizi": 46222, "Ġpassieren": 46223, "ĠHappiness": 46224, "åīĩ": 46225, "shi": 46226, "geht": 46227, "Ġstationed": 46228, "ĠErgebnis": 46229, "Ġdirectamente": 46230, "Ġsurvives": 46231, "Ġpersones": 46232, "BERG": 46233, "Ġvomiting": 46234, "Ġconhecer": 46235, "Ġadjour": 46236, "ĠCivic": 46237, "pei": 46238, "burst": 46239, "Ġëĭ¤ëĭĪ": 46240, "éı": 46241, "Ġsled": 46242, "Ġplataforma": 46243, "ĠSect": 46244, "ĠDefin": 46245, "çĻ»éĮ²": 46246, "énom": 46247, "chnet": 46248, "Ġprofitability": 46249, "Ġerreicht": 46250, "á»ıi": 46251, "cation": 46252, "Ġì§Ģê¸": 46253, "Ġperdre": 46254, "Ġfelony": 46255, "Ġ1957": 46256, "æĪijå¾Ī": 46257, "Ġunsuccessful": 46258, "Ġnagyon": 46259, "Ġelasticity": 46260, "Ġfacade": 46261, "Ġearthly": 46262, "ĠамеÑĢикан": 46263, "Ġconn": 46264, "cla": 46265, "Du": 46266, "Ġpolitiques": 46267, "Ġhalo": 46268, "iantes": 46269, "Ġмоей": 46270, "ãĥ³ãĥī": 46271, "tones": 46272, "elier": 46273, "è®ļ": 46274, "htaking": 46275, "Ġwichtige": 46276, "Ġanno": 46277, "ĠLok": 46278, "illions": 46279, "Ġviver": 46280, "Ġsolchen": 46281, "Ġsuf": 46282, "ĠSalz": 46283, "ĠNvidia": 46284, "zuge": 46285, "ĠSpike": 46286, "Video": 46287, "Ġtwor": 46288, "ĠAla": 46289, "èijī": 46290, "Ġhanya": 46291, "ĠAdm": 46292, "ìĿµ": 46293, "ĠPatienten": 46294, "ĠOnion": 46295, "ĠKobe": 46296, "ĠScene": 46297, "ĠRash": 46298, "æ¨Ļ": 46299, "ÑĢаÑģÑĤ": 46300, "istani": 46301, "General": 46302, "leye": 46303, "imbap": 46304, "Ġconcealed": 46305, "ĠFridays": 46306, "ĠWool": 46307, "ĠновÑĭÑħ": 46308, "شر": 46309, "Ġê²°ê³¼": 46310, "Ġjedoch": 46311, "´ìĭľ": 46312, "ĵ¤ëıĦ": 46313, "Ġìŀ¥ëĤľ": 46314, "ukt": 46315, "Lou": 46316, "Ġ먹ìĸ´": 46317, "ĠExpect": 46318, "Ġдомой": 46319, "Ġirresponsible": 46320, "Ġacerca": 46321, "ĠZust": 46322, "ר×ĺ": 46323, "UI": 46324, "Ġyoutubers": 46325, "ĠPositive": 46326, "Ġsocioe": 46327, "Ġsnatch": 46328, "èĥĮ": 46329, "Ġrefreshed": 46330, "Ġnominations": 46331, "ĠPatt": 46332, "Ġobsolete": 46333, "ĠdemiÅŁ": 46334, "åı¤": 46335, "ormuÅŁ": 46336, "ĠìĨĶì§ģíŀĪ": 46337, "Ġfla": 46338, "Ġcraziest": 46339, "ĠZie": 46340, "ĠTú": 46341, "zep": 46342, "icem": 46343, "Ġë©ĭìŀĪ": 46344, "Ġcynical": 46345, "ãģĿãĤĵãģª": 46346, "Ġtresp": 46347, "Ġcraz": 46348, "Õ¥Õ": 46349, "Ġnelle": 46350, "Ġmph": 46351, "ĠNered": 46352, "ĠKob": 46353, "ĠEck": 46354, "¨¸ëĭĪ": 46355, "Jan": 46356, "ĠТогда": 46357, "Ġdeci": 46358, "ĠVog": 46359, "Ġbubbling": 46360, "éĢĢ": 46361, "úa": 46362, "Ġproductos": 46363, "iberal": 46364, "Ġreplicated": 46365, "ĠImprove": 46366, "illary": 46367, "Cha": 46368, "Ġrédu": 46369, "ĥIJíķĺë©´": 46370, "Ġconnot": 46371, "ĠKrit": 46372, "ĠдÑĥÑħов": 46373, "Ġtreadmill": 46374, "ĠPW": 46375, "ĠзовÑĥÑĤ": 46376, "Ġclams": 46377, "Ġdrafting": 46378, "Ġ1956": 46379, "unta": 46380, "Ġexpenditures": 46381, "ĠHoover": 46382, "WOO": 46383, "ÑĪее": 46384, "Ġdeduction": 46385, "monary": 46386, "Ġrecib": 46387, "Ġpovo": 46388, "ĠëįĶë": 46389, "ĠPAL": 46390, "ĠBlow": 46391, "Ġwyp": 46392, "Ġdestac": 46393, "deal": 46394, "Graeme": 46395, "Ġnécessaire": 46396, "Ġdamned": 46397, "Ġ1938": 46398, "Ġìĭ¤ìłľë¡ľ": 46399, "Ġtroop": 46400, "Ġinsightful": 46401, "ĠTJ": 46402, "ĠоÑģв": 46403, "Ġfidelity": 46404, "ĠSkip": 46405, "ĠMayo": 46406, "ë§Ŀ": 46407, "appe": 46408, "Ġblas": 46409, "ĠWY": 46410, "ĠGN": 46411, "ctar": 46412, "Su": 46413, "Ġcuent": 46414, "hews": 46415, "Ġcorpses": 46416, "Abs": 46417, "Ġwastewater": 46418, "Ġciek": 46419, "ĠOnu": 46420, "Ġexplosives": 46421, "Ġarma": 46422, "ĠSTEPHAN": 46423, "politik": 46424, "ĠOsaka": 46425, "taÅĤ": 46426, "Ġyapıyor": 46427, "Ġizquier": 46428, "Ġbeleza": 46429, "ĠWyatt": 46430, "åIJ¸": 46431, "Ġsuk": 46432, "Ġspecjal": 46433, "Ġdanke": 46434, "whistle": 46435, "ĠfÃŃsica": 46436, "ĠHarriet": 46437, "ĠìķĦíĮĮ": 46438, "Ġwillkommen": 46439, "iping": 46440, "ĠÑģмоÑĤÑĢиÑĤе": 46441, "ĠможеÑĪÑĮ": 46442, "Ġinaccurate": 46443, "Ġarrogance": 46444, "ĠRemo": 46445, "γά": 46446, "assed": 46447, "Ġdeliveries": 46448, "Ġstinky": 46449, "ĠпеÑĢеж": 46450, "jay": 46451, "Ġtransitional": 46452, "Ġrere": 46453, "ĠNGOs": 46454, "ĠATM": 46455, "خت": 46456, "iology": 46457, "Ġвлад": 46458, "Ġschme": 46459, "ĠShine": 46460, "ìķ¡": 46461, "pants": 46462, "Ġserge": 46463, "Ġsenhor": 46464, "Ġabduct": 46465, "ĠBryant": 46466, "VES": 46467, "Ġawakened": 46468, "ĠLaz": 46469, "ropolis": 46470, "ĠLao": 46471, "è¾Ľèĭ¦": 46472, "Ġvilla": 46473, "Ġsummers": 46474, "Ġenthal": 46475, "Ġ1949": 46476, "Via": 46477, "Ġìĸ´ì¨": 46478, "Ġtendon": 46479, "Ġviolet": 46480, "Ġintellectually": 46481, "Ġbounced": 46482, "araus": 46483, "Ġ1919": 46484, "Ġvraag": 46485, "Ġspel": 46486, "ĠSchwar": 46487, "Scott": 46488, "ĠIndo": 46489, "Ġë§Ŀ": 46490, "Ġcanonical": 46491, "ĠIKE": 46492, "ĠthatÃŃs": 46493, "Ġmellan": 46494, "æ¯Ĵ": 46495, "igmat": 46496, "Could": 46497, "...?)": 46498, "Ġfoarte": 46499, "ĠKumar": 46500, "rendo": 46501, "Ġélé": 46502, "à´": 46503, "valuation": 46504, "cases": 46505, "Ġintuitively": 46506, "hong": 46507, "etted": 46508, "Ġsouven": 46509, "Ġmorb": 46510, "Ġcors": 46511, "ĠNV": 46512, "ĠHasan": 46513, "æĥħåĨµ": 46514, "ieved": 46515, "Ġì§Ģê¸ĪìĿĢ": 46516, "Ġdumpling": 46517, "Ġcontrôle": 46518, "Ġambiguity": 46519, "æ©Łæľĥ": 46520, "Ġcog": 46521, "ĠScriptures": 46522, "Ġcai": 46523, "Ġbever": 46524, "大家éĥ½": 46525, "Ġhuis": 46526, "Ġaime": 46527, "Ġerklären": 46528, "ĠLM": 46529, "ĠFey": 46530, "éļ¾": 46531, "றத": 46532, "Ġsupervised": 46533, "Ġjewe": 46534, "spl": 46535, "ĠÑĨенÑĤÑĢ": 46536, "Ġcollisions": 46537, "ÙĦÙģ": 46538, "ĠHogwarts": 46539, "ĠDurham": 46540, "×ķ×£": 46541, "Ġphosphate": 46542, "Ġoversee": 46543, "Ġinspections": 46544, "Ġbrinc": 46545, "ĠZak": 46546, "Ġpayoff": 46547, "Ġchaud": 46548, "ĠHunger": 46549, "ãos": 46550, "vir": 46551, "Ġfiance": 46552, "Ġboug": 46553, "lived": 46554, "cry": 46555, "åĽŀä¾Ĩ": 46556, "Ġjointly": 46557, "Ġgirlfriends": 46558, "ĠNexus": 46559, "¦¬ê²łìĬµëĭĪëĭ¤": 46560, "ĠKwang": 46561, "åĵĪåĽī": 46562, "å§ij": 46563, "ÅĤÄĻ": 46564, "ĠNeden": 46565, "iece": 46566, "Ġinserting": 46567, "æŁĵ": 46568, "ĠMummy": 46569, "ĠGlobe": 46570, "Ġlee": 46571, "Ġgerman": 46572, "Ġcreams": 46573, 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"Ġchuckling": 48167, "ĠMih": 48168, "Ġrallies": 48169, "Ġmanter": 48170, "Ġearnest": 48171, "super": 48172, "Ġgece": 48173, "ĠRend": 48174, "ĠGerade": 48175, "jenigen": 48176, "ĠVall": 48177, "ĠìŀĪëĤĺ": 48178, "ĠÑģказала": 48179, "Ġtrabalh": 48180, "ĠнаÑĪем": 48181, "ĠмеÑħ": 48182, "ikit": 48183, "Ġnouns": 48184, "Ġneurological": 48185, "Ġmotivational": 48186, "ĠMcMahon": 48187, "ĠFinished": 48188, "Ġë³´ìĿ´": 48189, "ĠFields": 48190, "Ġadolescents": 48191, "ĠTisch": 48192, "ĠNeben": 48193, "ĠFlowers": 48194, "ĠEnerg": 48195, "Ġdiret": 48196, "ĠThi": 48197, "ĠPicas": 48198, "æĥľ": 48199, "æĢİä¹Īæł·": 48200, "Ġavete": 48201, "ĠFors": 48202, "ĠChapel": 48203, "Não": 48204, "Et": 48205, "ĠÑģодеÑĢж": 48206, "reno": 48207, "Ġsven": 48208, "ĠdostÄĻp": 48209, "nee": 48210, "ĠSnapdragon": 48211, "ĠIDs": 48212, "ìķĺëĬĶëį°": 48213, "ר×ļ": 48214, "Ġsunflower": 48215, "Ġperpetual": 48216, "ç³ĸ": 48217, "Ġknights": 48218, "Ġgird": 48219, "ĠTold": 48220, "Ġvolcanoes": 48221, "Ġadversary": 48222, "ĠEconomy": 48223, "Ġextrapol": 48224, "Ġbluetooth": 48225, "Ġzooming": 48226, "Ġskys": 48227, "Ġgenial": 48228, "ÃŃculos": 48229, "ambre": 48230, "ĠмеÑĢ": 48231, "Ġteeny": 48232, "Ġstressing": 48233, "ìķĮ": 48234, "ONY": 48235, "Ġtranslucent": 48236, "Ġrounding": 48237, "Ġgrues": 48238, "×Ļ׳×Ķ": 48239, "après": 48240, "Ġprueba": 48241, "Ġpolygon": 48242, "Ġblueberry": 48243, "ĠProgramm": 48244, "Ġtrenches": 48245, "Ġsebagai": 48246, "Ġpalate": 48247, "Ġlaude": 48248, "Ġbehaved": 48249, "Ġlongitudinal": 48250, "ĠModule": 48251, "Ġadmir": 48252, "λι": 48253, "Greg": 48254, "Ġwyst": 48255, "Ġpropagate": 48256, "Ġmolds": 48257, "ĠTub": 48258, "ĠLoud": 48259, "usto": 48260, "Ġunstoppable": 48261, "Ġreinforcing": 48262, "éĿŀ常çļĦ": 48263, "ĠпÑĢоблема": 48264, "Ġpotencial": 48265, "Ġhemp": 48266, "ìŀĶ": 48267, "य": 48268, "Ġoptic": 48269, "Ġerfolgreich": 48270, "ÑģÑĭ": 48271, "олÑĮÑĪе": 48272, "urst": 48273, "ĠPois": 48274, "Ġrespondents": 48275, "Ġnehme": 48276, "ĠExternal": 48277, "olate": 48278, "Hyun": 48279, "Ġquartz": 48280, "Ġmathematician": 48281, "Ġbásicamente": 48282, "Ġail": 48283, "ìłľë¥¼": 48284, "attutto": 48285, "Ġnooit": 48286, "Ġafflict": 48287, "ĠOlga": 48288, "èŃ·": 48289, "ĠнаÑĤ": 48290, "Ġdites": 48291, "Ġrealidade": 48292, "Ġkän": 48293, "Ġuniqueness": 48294, "Ġpadres": 48295, "Ġsubsidi": 48296, "Ġpigeons": 48297, "βα": 48298, "stad": 48299, "Ġderen": 48300, "ĠСлед": 48301, "doo": 48302, "ĠопиÑģании": 48303, "Ġamber": 48304, "Ġgoosebumps": 48305, "ĠfrÃ¥gor": 48306, "ĠVital": 48307, "ĠIsraelites": 48308, "wasser": 48309, "Isn": 48310, "Ġcommits": 48311, "ĠSTEVEN": 48312, "ĠBevölker": 48313, "uitive": 48314, "Ġlegen": 48315, "Ġbruk": 48316, "иÑĢован": 48317, "ynen": 48318, "helm": 48319, "Ġgenerational": 48320, "ĠLändern": 48321, "οιÏĢÏĮν": 48322, "uzu": 48323, "Ġcaller": 48324, "онÑĮ": 48325, "ümü": 48326, "Ġbesar": 48327, "Ġplats": 48328, "Ġmigrated": 48329, "Ġjap": 48330, "ĠWAR": 48331, "Ġdissect": 48332, "ĠZusch": 48333, "ĠZeiten": 48334, "ĠLions": 48335, "ĠDF": 48336, "âĶ": 48337, "кив": 48338, "Ġpedestrians": 48339, "ĠMarilyn": 48340, "dock": 48341, "Ġyht": 48342, "Ġreincarn": 48343, "ĠSono": 48344, "ĠGrowth": 48345, "ÑĥÑģов": 48346, "Ġdungeons": 48347, "Ġbagus": 48348, "kich": 48349, "ĠÑĥкÑĢаÑĹ": 48350, "éĨ«": 48351, "ĠKeller": 48352, "chemistry": 48353, "Japanese": 48354, "Ġwillst": 48355, "Ġdecomposition": 48356, "ĠÑģÑĤен": 48357, "Ġrevived": 48358, "íķĻêµIJ": 48359, "ĠÅĵ": 48360, "ä½IJ": 48361, "ìĭ¸": 48362, "ippy": 48363, "Ġhourly": 48364, "jän": 48365, "ĠWorkshop": 48366, "Ŀ¼ìĦľ": 48367, "Ġcuarto": 48368, "Ġpatrim": 48369, "ĠBurch": 48370, "ĠìŀĪ기": 48371, "Ġhepat": 48372, "ĠhÃłng": 48373, "ĠëĮĢíķ´": 48374, "ĠваÑĪи": 48375, "Ġrework": 48376, "Ġparse": 48377, "Ġçıktı": 48378, "ĠSax": 48379, "ĠMongo": 48380, "ĠAaah": 48381, "ramble": 48382, "DJ": 48383, "Ġstabilized": 48384, "ĠSpeech": 48385, "Books": 48386, "Ġhurdles": 48387, "ĠWO": 48388, "ĠLamborg": 48389, "Ġ1933": 48390, "Ġvorbere": 48391, "Ġclinically": 48392, "Ġbreathtaking": 48393, "ĠGateway": 48394, "пеÑĢвÑĭÑħ": 48395, "uters": 48396, "Ġë¹µ": 48397, "Ġyeter": 48398, "Ġpulley": 48399, "Ġmuffin": 48400, "ĠPrefer": 48401, "ĠPence": 48402, "Ġinformação": 48403, "ìĬ¤íĬ¸ë": 48404, "ãĤ¸ãĥ£": 48405, "ĠTurtle": 48406, "ĠRegina": 48407, "ĠLoad": 48408, "does": 48409, "panze": 48410, "¸Ķ": 48411, "Ġmina": 48412, "ĠLatinos": 48413, "ammers": 48414, "ĠTort": 48415, "ĠBeyonce": 48416, "имоÑģÑĤи": 48417, "ĠвопÑĢоÑģÑĭ": 48418, "Ġbulun": 48419, "èĢĮå·²": 48420, "inek": 48421, "bereich": 48422, "Ġpasture": 48423, "ĠOA": 48424, "ĠMelt": 48425, "ĠEtt": 48426, "ĠDY": 48427, "Ġobwohl": 48428, "Ġleagues": 48429, "ÑĤеÑģÑĮ": 48430, "ĠкÑĥÑģ": 48431, "Ġvors": 48432, "Ġtopp": 48433, "ographical": 48434, "asst": 48435, "Ġlindo": 48436, "Ġë°ĿíĺĶ": 48437, "Ġréfl": 48438, "Ġclimbs": 48439, "Ġvarsa": 48440, "Ġmethyl": 48441, "ĠKarere": 48442, "Æ°á»Ł": 48443, "Rad": 48444, "Ġpreparedness": 48445, "онÑĩ": 48446, "ĠOD": 48447, "ĠCGI": 48448, "Ġम": 48449, "Ġspeechless": 48450, "Ġlasci": 48451, "Ġbolag": 48452, "ĠÑħоÑĩеÑĤÑģÑı": 48453, "Ġgrieving": 48454, "ĠJohannes": 48455, "ĠCarroll": 48456, "adaki": 48457, "Ī¬ë": 48458, "ĠsÅĤu": 48459, "Ġinnerhalb": 48460, "Ġgymnastics": 48461, "пÑĢи": 48462, "ifiques": 48463, "Ġkarate": 48464, "Ġdomu": 48465, "ãģĿãĤĮãģ§": 48466, "OTHER": 48467, "Ġdemandé": 48468, "Ġbooklet": 48469, "ĠKyoto": 48470, "Ġwoh": 48471, "ĠMarÃŃa": 48472, "violent": 48473, "JE": 48474, "Ġlóg": 48475, "Ġbrutally": 48476, "cot": 48477, "ĠÙħÛĮ": 48478, "ĠWarsz": 48479, "å®Ī": 48480, "wol": 48481, "Ġmikä": 48482, "ĠPronounce": 48483, "ĠBrendan": 48484, "Ġroup": 48485, "Ġitaliano": 48486, "å¦ĤæѤ": 48487, "ĠкомпÑĮÑİÑĤ": 48488, "Ġurging": 48489, "edes": 48490, "Ġcarbono": 48491, "ĠRichardson": 48492, "ĠÐĿаÑĩ": 48493, "ĠTrainer": 48494, "ĠCrimea": 48495, "Ġdiapers": 48496, "Ġcovet": 48497, "ĠMahar": 48498, "ĠHutch": 48499, "ĠAusw": 48500, "berty": 48501, "Ġindifferent": 48502, "кÑĢеÑĤ": 48503, "uldade": 48504, "Ġharms": 48505, "¢ÙĨ": 48506, "lesia": 48507, "Ġgio": 48508, "ĠMistress": 48509, "ĠKnox": 48510, "ĠFREE": 48511, "Ġ루ë": 48512, "ĠнаÑĪа": 48513, "Ġinvincible": 48514, "Ġmaiden": 48515, "ĠJeez": 48516, "Ġbreve": 48517, "pole": 48518, "Ġcriticisms": 48519, "ĠRusia": 48520, "म": 48521, "phin": 48522, "ĠCompare": 48523, "ĠBON": 48524, "Ġsneaking": 48525, "ĠRails": 48526, "ĠGeral": 48527, "Ġ1953": 48528, "Hola": 48529, "ĠопÑĭÑĤ": 48530, "Ġrainforest": 48531, "Ġbelum": 48532, "ĠObi": 48533, "ĠISS": 48534, "ãĤĮãģªãģĦ": 48535, "ĠСв": 48536, "Ġblond": 48537, "Ġwzgl": 48538, "ĠpowiedziaÅĤ": 48539, "Ġchoking": 48540, "ĠSongs": 48541, "ĠBiraz": 48542, "Ġyells": 48543, "Ġstylist": 48544, "ÏĮÏĦε": 48545, "Ġschreiben": 48546, "ĠJaw": 48547, "ĠEleven": 48548, "ĠRif": 48549, "/.": 48550, "Ġìĺ¤ëŀľë§Į": 48551, "Ġtreaties": 48552, "uffed": 48553, "ĠâĪĴ": 48554, "Ġroofs": 48555, "à¹Ģส": 48556, "Ġë»": 48557, "Ġsparkle": 48558, "ĠKiev": 48559, "ĠArgu": 48560, "erecht": 48561, "ĠÐĿадо": 48562, "ĠFIL": 48563, "Ġmolta": 48564, "ĠDevi": 48565, "Ġcampe": 48566, "Ġbenevol": 48567, "ĠTough": 48568, "Ġmoim": 48569, "Ġevacuate": 48570, "Ġerrado": 48571, "å©Ĩ": 48572, "ÑĢÑĥго": 48573, "Ġíİĺ": 48574, "ĠÎĵια": 48575, "Ġweaken": 48576, "Ġilluminated": 48577, "Ġsiglo": 48578, "ĠVacc": 48579, "ией": 48580, "alis": 48581, "ĠÑĥÑģÑĤÑĢой": 48582, "Ġdona": 48583, "ÅĤos": 48584, "üman": 48585, "Ġproducción": 48586, "Ġclot": 48587, "ĠMango": 48588, "Ġuneasy": 48589, "Ġshuts": 48590, "ĠExamples": 48591, "vell": 48592, "ebe": 48593, "Ġpromptly": 48594, "ĠTeles": 48595, "ĠпÑĢоÑĪл": 48596, "Ġpuerta": 48597, "Ġüberzeug": 48598, "Ġcoch": 48599, "social": 48600, "ĠBenson": 48601, "ĠMeth": 48602, "ĠExped": 48603, "Ġsupplemental": 48604, "Ġconceive": 48605, "Ġ×ĺ×ķ×ij": 48606, "Ġcaptivity": 48607, "ıĻìķĪ": 48608, "ĠÑħÑĥд": 48609, "forming": 48610, "Ġuploads": 48611, "Ġturbulence": 48612, "joint": 48613, "Ġsatisfactory": 48614, "ĠAnime": 48615, "Ġwashes": 48616, "Ġliberals": 48617, "ĠSunshine": 48618, "ĠREAL": 48619, "ublik": 48620, "binary": 48621, "Tony": 48622, "Ġpolarized": 48623, "Ġenriched": 48624, "taking": 48625, "ĠëģĿëĤĺ": 48626, "Ġpleasures": 48627, "Ġextermin": 48628, "inese": 48629, "atl": 48630, "vär": 48631, "аÑĢÑĭ": 48632, "ĠmyÅĽ": 48633, "narrator": 48634, "Ġодном": 48635, "ĠnajwiÄĻ": 48636, "Ġmobilize": 48637, "Ġmillor": 48638, "Ġata": 48639, "æ··": 48640, "ĠpolÃŃtico": 48641, "Ġplead": 48642, "Ġpainters": 48643, "ĠSow": 48644, "оÑĦ": 48645, "ĠìĺĽëĤł": 48646, "ĠÑĩÑĤоб": 48647, "Ġsabor": 48648, "ĠUndert": 48649, "ĠJERRY": 48650, "Å¡ÃŃ": 48651, "Ġë°ĸìĹIJ": 48652, "Ġprécéd": 48653, "Ġannotation": 48654, "ĠInaudible": 48655, "Ġtextured": 48656, "Ġfisherman": 48657, "vordan": 48658, "icherung": 48659, "ĠìłģìĿ´": 48660, "Ġgezeigt": 48661, "Ġmandates": 48662, "Ġbeak": 48663, "ĠTWO": 48664, "ĠAkbar": 48665, "ilian": 48666, "Ġtiếp": 48667, "Ġsuperiority": 48668, "inku": 48669, "Ġlys": 48670, "ĠFCC": 48671, "ĠCPA": 48672, "ustering": 48673, "nicos": 48674, "anja": 48675, "Ġchills": 48676, "ĠCage": 48677, "Ġsealing": 48678, "Ġsaç": 48679, "Ġdedans": 48680, "ĠAlger": 48681, "Ġspezie": 48682, "Ġcoloss": 48683, "ıyı": 48684, "clockwise": 48685, "Ġexactamente": 48686, "Ġiemand": 48687, "amı": 48688, "Ġmandar": 48689, "raj": 48690, "faced": 48691, "agua": 48692, "Ġê¹Ķë": 48693, "Ġinsbesondere": 48694, "Ġdrizzle": 48695, "Ġdiminish": 48696, "ĠYoda": 48697, "AI": 48698, "Ġbilmiyorum": 48699, "ĠMMA": 48700, "ategory": 48701, "ĠпеÑĢеп": 48702, "Ġparticipar": 48703, "Ġnormalized": 48704, "Ġcomplexities": 48705, "æ´²": 48706, "æݧ": 48707, "аÑĢов": 48708, "mist": 48709, "icha": 48710, "Group": 48711, "Ġresiliency": 48712, "Ġnogle": 48713, "ĠCNC": 48714, "prü": 48715, "Ġphysicists": 48716, "нок": 48717, "LI": 48718, "Ġstuffs": 48719, "Ġsistemas": 48720, "Ġinterfering": 48721, "ĠMarvin": 48722, "ército": 48723, "ĠìĹĨê³ł": 48724, "Ġsonic": 48725, "Ġequiv": 48726, "Ġabord": 48727, "ĠRamen": 48728, "Ġ09": 48729, "medim": 48730, "atiques": 48731, "ĠделаÑİÑĤ": 48732, "Ġunanimously": 48733, "Ġskirts": 48734, "ĠíĬ¹ë³Ħ": 48735, "ĠPrix": 48736, "kami": 48737, "Ġfruition": 48738, "Ġbirthdays": 48739, "иком": 48740, "Ġinaugural": 48741, "Ġcorrelate": 48742, "ĠTory": 48743, "ĠëĤĺìģ": 48744, "Ġdew": 48745, "ĠPrecis": 48746, "ihi": 48747, "Ġë¬¸ìłľê°Ģ": 48748, "Ġciting": 48749, "ĠLana": 48750, "ĠKag": 48751, "Ġplaythrough": 48752, "ĠProtocol": 48753, "frist": 48754, "hovah": 48755, "Ġmerciful": 48756, "Ġbilingual": 48757, "ĠGuitar": 48758, "rh": 48759, "Ġglamorous": 48760, "ĠVikings": 48761, "ĠOoooh": 48762, "íķĺëĬĶëį°": 48763, "ĠUganda": 48764, "Ġcollapses": 48765, "entry": 48766, "Ġantioxidants": 48767, "ëĤĺë": 48768, "ÑĪаÑı": 48769, "Ġtrivia": 48770, "Ġgäller": 48771, "Ġfungi": 48772, "Ġmilks": 48773, "Ġdicht": 48774, "μη": 48775, "poke": 48776, "ĠвÑĭпÑĥÑģк": 48777, "Ġfeeder": 48778, "ĠAlcohol": 48779, "hower": 48780, "Ġdeserving": 48781, "ĠRebel": 48782, "iosis": 48783, "Ġ103": 48784, "Ġhandout": 48785, "Ġenm": 48786, "Ġlandlords": 48787, "Ġgeology": 48788, "rils": 48789, "Ġcobra": 48790, "ĠVold": 48791, "ĠPanch": 48792, "ĠGREG": 48793, "Ġpross": 48794, "Ġbracelets": 48795, "ĠVega": 48796, "Ġrozum": 48797, "款": 48798, "азд": 48799, "ĠLynd": 48800, "ĠHonors": 48801, "Ġsurrendered": 48802, "Ġlibrarians": 48803, "125": 48804, "ĠÑģиг": 48805, "Ġuniformly": 48806, "ĠEagles": 48807, "ìķĻ": 48808, "иÑĤан": 48809, "andid": 48810, "ĠìłĪëĮĢ": 48811, "Ġض": 48812, "Ġarrests": 48813, "ĠCSV": 48814, "ĠAzerbaijan": 48815, "ortic": 48816, "ĠDX": 48817, "ĠAdventures": 48818, "Ġabus": 48819, "ĠFau": 48820, "Ġschlimm": 48821, "Ġrattling": 48822, "Ġconsumes": 48823, "ĠTolkien": 48824, "Ġresurrected": 48825, "ĠXY": 48826, "íĬ¸ê°Ģ": 48827, "ĠвÑĭÑģÑĤÑĥп": 48828, "ĠAngie": 48829, "żenia": 48830, "Mic": 48831, "ĠSheila": 48832, "achtet": 48833, "Ġoverst": 48834, "Ġlâ": 48835, "Ġineffective": 48836, "æĿ¡": 48837, "æĢİä¹ĪäºĨ": 48838, "å¿Ļ": 48839, "Ġwichtiger": 48840, "Ġvino": 48841, "Ġpum": 48842, "Ġangled": 48843, "ĠPione": 48844, "ĠMỹ": 48845, "ãģĿãĤĮãģ¯": 48846, "woÅĽÄĩ": 48847, "draw": 48848, "ัà¹Ī": 48849, "markets": 48850, "Ġcafes": 48851, "ĠCem": 48852, "âĿ¤": 48853, "ĠSuit": 48854, "MK": 48855, "Ġemphasizes": 48856, "Ġtortilla": 48857, "Ġmejorar": 48858, "ĠSurviv": 48859, "casting": 48860, "Ġeducación": 48861, "ĠGum": 48862, "uely": 48863, "ĠìĹ¬ê¸°ëĬĶ": 48864, "Ġstretchy": 48865, "ença": 48866, "Ġwithhold": 48867, "Ġexiting": 48868, "Ġenthalpy": 48869, "ĠTransit": 48870, "ılmÄ±ÅŁ": 48871, "alies": 48872, "Ġsalvar": 48873, "Ġleaned": 48874, "ĠgroÃŁes": 48875, "Ġfitt": 48876, "аки": 48877, "Sarah": 48878, "Ġhostel": 48879, "Ġfingerna": 48880, "ĠnadziejÄĻ": 48881, "wives": 48882, "Rec": 48883, "Ġspool": 48884, "аÑĤов": 48885, "ĠEnemy": 48886, "Ġfury": 48887, "Ġdetta": 48888, "ĠFay": 48889, "éļ¨": 48890, "ÑıÑİÑĤ": 48891, "Ġaproximadamente": 48892, "Ġsilos": 48893, "Ġmagist": 48894, "Ġcree": 48895, "ĠKrank": 48896, "ĠDOWN": 48897, "Ġstartled": 48898, "Ġreborn": 48899, "ĠUmwelt": 48900, "ĠSuzanne": 48901, "ниÑĨÑĭ": 48902, "outez": 48903, "ĠJAC": 48904, "yards": 48905, "radas": 48906, "rau": 48907, "ipts": 48908, "hail": 48909, "Ġparagraphs": 48910, "Ġmeglio": 48911, "Ġisolating": 48912, "Ġaceite": 48913, "ĠHarsh": 48914, "Ġcyst": 48915, "ĠBlockchain": 48916, "ĠÑħоÑĢоÑĪий": 48917, "Ġvirtuous": 48918, "Ġinvestigación": 48919, "Ġdevoir": 48920, "Ġmasturb": 48921, "ĠSale": 48922, "ÙĬرة": 48923, "ĠΧ": 48924, "ĠStraÃŁen": 48925, "Ġdikk": 48926, "Ġafore": 48927, "ĠJungkook": 48928, "Ġchociaż": 48929, "ĠDebatte": 48930, "Ġweirdly": 48931, "Ġviaje": 48932, "regist": 48933, "Help": 48934, "Ġkinderen": 48935, "Ġformulated": 48936, "Ġenfim": 48937, "ĠTowards": 48938, "коÑĹ": 48939, "ivering": 48940, "ĠдеÑĤи": 48941, "charger": 48942, "Ġpurl": 48943, "Ġacademically": 48944, "ĠNurse": 48945, "Ġdeleting": 48946, "ayo": 48947, "Ġrefusal": 48948, "Ġdepicts": 48949, "ĠDracula": 48950, "Ġtoasted": 48951, "ĠZombie": 48952, "ĠSuperior": 48953, "ĠBold": 48954, "Ġquizzes": 48955, "Ġgle": 48956, "450": 48957, "Ġcomeço": 48958, "ynn": 48959, "Ġverst": 48960, "ĠOlaf": 48961, "Ġpomoc": 48962, "ĠSask": 48963, "ëĺ": 48964, "ĠTCP": 48965, "ĠProperty": 48966, "íķĺì£ł": 48967, "à¸ľà¸¡": 48968, "boom": 48969, "aros": 48970, "ĠÑĢоÑģÑģий": 48971, "ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, "> length: + array = array.index_select(dim=axis, index=torch.arange(length)) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes]) + else: + if array.shape[axis] > length: + array = array.take(indices=range(length), axis=axis) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = np.pad(array, pad_widths) + + return array + + +@lru_cache(maxsize=None) +def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: + """ + load the mel filterbank matrix for projecting STFT into a Mel spectrogram. + Allows decoupling librosa dependency; saved using: + + np.savez_compressed( + "mel_filters.npz", + mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), + ) + """ + assert n_mels == 80, f"Unsupported n_mels: {n_mels}" + with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f: + return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) + + +def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS): + """ + Compute the log-Mel spectrogram of + + Parameters + ---------- + audio: Union[str, np.ndarray, torch.Tensor], shape = (*) + The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz + + n_mels: int + The number of Mel-frequency filters, only 80 is supported + + Returns + ------- + torch.Tensor, shape = (80, n_frames) + A Tensor that contains the Mel spectrogram + """ + if not torch.is_tensor(audio): + if isinstance(audio, str): + audio = load_audio(audio) + audio = torch.from_numpy(audio) + + window = torch.hann_window(N_FFT).to(audio.device) + stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) + + magnitudes = stft[:, :-1].abs() ** 2 + + filters = mel_filters(audio.device, n_mels) + mel_spec = filters @ magnitudes + + log_spec = torch.clamp(mel_spec, min=1e-10).log10() + log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) + log_spec = (log_spec + 4.0) / 4.0 + return log_spec diff --git a/src/models/whisper/whisper/decoding.py b/src/models/whisper/whisper/decoding.py new file mode 100644 index 0000000000000000000000000000000000000000..183505481e40a4f3779933e5fde8086d79e4cc39 --- /dev/null +++ b/src/models/whisper/whisper/decoding.py @@ -0,0 +1,729 @@ +from dataclasses import dataclass, field +from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch.distributions import Categorical + +from .audio import CHUNK_LENGTH +from .tokenizer import Tokenizer, get_tokenizer +from .utils import compression_ratio + +if TYPE_CHECKING: + from .model import Whisper + + +@torch.no_grad() +def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]: + """ + Detect the spoken language in the audio, and return them as list of strings, along with the ids + of the most probable language tokens and the probability distribution over all language tokens. + This is performed outside the main decode loop in order to not interfere with kv-caching. + + Returns + ------- + language_tokens : Tensor, shape = (n_audio,) + ids of the most probable language tokens, which appears after the startoftranscript token. + language_probs : List[Dict[str, float]], length = n_audio + list of dictionaries containing the probability distribution over all languages. + """ + if tokenizer is None: + tokenizer = get_tokenizer(model.is_multilingual) + if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence: + raise ValueError(f"This model doesn't have language tokens so it can't perform lang id") + + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + # skip encoder forward pass if already-encoded audio features were given + if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): + mel = model.encoder(mel) + + # forward pass using a single token, startoftranscript + n_audio = mel.shape[0] + x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1] + logits = model.logits(x, mel)[:, 0] + + # collect detected languages; suppress all non-language tokens + mask = torch.ones(logits.shape[-1], dtype=torch.bool) + mask[list(tokenizer.all_language_tokens)] = False + logits[:, mask] = -np.inf + language_tokens = logits.argmax(dim=-1) + language_token_probs = logits.softmax(dim=-1).cpu() + language_probs = [ + { + c: language_token_probs[i, j].item() + for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) + } + for i in range(n_audio) + ] + + if single: + language_tokens = language_tokens[0] + language_probs = language_probs[0] + + return language_tokens, language_probs + + +@dataclass(frozen=True) +class DecodingOptions: + task: str = "transcribe" # whether to perform X->X "transcribe" or X->English "translate" + language: Optional[str] = None # language that the audio is in; uses detected language if None + + # sampling-related options + temperature: float = 0.0 + sample_len: Optional[int] = None # maximum number of tokens to sample + best_of: Optional[int] = None # number of independent samples to collect, when t > 0 + beam_size: Optional[int] = None # number of beams in beam search, when t == 0 + patience: Optional[float] = None # patience in beam search (https://arxiv.org/abs/2204.05424) + + # options for ranking generations (either beams or best-of-N samples) + length_penalty: Optional[float] = None # "alpha" in Google NMT, None defaults to length norm + + # prompt, prefix, and token suppression + prompt: Optional[Union[str, List[int]]] = None # text or tokens for the previous context + prefix: Optional[Union[str, List[int]]] = None # text or tokens to prefix the current context + suppress_blank: bool = True # this will suppress blank outputs + + # list of tokens ids (or comma-separated token ids) to suppress + # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` + suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" + + # timestamp sampling options + without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only + max_initial_timestamp: Optional[float] = 1.0 # the initial timestamp cannot be later than this + + # implementation details + fp16: bool = True # use fp16 for most of the calculation + + +@dataclass(frozen=True) +class DecodingResult: + audio_features: Tensor + language: str + encoder_embeddings: np.ndarray + decoder_embeddings: np.ndarray + language_probs: Optional[Dict[str, float]] = None + tokens: List[int] = field(default_factory=list) + text: str = "" + avg_logprob: float = np.nan + no_speech_prob: float = np.nan + temperature: float = np.nan + compression_ratio: float = np.nan + + +class Inference: + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + """Perform a forward pass on the decoder and return per-token logits""" + raise NotImplementedError + + def rearrange_kv_cache(self, source_indices) -> None: + """Update the key-value cache according to the updated beams""" + raise NotImplementedError + + def cleanup_caching(self) -> None: + """Clean up any resources or hooks after decoding is finished""" + pass + + +class PyTorchInference(Inference): + def __init__(self, model: "Whisper", initial_token_length: int): + self.model: "Whisper" = model + self.initial_token_length = initial_token_length + self.kv_cache = {} + self.hooks = [] + + def logits(self, tokens: Tensor, audio_features: Tensor, include_embeddings=False) -> Tensor: + if not self.kv_cache: + self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() + + if tokens.shape[-1] > self.initial_token_length: + # only need to use the last token except in the first forward pass + tokens = tokens[:, -1:] + + return_val = self.model.decoder(tokens, audio_features, + kv_cache=self.kv_cache, include_embeddings=include_embeddings) + return return_val + + def cleanup_caching(self): + for hook in self.hooks: + hook.remove() + + self.kv_cache = {} + self.hooks = [] + + def rearrange_kv_cache(self, source_indices): + for module, tensor in self.kv_cache.items(): + # update the key/value cache to contain the selected sequences + self.kv_cache[module] = tensor[source_indices].detach() + + +class SequenceRanker: + def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]: + """ + Given a list of groups of samples and their cumulative log probabilities, + return the indices of the samples in each group to select as the final result + """ + raise NotImplementedError + + +class MaximumLikelihoodRanker(SequenceRanker): + """ + Select the sample with the highest log probabilities, penalized using either + a simple length normalization or Google NMT paper's length penalty + """ + + def __init__(self, length_penalty: Optional[float]): + self.length_penalty = length_penalty + + def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]): + def scores(logprobs, lengths): + result = [] + for logprob, length in zip(logprobs, lengths): + if self.length_penalty is None: + penalty = length + else: + # from the Google NMT paper + penalty = ((5 + length) / 6) ** self.length_penalty + result.append(logprob / penalty) + return result + + # get the sequence with the highest score + lengths = [[len(t) for t in s] for s in tokens] + return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)] + + +class TokenDecoder: + def reset(self): + """Initialize any stateful variables for decoding a new sequence""" + + def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + """Specify how to select the next token, based on the current trace and logits + + Parameters + ---------- + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + sum_logprobs : Tensor, shape = (n_batch) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Tensor, shape = (n_batch, current_sequence_length + 1) + the tokens, appended with the selected next token + + completed : bool + True if all sequences has reached the end of text + + """ + raise NotImplementedError + + def finalize( + self, tokens: Tensor, sum_logprobs: Tensor + ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]: + """Finalize search and return the final candidate sequences + + Parameters + ---------- + tokens : Tensor, shape = (n_audio, n_group, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence + + sum_logprobs : Tensor, shape = (n_audio, n_group) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Sequence[Sequence[Tensor]], length = n_audio + sequence of Tensors containing candidate token sequences, for each audio input + + sum_logprobs : List[List[float]], length = n_audio + sequence of cumulative log probabilities corresponding to the above + + """ + raise NotImplementedError + + +class GreedyDecoder(TokenDecoder): + def __init__(self, temperature: float, eot: int): + self.temperature = temperature + self.eot = eot + + def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + temperature = self.temperature + if temperature == 0: + next_tokens = logits.argmax(dim=-1) + else: + next_tokens = Categorical(logits=logits / temperature).sample() + + logprobs = F.log_softmax(logits.float(), dim=-1) + current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] + sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot) + + next_tokens[tokens[:, -1] == self.eot] = self.eot + tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1) + + completed = (tokens[:, -1] == self.eot).all() + return tokens, completed + + def finalize(self, tokens: Tensor, sum_logprobs: Tensor): + # make sure each sequence has at least one EOT token at the end + tokens = F.pad(tokens, (0, 1), value=self.eot) + return tokens, sum_logprobs.tolist() + + +class BeamSearchDecoder(TokenDecoder): + def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None): + self.beam_size = beam_size + self.eot = eot + self.inference = inference + self.patience = patience or 1.0 + self.max_candidates: int = round(beam_size * self.patience) + self.finished_sequences = None + + assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})" + + def reset(self): + self.finished_sequences = None + + def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + if tokens.shape[0] % self.beam_size != 0: + raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") + + n_audio = tokens.shape[0] // self.beam_size + if self.finished_sequences is None: # for the first update + self.finished_sequences = [{} for _ in range(n_audio)] + + logprobs = F.log_softmax(logits.float(), dim=-1) + next_tokens, source_indices, finished_sequences = [], [], [] + for i in range(n_audio): + scores, sources, finished = {}, {}, {} + + # STEP 1: calculate the cumulative log probabilities for possible candidates + for j in range(self.beam_size): + idx = i * self.beam_size + j + prefix = tokens[idx].tolist() + for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): + new_logprob = (sum_logprobs[idx] + logprob).item() + sequence = tuple(prefix + [token.item()]) + scores[sequence] = new_logprob + sources[sequence] = idx + + # STEP 2: rank the candidates and keep the top beam_size sequences for each audio + saved = 0 + for sequence in sorted(scores, key=scores.get, reverse=True): + if sequence[-1] == self.eot: + finished[sequence] = scores[sequence] + else: + sum_logprobs[len(next_tokens)] = scores[sequence] + next_tokens.append(sequence) + source_indices.append(sources[sequence]) + + saved += 1 + if saved == self.beam_size: + break + + finished_sequences.append(finished) + + tokens = torch.tensor(next_tokens, device=tokens.device) + self.inference.rearrange_kv_cache(source_indices) + + # add newly finished sequences to self.finished_sequences + assert len(self.finished_sequences) == len(finished_sequences) + for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences): + for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): + if len(previously_finished) >= self.max_candidates: + break # the candidate list is full + previously_finished[seq] = newly_finished[seq] + + # mark as completed if all audio has enough number of samples + completed = all( + len(sequences) >= self.max_candidates for sequences in self.finished_sequences + ) + return tokens, completed + + def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): + # collect all finished sequences, including patience, and add unfinished ones if not enough + sum_logprobs = sum_logprobs.cpu() + for i, sequences in enumerate(self.finished_sequences): + if len(sequences) < self.beam_size: # when not enough sequences are finished + for j in list(np.argsort(sum_logprobs[i]))[::-1]: + sequence = preceding_tokens[i, j].tolist() + [self.eot] + sequences[tuple(sequence)] = sum_logprobs[i][j].item() + if len(sequences) >= self.beam_size: + break + + tokens: List[List[Tensor]] = [ + [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences + ] + sum_logprobs: List[List[float]] = [ + list(sequences.values()) for sequences in self.finished_sequences + ] + return tokens, sum_logprobs + + +class LogitFilter: + def apply(self, logits: Tensor, tokens: Tensor) -> None: + """Apply any filtering or masking to logits in-place + + Parameters + ---------- + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + """ + raise NotImplementedError + + +class SuppressBlank(LogitFilter): + def __init__(self, tokenizer: Tokenizer, sample_begin: int): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + + def apply(self, logits: Tensor, tokens: Tensor): + if tokens.shape[1] == self.sample_begin: + logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf + + +class SuppressTokens(LogitFilter): + def __init__(self, suppress_tokens: Sequence[int]): + self.suppress_tokens = list(suppress_tokens) + + def apply(self, logits: Tensor, tokens: Tensor): + logits[:, self.suppress_tokens] = -np.inf + + +class ApplyTimestampRules(LogitFilter): + def __init__( + self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int] + ): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + self.max_initial_timestamp_index = max_initial_timestamp_index + + def apply(self, logits: Tensor, tokens: Tensor): + # suppress <|notimestamps|> which is handled by without_timestamps + if self.tokenizer.no_timestamps is not None: + logits[:, self.tokenizer.no_timestamps] = -np.inf + + # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly + for k in range(tokens.shape[0]): + seq = [t for t in tokens[k, self.sample_begin :].tolist()] + last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin + penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin + + if last_was_timestamp: + if penultimate_was_timestamp: # has to be non-timestamp + logits[k, self.tokenizer.timestamp_begin :] = -np.inf + else: # cannot be normal text tokens + logits[k, : self.tokenizer.eot] = -np.inf + + # apply the `max_initial_timestamp` option + if tokens.shape[1] == self.sample_begin and self.max_initial_timestamp_index is not None: + last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index + logits[:, last_allowed + 1 :] = -np.inf + + # if sum of probability over timestamps is above any other token, sample timestamp + logprobs = F.log_softmax(logits.float(), dim=-1) + for k in range(tokens.shape[0]): + timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1) + max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() + if timestamp_logprob > max_text_token_logprob: + logits[k, : self.tokenizer.timestamp_begin] = -np.inf + + +class DecodingTask: + inference: Inference + sequence_ranker: SequenceRanker + decoder: TokenDecoder + logit_filters: List[LogitFilter] + + def __init__(self, model: "Whisper", options: DecodingOptions): + self.model = model + + language = options.language or "en" + tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task) + self.tokenizer: Tokenizer = tokenizer + self.options: DecodingOptions = self._verify_options(options) + + self.n_group: int = options.beam_size or options.best_of or 1 + self.n_ctx: int = model.dims.n_text_ctx + self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2 + + self.sot_sequence: Tuple[int] = tokenizer.sot_sequence + if self.options.without_timestamps: + self.sot_sequence = tokenizer.sot_sequence_including_notimestamps + + self.initial_tokens: Tuple[int] = self._get_initial_tokens() + self.sample_begin: int = len(self.initial_tokens) + self.sot_index: int = self.initial_tokens.index(tokenizer.sot) + + # inference: implements the forward pass through the decoder, including kv caching + self.inference = PyTorchInference(model, len(self.initial_tokens)) + + # sequence ranker: implements how to rank a group of sampled sequences + self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty) + + # decoder: implements how to select the next tokens, given the autoregressive distribution + if options.beam_size is not None: + self.decoder = BeamSearchDecoder( + options.beam_size, tokenizer.eot, self.inference, options.patience + ) + else: + self.decoder = GreedyDecoder(options.temperature, tokenizer.eot) + + # logit filters: applies various rules to suppress or penalize certain tokens + self.logit_filters = [] + if self.options.suppress_blank: + self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) + if self.options.suppress_tokens: + self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) + if not options.without_timestamps: + precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds + max_initial_timestamp_index = None + if options.max_initial_timestamp: + max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision) + self.logit_filters.append( + ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index) + ) + + def _verify_options(self, options: DecodingOptions) -> DecodingOptions: + if options.beam_size is not None and options.best_of is not None: + raise ValueError("beam_size and best_of can't be given together") + if options.temperature == 0: + if options.best_of is not None: + raise ValueError("best_of with greedy sampling (T=0) is not compatible") + if options.patience is not None and options.beam_size is None: + raise ValueError("patience requires beam_size to be given") + if options.length_penalty is not None and not (0 <= options.length_penalty <= 1): + raise ValueError("length_penalty (alpha) should be a value between 0 and 1") + + return options + + def _get_initial_tokens(self) -> Tuple[int]: + tokens = list(self.sot_sequence) + prefix = self.options.prefix + prompt = self.options.prompt + + if prefix: + prefix_tokens = ( + self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix + ) + if self.sample_len is not None: + max_prefix_len = self.n_ctx // 2 - self.sample_len + prefix_tokens = prefix_tokens[-max_prefix_len:] + tokens = tokens + prefix_tokens + + if prompt: + prompt_tokens = ( + self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt + ) + tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens + + return tuple(tokens) + + def _get_suppress_tokens(self) -> Tuple[int]: + suppress_tokens = self.options.suppress_tokens + + if isinstance(suppress_tokens, str): + suppress_tokens = [int(t) for t in suppress_tokens.split(",")] + + if -1 in suppress_tokens: + suppress_tokens = [t for t in suppress_tokens if t >= 0] + suppress_tokens.extend(self.tokenizer.non_speech_tokens) + elif suppress_tokens is None or len(suppress_tokens) == 0: + suppress_tokens = [] # interpret empty string as an empty list + else: + assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" + + suppress_tokens.extend( + [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] + ) + if self.tokenizer.no_speech is not None: + # no-speech probability is collected separately + suppress_tokens.append(self.tokenizer.no_speech) + + return tuple(sorted(set(suppress_tokens))) + + def _get_audio_features(self, mel: Tensor, include_embeddings: bool = False): + if self.options.fp16: + mel = mel.half() + + if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state): + # encoded audio features are given; skip audio encoding + audio_features = mel + else: + result = self.model.encoder(mel, include_embeddings) + if include_embeddings: + audio_features, embeddings = result + else: + audio_features = result + + if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32): + return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}") + + if include_embeddings: + return audio_features, embeddings + else: + return audio_features + + def _detect_language(self, audio_features: Tensor, tokens: Tensor): + languages = [self.options.language] * audio_features.shape[0] + lang_probs = None + + if self.options.language is None or self.options.task == "lang_id": + lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer) + languages = [max(probs, key=probs.get) for probs in lang_probs] + if self.options.language is None: + tokens[:, self.sot_index + 1] = lang_tokens # write language tokens + + return languages, lang_probs + + def _main_loop(self, audio_features: Tensor, tokens: Tensor): + assert audio_features.shape[0] == tokens.shape[0] + n_batch = tokens.shape[0] + sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) + no_speech_probs = [np.nan] * n_batch + + try: + embeddings = [] + for i in range(self.sample_len): + logits, token_embeddings = self.inference.logits(tokens, audio_features, include_embeddings=True) + + if i == 0 and self.tokenizer.no_speech is not None: # save no_speech_probs + probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) + no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() + + # now we need to consider the logits at the last token only + logits = logits[:, -1] + token_embeddings = token_embeddings[:, :, -1] + + # Append embeddings together + embeddings.append(token_embeddings) + + # apply the logit filters, e.g. for suppressing or applying penalty to + for logit_filter in self.logit_filters: + logit_filter.apply(logits, tokens) + + # expand the tokens tensor with the selected next tokens + tokens, completed = self.decoder.update(tokens, logits, sum_logprobs) + + if completed or tokens.shape[-1] > self.n_ctx: + break + finally: + if completed: + embeddings = embeddings[:-1] + embeddings = np.stack(embeddings, 2) + self.inference.cleanup_caching() + + return tokens, sum_logprobs, no_speech_probs, embeddings + + @torch.no_grad() + def run(self, mel: Tensor) -> List[DecodingResult]: + self.decoder.reset() + tokenizer: Tokenizer = self.tokenizer + n_audio: int = mel.shape[0] + + # encoder forward pass + forward_pass: Tuple[Tensor, np.ndarray] = self._get_audio_features(mel, include_embeddings=True) + audio_features, encoder_embeddings = forward_pass + tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) + + # detect language if requested, overwriting the language token + languages, language_probs = self._detect_language(audio_features, tokens) + if self.options.task == "lang_id": + return [ + DecodingResult(audio_features=features, language=language, language_probs=probs) + for features, language, probs in zip(audio_features, languages, language_probs) + ] + + # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling + audio_features = audio_features.repeat_interleave(self.n_group, dim=0) + tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) + + # call the main sampling loop + tokens, sum_logprobs, no_speech_probs, decoder_embeddings = self._main_loop(audio_features, tokens) + + # reshape the tensors to have (n_audio, n_group) as the first two dimensions + audio_features = audio_features[:: self.n_group] + no_speech_probs = no_speech_probs[:: self.n_group] + assert audio_features.shape[0] == len(no_speech_probs) == n_audio + + tokens = tokens.reshape(n_audio, self.n_group, -1) + sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) + + # get the final candidates for each group, and slice between the first sampled token and EOT + tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) + tokens: List[List[Tensor]] = [ + [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens + ] + + # select the top-ranked sample in each group + selected = self.sequence_ranker.rank(tokens, sum_logprobs) + tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] + texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] + + sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] + avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)] + + fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs) + if len(set(map(len, fields))) != 1: + raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") + + return [ + DecodingResult( + audio_features=features, + language=language, + tokens=tokens, + text=text, + avg_logprob=avg_logprob, + no_speech_prob=no_speech_prob, + temperature=self.options.temperature, + compression_ratio=compression_ratio(text), + encoder_embeddings=encoder_embeddings, + decoder_embeddings=decoder_embeddings + ) + for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields) + ] + + +@torch.no_grad() +def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]: + """ + Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). + + Parameters + ---------- + model: Whisper + the Whisper model instance + + mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000) + A tensor containing the Mel spectrogram(s) + + options: DecodingOptions + A dataclass that contains all necessary options for decoding 30-second segments + + Returns + ------- + result: Union[DecodingResult, List[DecodingResult]] + The result(s) of decoding contained in `DecodingResult` dataclass instance(s) + """ + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + result = DecodingTask(model, options).run(mel) + + if single: + result = result[0] + + return result diff --git a/src/models/whisper/whisper/model.py b/src/models/whisper/whisper/model.py new file mode 100644 index 0000000000000000000000000000000000000000..acb6a0be88109a884b2c4d0e2d50daaf00870307 --- /dev/null +++ b/src/models/whisper/whisper/model.py @@ -0,0 +1,290 @@ +from dataclasses import dataclass +from typing import Dict +from typing import Iterable, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch import nn + +from .transcribe import transcribe as transcribe_function +from .decoding import detect_language as detect_language_function, decode as decode_function + + +@dataclass +class ModelDimensions: + n_mels: int + n_audio_ctx: int + n_audio_state: int + n_audio_head: int + n_audio_layer: int + n_vocab: int + n_text_ctx: int + n_text_state: int + n_text_head: int + n_text_layer: int + + +class LayerNorm(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + return super().forward(x.float()).type(x.dtype) + + +class Linear(nn.Linear): + def forward(self, x: Tensor) -> Tensor: + return F.linear( + x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype) + ) + + +class Conv1d(nn.Conv1d): + def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: + return super()._conv_forward( + x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) + ) + + +def sinusoids(length, channels, max_timescale=10000): + """Returns sinusoids for positional embedding""" + assert channels % 2 == 0 + log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) + scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, n_state: int, n_head: int): + super().__init__() + self.n_head = n_head + self.query = Linear(n_state, n_state) + self.key = Linear(n_state, n_state, bias=False) + self.value = Linear(n_state, n_state) + self.out = Linear(n_state, n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + q = self.query(x) + + if kv_cache is None or xa is None: + # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; + # otherwise, perform key/value projections for self- or cross-attention as usual. + k = self.key(x if xa is None else xa) + v = self.value(x if xa is None else xa) + else: + # for cross-attention, calculate keys and values once and reuse in subsequent calls. + k = kv_cache.get(self.key, self.key(xa)) + v = kv_cache.get(self.value, self.value(xa)) + + wv = self.qkv_attention(q, k, v, mask) + return self.out(wv) + + def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None): + n_batch, n_ctx, n_state = q.shape + scale = (n_state // self.n_head) ** -0.25 + q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale + k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale + v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) + + qk = q @ k + if mask is not None: + qk = qk + mask[:n_ctx, :n_ctx] + + w = F.softmax(qk.float(), dim=-1).to(q.dtype) + return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2) + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): + super().__init__() + + self.attn = MultiHeadAttention(n_state, n_head) + self.attn_ln = LayerNorm(n_state) + + self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None + self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None + + n_mlp = n_state * 4 + self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)) + self.mlp_ln = LayerNorm(n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache) + if self.cross_attn: + x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache) + x = x + self.mlp(self.mlp_ln(x)) + return x + + +class AudioEncoder(nn.Module): + def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): + super().__init__() + self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) + self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) + self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] + ) + self.ln_post = LayerNorm(n_state) + + def forward(self, x: Tensor, include_embeddings: bool = False): + """ + x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) + the mel spectrogram of the audio + include_embeddings: bool + whether to include intermediate steps in the output + """ + x = F.gelu(self.conv1(x)) + x = F.gelu(self.conv2(x)) + x = x.permute(0, 2, 1) + + assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape" + x = (x + self.positional_embedding).to(x.dtype) + + if include_embeddings: + embeddings = [x.cpu().detach().numpy()] + + for block in self.blocks: + x = block(x) + if include_embeddings: + embeddings.append(x.cpu().detach().numpy()) + + x = self.ln_post(x) + + if include_embeddings: + embeddings = np.stack(embeddings, axis=1) + return x, embeddings + else: + return x + + +class TextDecoder(nn.Module): + def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): + super().__init__() + + self.token_embedding = nn.Embedding(n_vocab, n_state) + self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)] + ) + self.ln = LayerNorm(n_state) + + mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1) + self.register_buffer("mask", mask, persistent=False) + + def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None, include_embeddings: bool = False): + """ + x : torch.LongTensor, shape = (batch_size, <= n_ctx) + the text tokens + xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx) + the encoded audio features to be attended on + include_embeddings : bool + Whether to include intermediate values in the output to this function + """ + offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 + x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]] + x = x.to(xa.dtype) + + if include_embeddings: + embeddings = [x.cpu().detach().numpy()] + + for block in self.blocks: + x = block(x, xa, mask=self.mask, kv_cache=kv_cache) + if include_embeddings: + embeddings.append(x.cpu().detach().numpy()) + + x = self.ln(x) + logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float() + + if include_embeddings: + embeddings = np.stack(embeddings, axis=1) + return logits, embeddings + else: + return logits + + +class Whisper(nn.Module): + def __init__(self, dims: ModelDimensions): + super().__init__() + self.dims = dims + self.encoder = AudioEncoder( + self.dims.n_mels, + self.dims.n_audio_ctx, + self.dims.n_audio_state, + self.dims.n_audio_head, + self.dims.n_audio_layer, + ) + self.decoder = TextDecoder( + self.dims.n_vocab, + self.dims.n_text_ctx, + self.dims.n_text_state, + self.dims.n_text_head, + self.dims.n_text_layer, + ) + + def embed_audio(self, mel: torch.Tensor): + return self.encoder.forward(mel) + + def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): + return self.decoder.forward(tokens, audio_features) + + def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]: + return self.decoder(tokens, self.encoder(mel)) + + @property + def device(self): + return next(self.parameters()).device + + @property + def is_multilingual(self): + return self.dims.n_vocab == 51865 + + def install_kv_cache_hooks(self, cache: Optional[dict] = None): + """ + The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value + tensors calculated for the previous positions. This method returns a dictionary that stores + all caches, and the necessary hooks for the key and value projection modules that save the + intermediate tensors to be reused during later calculations. + + Returns + ------- + cache : Dict[nn.Module, torch.Tensor] + A dictionary object mapping the key/value projection modules to its cache + hooks : List[RemovableHandle] + List of PyTorch RemovableHandle objects to stop the hooks to be called + """ + cache = {**cache} if cache is not None else {} + hooks = [] + + def save_to_cache(module, _, output): + if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]: + cache[module] = output # save as-is, for the first token or cross attention + else: + cache[module] = torch.cat([cache[module], output], dim=1).detach() + return cache[module] + + def install_hooks(layer: nn.Module): + if isinstance(layer, MultiHeadAttention): + hooks.append(layer.key.register_forward_hook(save_to_cache)) + hooks.append(layer.value.register_forward_hook(save_to_cache)) + + self.decoder.apply(install_hooks) + return cache, hooks + + detect_language = detect_language_function + transcribe = transcribe_function + decode = decode_function diff --git a/src/models/whisper/whisper/normalizers/__init__.py b/src/models/whisper/whisper/normalizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0b10d5a9d6389368fe7f3bac38e3b3cd9cc1470f --- /dev/null +++ b/src/models/whisper/whisper/normalizers/__init__.py @@ -0,0 +1,2 @@ +from .basic import BasicTextNormalizer +from .english import EnglishTextNormalizer diff --git a/src/models/whisper/whisper/normalizers/basic.py b/src/models/whisper/whisper/normalizers/basic.py new file mode 100644 index 0000000000000000000000000000000000000000..ef8d249bfca2cd10bccdd56950504581e2598560 --- /dev/null +++ b/src/models/whisper/whisper/normalizers/basic.py @@ -0,0 +1,71 @@ +import re +import unicodedata + +import regex + +# non-ASCII letters that are not separated by "NFKD" normalization +ADDITIONAL_DIACRITICS = { + "œ": "oe", + "Œ": "OE", + "ø": "o", + "Ø": "O", + "æ": "ae", + "Æ": "AE", + "ß": "ss", + "ẞ": "SS", + "đ": "d", + "Đ": "D", + "ð": "d", + "Ð": "D", + "þ": "th", + "Þ": "th", + "ł": "l", + "Ł": "L", +} + + +def remove_symbols_and_diacritics(s: str, keep=""): + """ + Replace any other markers, symbols, and punctuations with a space, + and drop any diacritics (category 'Mn' and some manual mappings) + """ + return "".join( + c + if c in keep + else ADDITIONAL_DIACRITICS[c] + if c in ADDITIONAL_DIACRITICS + else "" + if unicodedata.category(c) == "Mn" + else " " + if unicodedata.category(c)[0] in "MSP" + else c + for c in unicodedata.normalize("NFKD", s) + ) + + +def remove_symbols(s: str): + """ + Replace any other markers, symbols, punctuations with a space, keeping diacritics + """ + return "".join( + " " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s) + ) + + +class BasicTextNormalizer: + def __init__(self, remove_diacritics: bool = False, split_letters: bool = False): + self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols + self.split_letters = split_letters + + def __call__(self, s: str): + s = s.lower() + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = self.clean(s).lower() + + if self.split_letters: + s = " ".join(regex.findall(r"\X", s, regex.U)) + + s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space + + return s diff --git a/src/models/whisper/whisper/normalizers/english.json b/src/models/whisper/whisper/normalizers/english.json new file mode 100644 index 0000000000000000000000000000000000000000..bd84ae73faeb4dfa75214b99c4b392a51be90336 --- /dev/null +++ b/src/models/whisper/whisper/normalizers/english.json @@ -0,0 +1,1742 @@ +{ + "accessorise": "accessorize", + "accessorised": "accessorized", + "accessorises": "accessorizes", + "accessorising": "accessorizing", + "acclimatisation": "acclimatization", + "acclimatise": "acclimatize", + "acclimatised": "acclimatized", + "acclimatises": "acclimatizes", + "acclimatising": "acclimatizing", + "accoutrements": "accouterments", + "aeon": "eon", + "aeons": "eons", + "aerogramme": "aerogram", + "aerogrammes": "aerograms", + "aeroplane": "airplane", + "aeroplanes": "airplanes", + "aesthete": "esthete", + "aesthetes": "esthetes", + "aesthetic": "esthetic", + "aesthetically": "esthetically", + "aesthetics": "esthetics", + "aetiology": "etiology", + "ageing": "aging", + "aggrandisement": "aggrandizement", + "agonise": "agonize", + "agonised": "agonized", + "agonises": "agonizes", + "agonising": "agonizing", + "agonisingly": "agonizingly", + "almanack": "almanac", + "almanacks": "almanacs", + "aluminium": "aluminum", + "amortisable": "amortizable", + "amortisation": "amortization", + "amortisations": "amortizations", + "amortise": "amortize", + "amortised": "amortized", + "amortises": "amortizes", + "amortising": "amortizing", + "amphitheatre": "amphitheater", + "amphitheatres": "amphitheaters", + "anaemia": "anemia", + "anaemic": "anemic", + "anaesthesia": "anesthesia", + "anaesthetic": "anesthetic", + "anaesthetics": "anesthetics", + "anaesthetise": "anesthetize", + "anaesthetised": "anesthetized", + "anaesthetises": "anesthetizes", + "anaesthetising": "anesthetizing", + "anaesthetist": "anesthetist", + "anaesthetists": "anesthetists", + "anaesthetize": "anesthetize", + "anaesthetized": "anesthetized", + "anaesthetizes": "anesthetizes", + "anaesthetizing": "anesthetizing", + "analogue": "analog", + "analogues": "analogs", + "analyse": "analyze", + "analysed": "analyzed", + "analyses": "analyzes", + "analysing": "analyzing", + "anglicise": "anglicize", + "anglicised": "anglicized", + "anglicises": "anglicizes", + "anglicising": "anglicizing", + "annualised": "annualized", + "antagonise": "antagonize", + "antagonised": "antagonized", + "antagonises": "antagonizes", + "antagonising": "antagonizing", + "apologise": "apologize", + "apologised": "apologized", + "apologises": "apologizes", + "apologising": "apologizing", + "appal": "appall", + "appals": "appalls", + "appetiser": "appetizer", + "appetisers": "appetizers", + "appetising": "appetizing", + "appetisingly": "appetizingly", + "arbour": "arbor", + "arbours": "arbors", + "archeological": "archaeological", + "archaeologically": "archeologically", + "archaeologist": "archeologist", + "archaeologists": "archeologists", + "archaeology": "archeology", + "ardour": "ardor", + "armour": "armor", + "armoured": "armored", + "armourer": "armorer", + "armourers": "armorers", + "armouries": "armories", + "armoury": "armory", + "artefact": "artifact", + "artefacts": "artifacts", + "authorise": "authorize", + "authorised": "authorized", + "authorises": "authorizes", + "authorising": "authorizing", + "axe": "ax", + "backpedalled": "backpedaled", + "backpedalling": "backpedaling", + "bannister": "banister", + "bannisters": "banisters", + "baptise": "baptize", + "baptised": "baptized", + "baptises": "baptizes", + "baptising": "baptizing", + "bastardise": "bastardize", + "bastardised": "bastardized", + "bastardises": "bastardizes", + "bastardising": "bastardizing", + "battleax": "battleaxe", + "baulk": "balk", + "baulked": "balked", + "baulking": "balking", + "baulks": "balks", + "bedevilled": "bedeviled", + "bedevilling": "bedeviling", + "behaviour": "behavior", + "behavioural": "behavioral", + "behaviourism": "behaviorism", + "behaviourist": "behaviorist", + "behaviourists": "behaviorists", + "behaviours": "behaviors", + "behove": "behoove", + "behoved": "behooved", + "behoves": "behooves", + "bejewelled": "bejeweled", + "belabour": "belabor", + "belaboured": "belabored", + "belabouring": "belaboring", + "belabours": "belabors", + "bevelled": "beveled", + "bevvies": "bevies", + "bevvy": "bevy", + "biassed": "biased", + "biassing": "biasing", + "bingeing": "binging", + "bougainvillaea": "bougainvillea", + "bougainvillaeas": "bougainvilleas", + "bowdlerise": "bowdlerize", + "bowdlerised": "bowdlerized", + "bowdlerises": "bowdlerizes", + "bowdlerising": "bowdlerizing", + "breathalyse": "breathalyze", + "breathalysed": "breathalyzed", + "breathalyser": "breathalyzer", + "breathalysers": "breathalyzers", + "breathalyses": "breathalyzes", + "breathalysing": "breathalyzing", + "brutalise": "brutalize", + "brutalised": "brutalized", + "brutalises": "brutalizes", + "brutalising": "brutalizing", + "busses": "buses", + "bussing": "busing", + "caesarean": "cesarean", + "caesareans": "cesareans", + "calibre": "caliber", + "calibres": "calibers", + "calliper": "caliper", + "callipers": "calipers", + "callisthenics": "calisthenics", + "canalise": "canalize", + "canalised": "canalized", + "canalises": "canalizes", + "canalising": "canalizing", + "cancelation": "cancellation", + "cancelations": "cancellations", + "cancelled": "canceled", + "cancelling": "canceling", + "candour": "candor", + "cannibalise": "cannibalize", + "cannibalised": "cannibalized", + "cannibalises": "cannibalizes", + "cannibalising": "cannibalizing", + "canonise": "canonize", + "canonised": "canonized", + "canonises": "canonizes", + "canonising": "canonizing", + "capitalise": "capitalize", + "capitalised": "capitalized", + "capitalises": "capitalizes", + "capitalising": "capitalizing", + "caramelise": "caramelize", + "caramelised": "caramelized", + "caramelises": "caramelizes", + "caramelising": "caramelizing", + "carbonise": "carbonize", + "carbonised": "carbonized", + "carbonises": "carbonizes", + "carbonising": "carbonizing", + "carolled": "caroled", + "carolling": "caroling", + "catalogue": "catalog", + "catalogued": "cataloged", + "catalogues": "catalogs", + "cataloguing": "cataloging", + "catalyse": "catalyze", + "catalysed": "catalyzed", + "catalyses": "catalyzes", + "catalysing": "catalyzing", + "categorise": "categorize", + "categorised": "categorized", + "categorises": "categorizes", + "categorising": "categorizing", + "cauterise": "cauterize", + "cauterised": "cauterized", + "cauterises": "cauterizes", + "cauterising": "cauterizing", + "cavilled": "caviled", + "cavilling": "caviling", + "centigramme": "centigram", + "centigrammes": "centigrams", + "centilitre": "centiliter", + "centilitres": "centiliters", + "centimetre": "centimeter", + "centimetres": "centimeters", + "centralise": "centralize", + "centralised": "centralized", + "centralises": "centralizes", + "centralising": "centralizing", + "centre": "center", + "centred": "centered", + "centrefold": "centerfold", + "centrefolds": "centerfolds", + "centrepiece": "centerpiece", + "centrepieces": "centerpieces", + "centres": "centers", + "channelled": "channeled", + "channelling": "channeling", + "characterise": "characterize", + "characterised": "characterized", + "characterises": "characterizes", + "characterising": "characterizing", + "cheque": "check", + "chequebook": "checkbook", + "chequebooks": "checkbooks", + "chequered": "checkered", + "cheques": "checks", + "chilli": "chili", + "chimaera": "chimera", + "chimaeras": "chimeras", + "chiselled": "chiseled", + "chiselling": "chiseling", + "circularise": "circularize", + "circularised": "circularized", + "circularises": "circularizes", + "circularising": "circularizing", + "civilise": "civilize", + "civilised": "civilized", + "civilises": "civilizes", + "civilising": "civilizing", + "clamour": "clamor", + "clamoured": "clamored", + "clamouring": "clamoring", + "clamours": "clamors", + "clangour": "clangor", + "clarinettist": "clarinetist", + "clarinettists": "clarinetists", + "collectivise": "collectivize", + "collectivised": "collectivized", + "collectivises": "collectivizes", + "collectivising": "collectivizing", + "colonisation": "colonization", + "colonise": "colonize", + "colonised": "colonized", + "coloniser": "colonizer", + "colonisers": "colonizers", + "colonises": "colonizes", + "colonising": "colonizing", + "colour": "color", + "colourant": "colorant", + "colourants": "colorants", + "coloured": "colored", + "coloureds": "coloreds", + "colourful": "colorful", + "colourfully": "colorfully", + "colouring": "coloring", + "colourize": "colorize", + "colourized": "colorized", + "colourizes": "colorizes", + "colourizing": "colorizing", + "colourless": "colorless", + "colours": "colors", + "commercialise": "commercialize", + "commercialised": "commercialized", + "commercialises": "commercializes", + "commercialising": "commercializing", + "compartmentalise": "compartmentalize", + "compartmentalised": "compartmentalized", + "compartmentalises": "compartmentalizes", + "compartmentalising": "compartmentalizing", + "computerise": "computerize", + "computerised": "computerized", + "computerises": "computerizes", + "computerising": "computerizing", + "conceptualise": "conceptualize", + "conceptualised": "conceptualized", + "conceptualises": "conceptualizes", + "conceptualising": "conceptualizing", + "connexion": "connection", + "connexions": "connections", + "contextualise": "contextualize", + "contextualised": "contextualized", + "contextualises": "contextualizes", + "contextualising": "contextualizing", + "cosier": "cozier", + "cosies": "cozies", + "cosiest": "coziest", + "cosily": "cozily", + "cosiness": "coziness", + "cosy": "cozy", + "councillor": "councilor", + "councillors": "councilors", + "counselled": "counseled", + "counselling": "counseling", + "counsellor": "counselor", + "counsellors": "counselors", + "crenelated": "crenellated", + "criminalise": "criminalize", + "criminalised": "criminalized", + "criminalises": "criminalizes", + "criminalising": "criminalizing", + "criticise": "criticize", + "criticised": "criticized", + "criticises": "criticizes", + "criticising": "criticizing", + "crueller": "crueler", + "cruellest": "cruelest", + "crystallisation": "crystallization", + "crystallise": "crystallize", + "crystallised": "crystallized", + "crystallises": "crystallizes", + "crystallising": "crystallizing", + "cudgelled": "cudgeled", + "cudgelling": "cudgeling", + "customise": "customize", + "customised": "customized", + "customises": "customizes", + "customising": "customizing", + "cypher": "cipher", + "cyphers": "ciphers", + "decentralisation": "decentralization", + "decentralise": "decentralize", + "decentralised": "decentralized", + "decentralises": "decentralizes", + "decentralising": "decentralizing", + "decriminalisation": "decriminalization", + "decriminalise": "decriminalize", + "decriminalised": "decriminalized", + "decriminalises": "decriminalizes", + "decriminalising": "decriminalizing", + "defence": "defense", + "defenceless": "defenseless", + "defences": "defenses", + "dehumanisation": "dehumanization", + "dehumanise": "dehumanize", + "dehumanised": "dehumanized", + "dehumanises": "dehumanizes", + "dehumanising": "dehumanizing", + "demeanour": "demeanor", + "demilitarisation": "demilitarization", + "demilitarise": "demilitarize", + "demilitarised": "demilitarized", + "demilitarises": "demilitarizes", + "demilitarising": "demilitarizing", + "demobilisation": "demobilization", + "demobilise": "demobilize", + "demobilised": "demobilized", + "demobilises": "demobilizes", + "demobilising": "demobilizing", + "democratisation": "democratization", + "democratise": "democratize", + "democratised": "democratized", + "democratises": "democratizes", + "democratising": "democratizing", + "demonise": "demonize", + "demonised": "demonized", + "demonises": "demonizes", + "demonising": "demonizing", + "demoralisation": "demoralization", + "demoralise": "demoralize", + "demoralised": "demoralized", + "demoralises": "demoralizes", + "demoralising": "demoralizing", + "denationalisation": "denationalization", + "denationalise": "denationalize", + "denationalised": "denationalized", + "denationalises": "denationalizes", + "denationalising": "denationalizing", + "deodorise": "deodorize", + "deodorised": "deodorized", + "deodorises": "deodorizes", + "deodorising": "deodorizing", + "depersonalise": "depersonalize", + "depersonalised": "depersonalized", + "depersonalises": "depersonalizes", + "depersonalising": "depersonalizing", + "deputise": "deputize", + "deputised": "deputized", + "deputises": "deputizes", + "deputising": "deputizing", + "desensitisation": "desensitization", + "desensitise": "desensitize", + "desensitised": "desensitized", + "desensitises": "desensitizes", + "desensitising": "desensitizing", + "destabilisation": "destabilization", + "destabilise": "destabilize", + "destabilised": "destabilized", + "destabilises": "destabilizes", + "destabilising": "destabilizing", + "dialled": "dialed", + "dialling": "dialing", + "dialogue": "dialog", + "dialogues": "dialogs", + "diarrhoea": "diarrhea", + "digitise": "digitize", + "digitised": "digitized", + "digitises": "digitizes", + "digitising": "digitizing", + "disc": "disk", + "discolour": "discolor", + "discoloured": "discolored", + "discolouring": "discoloring", + "discolours": "discolors", + "discs": "disks", + "disembowelled": "disemboweled", + "disembowelling": "disemboweling", + "disfavour": "disfavor", + "dishevelled": "disheveled", + "dishonour": "dishonor", + "dishonourable": "dishonorable", + "dishonourably": "dishonorably", + "dishonoured": "dishonored", + "dishonouring": "dishonoring", + "dishonours": "dishonors", + "disorganisation": "disorganization", + "disorganised": "disorganized", + "distil": "distill", + "distils": "distills", + "dramatisation": "dramatization", + "dramatisations": "dramatizations", + "dramatise": "dramatize", + "dramatised": "dramatized", + "dramatises": "dramatizes", + "dramatising": "dramatizing", + "draught": "draft", + "draughtboard": "draftboard", + "draughtboards": "draftboards", + "draughtier": "draftier", + "draughtiest": "draftiest", + "draughts": "drafts", + "draughtsman": "draftsman", + "draughtsmanship": "draftsmanship", + "draughtsmen": "draftsmen", + "draughtswoman": "draftswoman", + "draughtswomen": "draftswomen", + "draughty": "drafty", + "drivelled": "driveled", + "drivelling": "driveling", + "duelled": "dueled", + "duelling": "dueling", + "economise": "economize", + "economised": "economized", + "economises": "economizes", + "economising": "economizing", + "edoema": "edema", + "editorialise": "editorialize", + "editorialised": "editorialized", + "editorialises": "editorializes", + "editorialising": "editorializing", + "empathise": "empathize", + "empathised": "empathized", + "empathises": "empathizes", + "empathising": "empathizing", + "emphasise": "emphasize", + "emphasised": "emphasized", + "emphasises": "emphasizes", + "emphasising": "emphasizing", + "enamelled": "enameled", + "enamelling": "enameling", + "enamoured": "enamored", + "encyclopaedia": "encyclopedia", + "encyclopaedias": "encyclopedias", + "encyclopaedic": "encyclopedic", + "endeavour": "endeavor", + "endeavoured": "endeavored", + "endeavouring": "endeavoring", + "endeavours": "endeavors", + "energise": "energize", + "energised": "energized", + "energises": "energizes", + "energising": "energizing", + "enrol": "enroll", + "enrols": "enrolls", + "enthral": "enthrall", + "enthrals": "enthralls", + "epaulette": "epaulet", + "epaulettes": "epaulets", + "epicentre": "epicenter", + "epicentres": "epicenters", + "epilogue": "epilog", + "epilogues": "epilogs", + "epitomise": "epitomize", + "epitomised": "epitomized", + "epitomises": "epitomizes", + "epitomising": "epitomizing", + "equalisation": "equalization", + "equalise": "equalize", + "equalised": "equalized", + "equaliser": "equalizer", + "equalisers": "equalizers", + "equalises": "equalizes", + "equalising": "equalizing", + "eulogise": "eulogize", + "eulogised": "eulogized", + "eulogises": "eulogizes", + "eulogising": "eulogizing", + "evangelise": "evangelize", + "evangelised": "evangelized", + "evangelises": "evangelizes", + "evangelising": "evangelizing", + "exorcise": "exorcize", + "exorcised": "exorcized", + "exorcises": "exorcizes", + "exorcising": "exorcizing", + "extemporisation": "extemporization", + "extemporise": "extemporize", + "extemporised": "extemporized", + "extemporises": "extemporizes", + "extemporising": "extemporizing", + "externalisation": "externalization", + "externalisations": "externalizations", + "externalise": "externalize", + "externalised": "externalized", + "externalises": "externalizes", + "externalising": "externalizing", + "factorise": "factorize", + "factorised": "factorized", + "factorises": "factorizes", + "factorising": "factorizing", + "faecal": "fecal", + "faeces": "feces", + "familiarisation": "familiarization", + "familiarise": "familiarize", + "familiarised": "familiarized", + "familiarises": "familiarizes", + "familiarising": "familiarizing", + "fantasise": "fantasize", + "fantasised": "fantasized", + "fantasises": "fantasizes", + "fantasising": "fantasizing", + "favour": "favor", + "favourable": "favorable", + "favourably": "favorably", + "favoured": "favored", + "favouring": "favoring", + "favourite": "favorite", + "favourites": "favorites", + "favouritism": "favoritism", + "favours": "favors", + "feminise": "feminize", + "feminised": "feminized", + "feminises": "feminizes", + "feminising": "feminizing", + "fertilisation": "fertilization", + "fertilise": "fertilize", + "fertilised": "fertilized", + "fertiliser": "fertilizer", + "fertilisers": "fertilizers", + "fertilises": "fertilizes", + "fertilising": "fertilizing", + "fervour": "fervor", + "fibre": "fiber", + "fibreglass": "fiberglass", + "fibres": "fibers", + "fictionalisation": "fictionalization", + "fictionalisations": "fictionalizations", + "fictionalise": "fictionalize", + "fictionalised": "fictionalized", + "fictionalises": "fictionalizes", + "fictionalising": "fictionalizing", + "fillet": "filet", + "filleted": "fileted", + "filleting": "fileting", + "fillets": "filets", + "finalisation": "finalization", + "finalise": "finalize", + "finalised": "finalized", + "finalises": "finalizes", + "finalising": "finalizing", + "flautist": "flutist", + "flautists": "flutists", + "flavour": "flavor", + "flavoured": "flavored", + "flavouring": "flavoring", + "flavourings": "flavorings", + "flavourless": "flavorless", + "flavours": "flavors", + "flavoursome": "flavorsome", + "flyer / flier": "flier / flyer", + "foetal": "fetal", + "foetid": "fetid", + "foetus": "fetus", + "foetuses": "fetuses", + "formalisation": "formalization", + "formalise": "formalize", + "formalised": "formalized", + "formalises": "formalizes", + "formalising": "formalizing", + "fossilisation": "fossilization", + "fossilise": "fossilize", + "fossilised": "fossilized", + "fossilises": "fossilizes", + "fossilising": "fossilizing", + "fraternisation": "fraternization", + "fraternise": "fraternize", + "fraternised": "fraternized", + "fraternises": "fraternizes", + "fraternising": "fraternizing", + "fulfil": "fulfill", + "fulfilment": "fulfillment", + "fulfils": "fulfills", + "funnelled": "funneled", + "funnelling": "funneling", + "galvanise": "galvanize", + "galvanised": "galvanized", + "galvanises": "galvanizes", + "galvanising": "galvanizing", + "gambolled": "gamboled", + "gambolling": "gamboling", + "gaol": "jail", + "gaolbird": "jailbird", + "gaolbirds": "jailbirds", + "gaolbreak": "jailbreak", + "gaolbreaks": "jailbreaks", + "gaoled": "jailed", + "gaoler": "jailer", + "gaolers": "jailers", + "gaoling": "jailing", + "gaols": "jails", + "gasses": "gases", + "gage": "gauge", + "gaged": "gauged", + "gages": "gauges", + "gaging": "gauging", + "generalisation": "generalization", + "generalisations": "generalizations", + "generalise": "generalize", + "generalised": "generalized", + "generalises": "generalizes", + "generalising": "generalizing", + "ghettoise": "ghettoize", + "ghettoised": "ghettoized", + "ghettoises": "ghettoizes", + "ghettoising": "ghettoizing", + "gipsies": "gypsies", + "glamorise": "glamorize", + "glamorised": "glamorized", + "glamorises": "glamorizes", + "glamorising": "glamorizing", + "glamor": "glamour", + "globalisation": "globalization", + "globalise": "globalize", + "globalised": "globalized", + "globalises": "globalizes", + "globalising": "globalizing", + "glueing": "gluing", + "goitre": "goiter", + "goitres": "goiters", + "gonorrhoea": "gonorrhea", + "gramme": "gram", + "grammes": "grams", + "gravelled": "graveled", + "grey": "gray", + "greyed": "grayed", + "greying": "graying", + "greyish": "grayish", + "greyness": "grayness", + "greys": "grays", + "grovelled": "groveled", + "grovelling": "groveling", + "groyne": "groin", + "groynes": "groins", + "gruelling": "grueling", + "gruellingly": "gruelingly", + "gryphon": "griffin", + "gryphons": "griffins", + "gynaecological": "gynecological", + "gynaecologist": "gynecologist", + "gynaecologists": "gynecologists", + "gynaecology": "gynecology", + "haematological": "hematological", + "haematologist": "hematologist", + "haematologists": "hematologists", + "haematology": "hematology", + "haemoglobin": "hemoglobin", + "haemophilia": "hemophilia", + "haemophiliac": "hemophiliac", + "haemophiliacs": "hemophiliacs", + "haemorrhage": "hemorrhage", + "haemorrhaged": "hemorrhaged", + "haemorrhages": "hemorrhages", + "haemorrhaging": "hemorrhaging", + "haemorrhoids": "hemorrhoids", + "harbour": "harbor", + "harboured": "harbored", + "harbouring": "harboring", + "harbours": "harbors", + "harmonisation": "harmonization", + "harmonise": "harmonize", + "harmonised": "harmonized", + "harmonises": "harmonizes", + "harmonising": "harmonizing", + "homoeopath": "homeopath", + "homoeopathic": "homeopathic", + "homoeopaths": "homeopaths", + "homoeopathy": "homeopathy", + "homogenise": "homogenize", + "homogenised": "homogenized", + "homogenises": "homogenizes", + "homogenising": "homogenizing", + "honour": "honor", + "honourable": "honorable", + "honourably": "honorably", + "honoured": "honored", + "honouring": "honoring", + "honours": "honors", + "hospitalisation": "hospitalization", + "hospitalise": "hospitalize", + "hospitalised": "hospitalized", + "hospitalises": "hospitalizes", + "hospitalising": "hospitalizing", + "humanise": "humanize", + "humanised": "humanized", + "humanises": "humanizes", + "humanising": "humanizing", + "humour": "humor", + "humoured": "humored", + "humouring": "humoring", + "humourless": "humorless", + "humours": "humors", + "hybridise": "hybridize", + "hybridised": "hybridized", + "hybridises": "hybridizes", + "hybridising": "hybridizing", + "hypnotise": "hypnotize", + "hypnotised": "hypnotized", + "hypnotises": "hypnotizes", + "hypnotising": "hypnotizing", + "hypothesise": "hypothesize", + "hypothesised": "hypothesized", + "hypothesises": "hypothesizes", + "hypothesising": "hypothesizing", + "idealisation": "idealization", + "idealise": "idealize", + "idealised": "idealized", + "idealises": "idealizes", + "idealising": "idealizing", + "idolise": "idolize", + "idolised": "idolized", + "idolises": "idolizes", + "idolising": "idolizing", + "immobilisation": "immobilization", + "immobilise": "immobilize", + "immobilised": "immobilized", + "immobiliser": "immobilizer", + "immobilisers": "immobilizers", + "immobilises": "immobilizes", + "immobilising": "immobilizing", + "immortalise": "immortalize", + "immortalised": "immortalized", + "immortalises": "immortalizes", + "immortalising": "immortalizing", + "immunisation": "immunization", + "immunise": "immunize", + "immunised": "immunized", + "immunises": "immunizes", + "immunising": "immunizing", + "impanelled": "impaneled", + "impanelling": "impaneling", + "imperilled": "imperiled", + "imperilling": "imperiling", + "individualise": "individualize", + "individualised": "individualized", + "individualises": "individualizes", + "individualising": "individualizing", + "industrialise": "industrialize", + "industrialised": "industrialized", + "industrialises": "industrializes", + "industrialising": "industrializing", + "inflexion": "inflection", + "inflexions": "inflections", + "initialise": "initialize", + "initialised": "initialized", + "initialises": "initializes", + "initialising": "initializing", + "initialled": "initialed", + "initialling": "initialing", + "instal": "install", + "instalment": "installment", + "instalments": "installments", + "instals": "installs", + "instil": "instill", + "instils": "instills", + "institutionalisation": "institutionalization", + "institutionalise": "institutionalize", + "institutionalised": "institutionalized", + "institutionalises": "institutionalizes", + "institutionalising": "institutionalizing", + "intellectualise": "intellectualize", + "intellectualised": "intellectualized", + "intellectualises": "intellectualizes", + "intellectualising": "intellectualizing", + "internalisation": "internalization", + "internalise": "internalize", + "internalised": "internalized", + "internalises": "internalizes", + "internalising": "internalizing", + "internationalisation": "internationalization", + "internationalise": "internationalize", + "internationalised": "internationalized", + "internationalises": "internationalizes", + "internationalising": "internationalizing", + "ionisation": "ionization", + "ionise": "ionize", + "ionised": "ionized", + "ioniser": "ionizer", + "ionisers": "ionizers", + "ionises": "ionizes", + "ionising": "ionizing", + "italicise": "italicize", + "italicised": "italicized", + "italicises": "italicizes", + "italicising": "italicizing", + "itemise": "itemize", + "itemised": "itemized", + "itemises": "itemizes", + "itemising": "itemizing", + "jeopardise": "jeopardize", + "jeopardised": "jeopardized", + "jeopardises": "jeopardizes", + "jeopardising": "jeopardizing", + "jewelled": "jeweled", + "jeweller": "jeweler", + "jewellers": "jewelers", + "jewellery": "jewelry", + "judgement": "judgment", + "kilogramme": "kilogram", + "kilogrammes": "kilograms", + "kilometre": "kilometer", + "kilometres": "kilometers", + "labelled": "labeled", + "labelling": "labeling", + "labour": "labor", + "laboured": "labored", + "labourer": "laborer", + "labourers": "laborers", + "labouring": "laboring", + "labours": "labors", + "lacklustre": "lackluster", + "legalisation": "legalization", + "legalise": "legalize", + "legalised": "legalized", + "legalises": "legalizes", + "legalising": "legalizing", + "legitimise": "legitimize", + "legitimised": "legitimized", + "legitimises": "legitimizes", + "legitimising": "legitimizing", + "leukaemia": "leukemia", + "levelled": "leveled", + "leveller": "leveler", + "levellers": "levelers", + "levelling": "leveling", + "libelled": "libeled", + "libelling": "libeling", + "libellous": "libelous", + "liberalisation": "liberalization", + "liberalise": "liberalize", + "liberalised": "liberalized", + "liberalises": "liberalizes", + "liberalising": "liberalizing", + "licence": "license", + "licenced": "licensed", + "licences": "licenses", + "licencing": "licensing", + "likeable": "likable", + "lionisation": "lionization", + "lionise": "lionize", + "lionised": "lionized", + "lionises": "lionizes", + "lionising": "lionizing", + "liquidise": "liquidize", + "liquidised": "liquidized", + "liquidiser": "liquidizer", + "liquidisers": "liquidizers", + "liquidises": "liquidizes", + "liquidising": "liquidizing", + "litre": "liter", + "litres": "liters", + "localise": "localize", + "localised": "localized", + "localises": "localizes", + "localising": "localizing", + "louvre": "louver", + "louvred": "louvered", + "louvres": "louvers", + "lustre": "luster", + "magnetise": "magnetize", + "magnetised": "magnetized", + "magnetises": "magnetizes", + "magnetising": "magnetizing", + "manoeuvrability": "maneuverability", + "manoeuvrable": "maneuverable", + "manoeuvre": "maneuver", + "manoeuvred": "maneuvered", + "manoeuvres": "maneuvers", + "manoeuvring": "maneuvering", + "manoeuvrings": "maneuverings", + "marginalisation": "marginalization", + "marginalise": "marginalize", + "marginalised": "marginalized", + "marginalises": "marginalizes", + "marginalising": "marginalizing", + "marshalled": "marshaled", + "marshalling": "marshaling", + "marvelled": "marveled", + "marvelling": "marveling", + "marvellous": "marvelous", + "marvellously": "marvelously", + "materialisation": "materialization", + "materialise": "materialize", + "materialised": "materialized", + "materialises": "materializes", + "materialising": "materializing", + "maximisation": "maximization", + "maximise": "maximize", + "maximised": "maximized", + "maximises": "maximizes", + "maximising": "maximizing", + "meagre": "meager", + "mechanisation": "mechanization", + "mechanise": "mechanize", + "mechanised": "mechanized", + "mechanises": "mechanizes", + "mechanising": "mechanizing", + "mediaeval": "medieval", + "memorialise": "memorialize", + "memorialised": "memorialized", + "memorialises": "memorializes", + "memorialising": "memorializing", + "memorise": "memorize", + "memorised": "memorized", + "memorises": "memorizes", + "memorising": "memorizing", + "mesmerise": "mesmerize", + "mesmerised": "mesmerized", + "mesmerises": "mesmerizes", + "mesmerising": "mesmerizing", + "metabolise": "metabolize", + "metabolised": "metabolized", + "metabolises": "metabolizes", + "metabolising": "metabolizing", + "metre": "meter", + "metres": "meters", + "micrometre": "micrometer", + "micrometres": "micrometers", + "militarise": "militarize", + "militarised": "militarized", + "militarises": "militarizes", + "militarising": "militarizing", + "milligramme": "milligram", + "milligrammes": "milligrams", + "millilitre": "milliliter", + "millilitres": "milliliters", + "millimetre": "millimeter", + "millimetres": "millimeters", + "miniaturisation": "miniaturization", + "miniaturise": "miniaturize", + "miniaturised": "miniaturized", + "miniaturises": "miniaturizes", + "miniaturising": "miniaturizing", + "minibusses": "minibuses", + "minimise": "minimize", + "minimised": "minimized", + "minimises": "minimizes", + "minimising": "minimizing", + "misbehaviour": "misbehavior", + "misdemeanour": "misdemeanor", + "misdemeanours": "misdemeanors", + "misspelt": "misspelled", + "mitre": "miter", + "mitres": "miters", + "mobilisation": "mobilization", + "mobilise": "mobilize", + "mobilised": "mobilized", + "mobilises": "mobilizes", + "mobilising": "mobilizing", + "modelled": "modeled", + "modeller": "modeler", + "modellers": "modelers", + "modelling": "modeling", + "modernise": "modernize", + "modernised": "modernized", + "modernises": "modernizes", + "modernising": "modernizing", + "moisturise": "moisturize", + "moisturised": "moisturized", + "moisturiser": "moisturizer", + "moisturisers": "moisturizers", + "moisturises": "moisturizes", + "moisturising": "moisturizing", + "monologue": "monolog", + "monologues": "monologs", + "monopolisation": "monopolization", + "monopolise": "monopolize", + "monopolised": "monopolized", + "monopolises": "monopolizes", + "monopolising": "monopolizing", + "moralise": "moralize", + "moralised": "moralized", + "moralises": "moralizes", + "moralising": "moralizing", + "motorised": "motorized", + "mould": "mold", + "moulded": "molded", + "moulder": "molder", + "mouldered": "moldered", + "mouldering": "moldering", + "moulders": "molders", + "mouldier": "moldier", + "mouldiest": "moldiest", + "moulding": "molding", + "mouldings": "moldings", + "moulds": "molds", + "mouldy": "moldy", + "moult": "molt", + "moulted": "molted", + "moulting": "molting", + "moults": "molts", + "moustache": "mustache", + "moustached": "mustached", + "moustaches": "mustaches", + "moustachioed": "mustachioed", + "multicoloured": "multicolored", + "nationalisation": "nationalization", + "nationalisations": "nationalizations", + "nationalise": "nationalize", + "nationalised": "nationalized", + "nationalises": "nationalizes", + "nationalising": "nationalizing", + "naturalisation": "naturalization", + "naturalise": "naturalize", + "naturalised": "naturalized", + "naturalises": "naturalizes", + "naturalising": "naturalizing", + "neighbour": "neighbor", + "neighbourhood": "neighborhood", + "neighbourhoods": "neighborhoods", + "neighbouring": "neighboring", + "neighbourliness": "neighborliness", + "neighbourly": "neighborly", + "neighbours": "neighbors", + "neutralisation": "neutralization", + "neutralise": "neutralize", + "neutralised": "neutralized", + "neutralises": "neutralizes", + "neutralising": "neutralizing", + "normalisation": "normalization", + "normalise": "normalize", + "normalised": "normalized", + "normalises": "normalizes", + "normalising": "normalizing", + "odour": "odor", + "odourless": "odorless", + "odours": "odors", + "oesophagus": "esophagus", + "oesophaguses": "esophaguses", + "oestrogen": "estrogen", + "offence": "offense", + "offences": "offenses", + "omelette": "omelet", + "omelettes": "omelets", + "optimise": "optimize", + "optimised": "optimized", + "optimises": "optimizes", + "optimising": "optimizing", + "organisation": "organization", + "organisational": "organizational", + "organisations": "organizations", + "organise": "organize", + "organised": "organized", + "organiser": "organizer", + "organisers": "organizers", + "organises": "organizes", + "organising": "organizing", + "orthopaedic": "orthopedic", + "orthopaedics": "orthopedics", + "ostracise": "ostracize", + "ostracised": "ostracized", + "ostracises": "ostracizes", + "ostracising": "ostracizing", + "outmanoeuvre": "outmaneuver", + "outmanoeuvred": "outmaneuvered", + "outmanoeuvres": "outmaneuvers", + "outmanoeuvring": "outmaneuvering", + "overemphasise": "overemphasize", + "overemphasised": "overemphasized", + "overemphasises": "overemphasizes", + "overemphasising": "overemphasizing", + "oxidisation": "oxidization", + "oxidise": "oxidize", + "oxidised": "oxidized", + "oxidises": "oxidizes", + "oxidising": "oxidizing", + "paederast": "pederast", + "paederasts": "pederasts", + "paediatric": "pediatric", + "paediatrician": "pediatrician", + "paediatricians": "pediatricians", + "paediatrics": "pediatrics", + "paedophile": "pedophile", + "paedophiles": "pedophiles", + "paedophilia": "pedophilia", + "palaeolithic": "paleolithic", + "palaeontologist": "paleontologist", + "palaeontologists": "paleontologists", + "palaeontology": "paleontology", + "panelled": "paneled", + "panelling": "paneling", + "panellist": "panelist", + "panellists": "panelists", + "paralyse": "paralyze", + "paralysed": "paralyzed", + "paralyses": "paralyzes", + "paralysing": "paralyzing", + "parcelled": "parceled", + "parcelling": "parceling", + "parlour": "parlor", + "parlours": "parlors", + "particularise": "particularize", + "particularised": "particularized", + "particularises": "particularizes", + "particularising": "particularizing", + "passivisation": "passivization", + "passivise": "passivize", + "passivised": "passivized", + "passivises": "passivizes", + "passivising": "passivizing", + "pasteurisation": "pasteurization", + "pasteurise": "pasteurize", + "pasteurised": "pasteurized", + "pasteurises": "pasteurizes", + "pasteurising": "pasteurizing", + "patronise": "patronize", + "patronised": "patronized", + "patronises": "patronizes", + "patronising": "patronizing", + "patronisingly": "patronizingly", + "pedalled": "pedaled", + "pedalling": "pedaling", + "pedestrianisation": "pedestrianization", + "pedestrianise": "pedestrianize", + "pedestrianised": "pedestrianized", + "pedestrianises": "pedestrianizes", + "pedestrianising": "pedestrianizing", + "penalise": "penalize", + "penalised": "penalized", + "penalises": "penalizes", + "penalising": "penalizing", + "pencilled": "penciled", + "pencilling": "penciling", + "personalise": "personalize", + "personalised": "personalized", + "personalises": "personalizes", + "personalising": "personalizing", + "pharmacopoeia": "pharmacopeia", + "pharmacopoeias": "pharmacopeias", + "philosophise": "philosophize", + "philosophised": "philosophized", + "philosophises": "philosophizes", + "philosophising": "philosophizing", + "philtre": "filter", + "philtres": "filters", + "phoney": "phony", + "plagiarise": "plagiarize", + "plagiarised": "plagiarized", + "plagiarises": "plagiarizes", + "plagiarising": "plagiarizing", + "plough": "plow", + "ploughed": "plowed", + "ploughing": "plowing", + "ploughman": "plowman", + "ploughmen": "plowmen", + "ploughs": "plows", + "ploughshare": "plowshare", + "ploughshares": "plowshares", + "polarisation": "polarization", + "polarise": "polarize", + "polarised": "polarized", + "polarises": "polarizes", + "polarising": "polarizing", + "politicisation": "politicization", + "politicise": "politicize", + "politicised": "politicized", + "politicises": "politicizes", + "politicising": "politicizing", + "popularisation": "popularization", + "popularise": "popularize", + "popularised": "popularized", + "popularises": "popularizes", + "popularising": "popularizing", + "pouffe": "pouf", + "pouffes": "poufs", + "practise": "practice", + "practised": "practiced", + "practises": "practices", + "practising": "practicing", + "praesidium": "presidium", + "praesidiums": "presidiums", + "pressurisation": "pressurization", + "pressurise": "pressurize", + "pressurised": "pressurized", + "pressurises": "pressurizes", + "pressurising": "pressurizing", + "pretence": "pretense", + "pretences": "pretenses", + "primaeval": "primeval", + "prioritisation": "prioritization", + "prioritise": "prioritize", + "prioritised": "prioritized", + "prioritises": "prioritizes", + "prioritising": "prioritizing", + "privatisation": "privatization", + "privatisations": "privatizations", + "privatise": "privatize", + "privatised": "privatized", + "privatises": "privatizes", + "privatising": "privatizing", + "professionalisation": "professionalization", + "professionalise": "professionalize", + "professionalised": "professionalized", + "professionalises": "professionalizes", + "professionalising": "professionalizing", + "programme": "program", + "programmes": "programs", + "prologue": "prolog", + "prologues": "prologs", + "propagandise": "propagandize", + "propagandised": "propagandized", + "propagandises": "propagandizes", + "propagandising": "propagandizing", + "proselytise": "proselytize", + "proselytised": "proselytized", + "proselytiser": "proselytizer", + "proselytisers": "proselytizers", + "proselytises": "proselytizes", + "proselytising": "proselytizing", + "psychoanalyse": "psychoanalyze", + "psychoanalysed": "psychoanalyzed", + "psychoanalyses": "psychoanalyzes", + "psychoanalysing": "psychoanalyzing", + "publicise": "publicize", + "publicised": "publicized", + "publicises": "publicizes", + "publicising": "publicizing", + "pulverisation": "pulverization", + "pulverise": "pulverize", + "pulverised": "pulverized", + "pulverises": "pulverizes", + "pulverising": "pulverizing", + "pummelled": "pummel", + "pummelling": "pummeled", + "pyjama": "pajama", + "pyjamas": "pajamas", + "pzazz": "pizzazz", + "quarrelled": "quarreled", + "quarrelling": "quarreling", + "radicalise": "radicalize", + "radicalised": "radicalized", + "radicalises": "radicalizes", + "radicalising": "radicalizing", + "rancour": "rancor", + "randomise": "randomize", + "randomised": "randomized", + "randomises": "randomizes", + "randomising": "randomizing", + "rationalisation": "rationalization", + "rationalisations": "rationalizations", + "rationalise": "rationalize", + "rationalised": "rationalized", + "rationalises": "rationalizes", + "rationalising": "rationalizing", + "ravelled": "raveled", + "ravelling": "raveling", + "realisable": "realizable", + "realisation": "realization", + "realisations": "realizations", + "realise": "realize", + "realised": "realized", + "realises": "realizes", + "realising": "realizing", + "recognisable": "recognizable", + "recognisably": "recognizably", + "recognisance": "recognizance", + "recognise": "recognize", + "recognised": "recognized", + "recognises": "recognizes", + "recognising": "recognizing", + "reconnoitre": "reconnoiter", + "reconnoitred": "reconnoitered", + "reconnoitres": "reconnoiters", + "reconnoitring": "reconnoitering", + "refuelled": "refueled", + "refuelling": "refueling", + "regularisation": "regularization", + "regularise": "regularize", + "regularised": "regularized", + "regularises": "regularizes", + "regularising": "regularizing", + "remodelled": "remodeled", + "remodelling": "remodeling", + "remould": "remold", + "remoulded": "remolded", + "remoulding": "remolding", + "remoulds": "remolds", + "reorganisation": "reorganization", + "reorganisations": "reorganizations", + "reorganise": "reorganize", + "reorganised": "reorganized", + "reorganises": "reorganizes", + "reorganising": "reorganizing", + "revelled": "reveled", + "reveller": "reveler", + "revellers": "revelers", + "revelling": "reveling", + "revitalise": "revitalize", + "revitalised": "revitalized", + "revitalises": "revitalizes", + "revitalising": "revitalizing", + "revolutionise": "revolutionize", + "revolutionised": "revolutionized", + "revolutionises": "revolutionizes", + "revolutionising": "revolutionizing", + "rhapsodise": "rhapsodize", + "rhapsodised": "rhapsodized", + "rhapsodises": "rhapsodizes", + "rhapsodising": "rhapsodizing", + "rigour": "rigor", + "rigours": "rigors", + "ritualised": "ritualized", + "rivalled": "rivaled", + "rivalling": "rivaling", + "romanticise": "romanticize", + "romanticised": "romanticized", + "romanticises": "romanticizes", + "romanticising": "romanticizing", + "rumour": "rumor", + "rumoured": "rumored", + "rumours": "rumors", + "sabre": "saber", + "sabres": "sabers", + "saltpetre": "saltpeter", + "sanitise": "sanitize", + "sanitised": "sanitized", + "sanitises": "sanitizes", + "sanitising": "sanitizing", + "satirise": "satirize", + "satirised": "satirized", + "satirises": "satirizes", + "satirising": "satirizing", + "saviour": "savior", + "saviours": "saviors", + "savour": "savor", + "savoured": "savored", + "savouries": "savories", + "savouring": "savoring", + "savours": "savors", + "savoury": "savory", + "scandalise": "scandalize", + "scandalised": "scandalized", + "scandalises": "scandalizes", + "scandalising": "scandalizing", + "sceptic": "skeptic", + "sceptical": "skeptical", + "sceptically": "skeptically", + "scepticism": "skepticism", + "sceptics": "skeptics", + "sceptre": "scepter", + "sceptres": "scepters", + "scrutinise": "scrutinize", + "scrutinised": "scrutinized", + "scrutinises": "scrutinizes", + "scrutinising": "scrutinizing", + "secularisation": "secularization", + "secularise": "secularize", + "secularised": "secularized", + "secularises": "secularizes", + "secularising": "secularizing", + "sensationalise": "sensationalize", + "sensationalised": "sensationalized", + "sensationalises": "sensationalizes", + "sensationalising": "sensationalizing", + "sensitise": "sensitize", + "sensitised": "sensitized", + "sensitises": "sensitizes", + "sensitising": "sensitizing", + "sentimentalise": "sentimentalize", + "sentimentalised": "sentimentalized", + "sentimentalises": "sentimentalizes", + "sentimentalising": "sentimentalizing", + "sepulchre": "sepulcher", + "sepulchres": "sepulchers", + "serialisation": "serialization", + "serialisations": "serializations", + "serialise": "serialize", + "serialised": "serialized", + "serialises": "serializes", + "serialising": "serializing", + "sermonise": "sermonize", + "sermonised": "sermonized", + "sermonises": "sermonizes", + "sermonising": "sermonizing", + "sheikh": "sheik", + "shovelled": "shoveled", + "shovelling": "shoveling", + "shrivelled": "shriveled", + "shrivelling": "shriveling", + "signalise": "signalize", + "signalised": "signalized", + "signalises": "signalizes", + "signalising": "signalizing", + "signalled": "signaled", + "signalling": "signaling", + "smoulder": "smolder", + "smouldered": "smoldered", + "smouldering": "smoldering", + "smoulders": "smolders", + "snivelled": "sniveled", + "snivelling": "sniveling", + "snorkelled": "snorkeled", + "snorkelling": "snorkeling", + "snowplough": "snowplow", + "snowploughs": "snowplow", + "socialisation": "socialization", + "socialise": "socialize", + "socialised": "socialized", + "socialises": "socializes", + "socialising": "socializing", + "sodomise": "sodomize", + "sodomised": "sodomized", + "sodomises": "sodomizes", + "sodomising": "sodomizing", + "solemnise": "solemnize", + "solemnised": "solemnized", + "solemnises": "solemnizes", + "solemnising": "solemnizing", + "sombre": "somber", + "specialisation": "specialization", + "specialisations": "specializations", + "specialise": "specialize", + "specialised": "specialized", + "specialises": "specializes", + "specialising": "specializing", + "spectre": "specter", + "spectres": "specters", + "spiralled": "spiraled", + "spiralling": "spiraling", + "splendour": "splendor", + "splendours": "splendors", + "squirrelled": "squirreled", + "squirrelling": "squirreling", + "stabilisation": "stabilization", + "stabilise": "stabilize", + "stabilised": "stabilized", + "stabiliser": "stabilizer", + "stabilisers": "stabilizers", + "stabilises": "stabilizes", + "stabilising": "stabilizing", + "standardisation": "standardization", + "standardise": "standardize", + "standardised": "standardized", + "standardises": "standardizes", + "standardising": "standardizing", + "stencilled": "stenciled", + "stencilling": "stenciling", + "sterilisation": "sterilization", + "sterilisations": "sterilizations", + "sterilise": "sterilize", + "sterilised": "sterilized", + "steriliser": "sterilizer", + "sterilisers": "sterilizers", + "sterilises": "sterilizes", + "sterilising": "sterilizing", + "stigmatisation": "stigmatization", + "stigmatise": "stigmatize", + "stigmatised": "stigmatized", + "stigmatises": "stigmatizes", + "stigmatising": "stigmatizing", + "storey": "story", + "storeys": "stories", + "subsidisation": "subsidization", + "subsidise": "subsidize", + "subsidised": "subsidized", + "subsidiser": "subsidizer", + "subsidisers": "subsidizers", + "subsidises": "subsidizes", + "subsidising": "subsidizing", + "succour": "succor", + "succoured": "succored", + "succouring": "succoring", + "succours": "succors", + "sulphate": "sulfate", + "sulphates": "sulfates", + "sulphide": "sulfide", + "sulphides": "sulfides", + "sulphur": "sulfur", + "sulphurous": "sulfurous", + "summarise": "summarize", + "summarised": "summarized", + "summarises": "summarizes", + "summarising": "summarizing", + "swivelled": "swiveled", + "swivelling": "swiveling", + "symbolise": "symbolize", + "symbolised": "symbolized", + "symbolises": "symbolizes", + "symbolising": "symbolizing", + "sympathise": "sympathize", + "sympathised": "sympathized", + "sympathiser": "sympathizer", + "sympathisers": "sympathizers", + "sympathises": "sympathizes", + "sympathising": "sympathizing", + "synchronisation": "synchronization", + "synchronise": "synchronize", + "synchronised": "synchronized", + "synchronises": "synchronizes", + "synchronising": "synchronizing", + "synthesise": "synthesize", + "synthesised": "synthesized", + "synthesiser": "synthesizer", + "synthesisers": "synthesizers", + "synthesises": "synthesizes", + "synthesising": "synthesizing", + "syphon": "siphon", + "syphoned": "siphoned", + "syphoning": "siphoning", + "syphons": "siphons", + "systematisation": "systematization", + "systematise": "systematize", + "systematised": "systematized", + "systematises": "systematizes", + "systematising": "systematizing", + "tantalise": "tantalize", + "tantalised": "tantalized", + "tantalises": "tantalizes", + "tantalising": "tantalizing", + "tantalisingly": "tantalizingly", + "tasselled": "tasseled", + "technicolour": "technicolor", + "temporise": "temporize", + "temporised": "temporized", + "temporises": "temporizes", + "temporising": "temporizing", + "tenderise": "tenderize", + "tenderised": "tenderized", + "tenderises": "tenderizes", + "tenderising": "tenderizing", + "terrorise": "terrorize", + "terrorised": "terrorized", + "terrorises": "terrorizes", + "terrorising": "terrorizing", + "theatre": "theater", + "theatregoer": "theatergoer", + "theatregoers": "theatergoers", + "theatres": "theaters", + "theorise": "theorize", + "theorised": "theorized", + "theorises": "theorizes", + "theorising": "theorizing", + "tonne": "ton", + "tonnes": "tons", + "towelled": "toweled", + "towelling": "toweling", + "toxaemia": "toxemia", + "tranquillise": "tranquilize", + "tranquillised": "tranquilized", + "tranquilliser": "tranquilizer", + "tranquillisers": "tranquilizers", + "tranquillises": "tranquilizes", + "tranquillising": "tranquilizing", + "tranquillity": "tranquility", + "tranquillize": "tranquilize", + "tranquillized": "tranquilized", + "tranquillizer": "tranquilizer", + "tranquillizers": "tranquilizers", + "tranquillizes": "tranquilizes", + "tranquillizing": "tranquilizing", + "tranquilly": "tranquility", + "transistorised": "transistorized", + "traumatise": "traumatize", + "traumatised": "traumatized", + "traumatises": "traumatizes", + "traumatising": "traumatizing", + "travelled": "traveled", + "traveller": "traveler", + "travellers": "travelers", + "travelling": "traveling", + "travelog": "travelogue", + "travelogs": "travelogues", + "trialled": "trialed", + "trialling": "trialing", + "tricolour": "tricolor", + "tricolours": "tricolors", + "trivialise": "trivialize", + "trivialised": "trivialized", + "trivialises": "trivializes", + "trivialising": "trivializing", + "tumour": "tumor", + "tumours": "tumors", + "tunnelled": "tunneled", + "tunnelling": "tunneling", + "tyrannise": "tyrannize", + "tyrannised": "tyrannized", + "tyrannises": "tyrannizes", + "tyrannising": "tyrannizing", + "tyre": "tire", + "tyres": "tires", + "unauthorised": "unauthorized", + "uncivilised": "uncivilized", + "underutilised": "underutilized", + "unequalled": "unequaled", + "unfavourable": "unfavorable", + "unfavourably": "unfavorably", + "unionisation": "unionization", + "unionise": "unionize", + "unionised": "unionized", + "unionises": "unionizes", + "unionising": "unionizing", + "unorganised": "unorganized", + "unravelled": "unraveled", + "unravelling": "unraveling", + "unrecognisable": "unrecognizable", + "unrecognised": "unrecognized", + "unrivalled": "unrivaled", + "unsavoury": "unsavory", + "untrammelled": "untrammeled", + "urbanisation": "urbanization", + "urbanise": "urbanize", + "urbanised": "urbanized", + "urbanises": "urbanizes", + "urbanising": "urbanizing", + "utilisable": "utilizable", + "utilisation": "utilization", + "utilise": "utilize", + "utilised": "utilized", + "utilises": "utilizes", + "utilising": "utilizing", + "valour": "valor", + "vandalise": "vandalize", + "vandalised": "vandalized", + "vandalises": "vandalizes", + "vandalising": "vandalizing", + "vaporisation": "vaporization", + "vaporise": "vaporize", + "vaporised": "vaporized", + "vaporises": "vaporizes", + "vaporising": "vaporizing", + "vapour": "vapor", + "vapours": "vapors", + "verbalise": "verbalize", + "verbalised": "verbalized", + "verbalises": "verbalizes", + "verbalising": "verbalizing", + "victimisation": "victimization", + "victimise": "victimize", + "victimised": "victimized", + "victimises": "victimizes", + "victimising": "victimizing", + "videodisc": "videodisk", + "videodiscs": "videodisks", + "vigour": "vigor", + "visualisation": "visualization", + "visualisations": "visualizations", + "visualise": "visualize", + "visualised": "visualized", + "visualises": "visualizes", + "visualising": "visualizing", + "vocalisation": "vocalization", + "vocalisations": "vocalizations", + "vocalise": "vocalize", + "vocalised": "vocalized", + "vocalises": "vocalizes", + "vocalising": "vocalizing", + "vulcanised": "vulcanized", + "vulgarisation": "vulgarization", + "vulgarise": "vulgarize", + "vulgarised": "vulgarized", + "vulgarises": "vulgarizes", + "vulgarising": "vulgarizing", + "waggon": "wagon", + "waggons": "wagons", + "watercolour": "watercolor", + "watercolours": "watercolors", + "weaselled": "weaseled", + "weaselling": "weaseling", + "westernisation": "westernization", + "westernise": "westernize", + "westernised": "westernized", + "westernises": "westernizes", + "westernising": "westernizing", + "womanise": "womanize", + "womanised": "womanized", + "womaniser": "womanizer", + "womanisers": "womanizers", + "womanises": "womanizes", + "womanising": "womanizing", + "woollen": "woolen", + "woollens": "woolens", + "woollies": "woolies", + "woolly": "wooly", + "worshipped": "worshiped", + "worshipping": "worshiping", + "worshipper": "worshiper", + "yodelled": "yodeled", + "yodelling": "yodeling", + "yoghourt": "yogurt", + "yoghourts": "yogurts", + "yoghurt": "yogurt", + "yoghurts": "yogurts", + "mhm": "hmm", + "mm": "hmm", + "mmm": "hmm" +} \ No newline at end of file diff --git a/src/models/whisper/whisper/normalizers/english.py b/src/models/whisper/whisper/normalizers/english.py new file mode 100644 index 0000000000000000000000000000000000000000..d5c2bb4ebef1a04b9ab14970c10c3fb0d4e948d4 --- /dev/null +++ b/src/models/whisper/whisper/normalizers/english.py @@ -0,0 +1,543 @@ +import json +import os +import re +from fractions import Fraction +from typing import Iterator, List, Match, Optional, Union + +from more_itertools import windowed + +from .basic import remove_symbols_and_diacritics + + +class EnglishNumberNormalizer: + """ + Convert any spelled-out numbers into arabic numbers, while handling: + + - remove any commas + - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc. + - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars` + - spell out `one` and `ones` + - interpret successive single-digit numbers as nominal: `one oh one` -> `101` + """ + + def __init__(self): + super().__init__() + + self.zeros = {"o", "oh", "zero"} + self.ones = { + name: i + for i, name in enumerate( + [ + "one", + "two", + "three", + "four", + "five", + "six", + "seven", + "eight", + "nine", + "ten", + "eleven", + "twelve", + "thirteen", + "fourteen", + "fifteen", + "sixteen", + "seventeen", + "eighteen", + "nineteen", + ], + start=1, + ) + } + self.ones_plural = { + "sixes" if name == "six" else name + "s": (value, "s") + for name, value in self.ones.items() + } + self.ones_ordinal = { + "zeroth": (0, "th"), + "first": (1, "st"), + "second": (2, "nd"), + "third": (3, "rd"), + "fifth": (5, "th"), + "twelfth": (12, "th"), + **{ + name + ("h" if name.endswith("t") else "th"): (value, "th") + for name, value in self.ones.items() + if value > 3 and value != 5 and value != 12 + }, + } + self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal} + + self.tens = { + "twenty": 20, + "thirty": 30, + "forty": 40, + "fifty": 50, + "sixty": 60, + "seventy": 70, + "eighty": 80, + "ninety": 90, + } + self.tens_plural = { + name.replace("y", "ies"): (value, "s") for name, value in self.tens.items() + } + self.tens_ordinal = { + name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items() + } + self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal} + + self.multipliers = { + "hundred": 100, + "thousand": 1_000, + "million": 1_000_000, + "billion": 1_000_000_000, + "trillion": 1_000_000_000_000, + "quadrillion": 1_000_000_000_000_000, + "quintillion": 1_000_000_000_000_000_000, + "sextillion": 1_000_000_000_000_000_000_000, + "septillion": 1_000_000_000_000_000_000_000_000, + "octillion": 1_000_000_000_000_000_000_000_000_000, + "nonillion": 1_000_000_000_000_000_000_000_000_000_000, + "decillion": 1_000_000_000_000_000_000_000_000_000_000_000, + } + self.multipliers_plural = { + name + "s": (value, "s") for name, value in self.multipliers.items() + } + self.multipliers_ordinal = { + name + "th": (value, "th") for name, value in self.multipliers.items() + } + self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal} + self.decimals = {*self.ones, *self.tens, *self.zeros} + + self.preceding_prefixers = { + "minus": "-", + "negative": "-", + "plus": "+", + "positive": "+", + } + self.following_prefixers = { + "pound": "£", + "pounds": "£", + "euro": "€", + "euros": "€", + "dollar": "$", + "dollars": "$", + "cent": "¢", + "cents": "¢", + } + self.prefixes = set( + list(self.preceding_prefixers.values()) + list(self.following_prefixers.values()) + ) + self.suffixers = { + "per": {"cent": "%"}, + "percent": "%", + } + self.specials = {"and", "double", "triple", "point"} + + self.words = set( + [ + key + for mapping in [ + self.zeros, + self.ones, + self.ones_suffixed, + self.tens, + self.tens_suffixed, + self.multipliers, + self.multipliers_suffixed, + self.preceding_prefixers, + self.following_prefixers, + self.suffixers, + self.specials, + ] + for key in mapping + ] + ) + self.literal_words = {"one", "ones"} + + def process_words(self, words: List[str]) -> Iterator[str]: + prefix: Optional[str] = None + value: Optional[Union[str, int]] = None + skip = False + + def to_fraction(s: str): + try: + return Fraction(s) + except ValueError: + return None + + def output(result: Union[str, int]): + nonlocal prefix, value + result = str(result) + if prefix is not None: + result = prefix + result + value = None + prefix = None + return result + + if len(words) == 0: + return + + for prev, current, next in windowed([None] + words + [None], 3): + if skip: + skip = False + continue + + next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next) + has_prefix = current[0] in self.prefixes + current_without_prefix = current[1:] if has_prefix else current + if re.match(r"^\d+(\.\d+)?$", current_without_prefix): + # arabic numbers (potentially with signs and fractions) + f = to_fraction(current_without_prefix) + assert f is not None + if value is not None: + if isinstance(value, str) and value.endswith("."): + # concatenate decimals / ip address components + value = str(value) + str(current) + continue + else: + yield output(value) + + prefix = current[0] if has_prefix else prefix + if f.denominator == 1: + value = f.numerator # store integers as int + else: + value = current_without_prefix + elif current not in self.words: + # non-numeric words + if value is not None: + yield output(value) + yield output(current) + elif current in self.zeros: + value = str(value or "") + "0" + elif current in self.ones: + ones = self.ones[current] + + if value is None: + value = ones + elif isinstance(value, str) or prev in self.ones: + if prev in self.tens and ones < 10: # replace the last zero with the digit + assert value[-1] == "0" + value = value[:-1] + str(ones) + else: + value = str(value) + str(ones) + elif ones < 10: + if value % 10 == 0: + value += ones + else: + value = str(value) + str(ones) + else: # eleven to nineteen + if value % 100 == 0: + value += ones + else: + value = str(value) + str(ones) + elif current in self.ones_suffixed: + # ordinal or cardinal; yield the number right away + ones, suffix = self.ones_suffixed[current] + if value is None: + yield output(str(ones) + suffix) + elif isinstance(value, str) or prev in self.ones: + if prev in self.tens and ones < 10: + assert value[-1] == "0" + yield output(value[:-1] + str(ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + elif ones < 10: + if value % 10 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + else: # eleven to nineteen + if value % 100 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + value = None + elif current in self.tens: + tens = self.tens[current] + if value is None: + value = tens + elif isinstance(value, str): + value = str(value) + str(tens) + else: + if value % 100 == 0: + value += tens + else: + value = str(value) + str(tens) + elif current in self.tens_suffixed: + # ordinal or cardinal; yield the number right away + tens, suffix = self.tens_suffixed[current] + if value is None: + yield output(str(tens) + suffix) + elif isinstance(value, str): + yield output(str(value) + str(tens) + suffix) + else: + if value % 100 == 0: + yield output(str(value + tens) + suffix) + else: + yield output(str(value) + str(tens) + suffix) + elif current in self.multipliers: + multiplier = self.multipliers[current] + if value is None: + value = multiplier + elif isinstance(value, str) or value == 0: + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + value = p.numerator + else: + yield output(value) + value = multiplier + else: + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + elif current in self.multipliers_suffixed: + multiplier, suffix = self.multipliers_suffixed[current] + if value is None: + yield output(str(multiplier) + suffix) + elif isinstance(value, str): + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + yield output(str(p.numerator) + suffix) + else: + yield output(value) + yield output(str(multiplier) + suffix) + else: # int + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + yield output(str(value) + suffix) + value = None + elif current in self.preceding_prefixers: + # apply prefix (positive, minus, etc.) if it precedes a number + if value is not None: + yield output(value) + + if next in self.words or next_is_numeric: + prefix = self.preceding_prefixers[current] + else: + yield output(current) + elif current in self.following_prefixers: + # apply prefix (dollars, cents, etc.) only after a number + if value is not None: + prefix = self.following_prefixers[current] + yield output(value) + else: + yield output(current) + elif current in self.suffixers: + # apply suffix symbols (percent -> '%') + if value is not None: + suffix = self.suffixers[current] + if isinstance(suffix, dict): + if next in suffix: + yield output(str(value) + suffix[next]) + skip = True + else: + yield output(value) + yield output(current) + else: + yield output(str(value) + suffix) + else: + yield output(current) + elif current in self.specials: + if next not in self.words and not next_is_numeric: + # apply special handling only if the next word can be numeric + if value is not None: + yield output(value) + yield output(current) + elif current == "and": + # ignore "and" after hundreds, thousands, etc. + if prev not in self.multipliers: + if value is not None: + yield output(value) + yield output(current) + elif current == "double" or current == "triple": + if next in self.ones or next in self.zeros: + repeats = 2 if current == "double" else 3 + ones = self.ones.get(next, 0) + value = str(value or "") + str(ones) * repeats + skip = True + else: + if value is not None: + yield output(value) + yield output(current) + elif current == "point": + if next in self.decimals or next_is_numeric: + value = str(value or "") + "." + else: + # should all have been covered at this point + raise ValueError(f"Unexpected token: {current}") + else: + # all should have been covered at this point + raise ValueError(f"Unexpected token: {current}") + + if value is not None: + yield output(value) + + def preprocess(self, s: str): + # replace " and a half" with " point five" + results = [] + + segments = re.split(r"\band\s+a\s+half\b", s) + for i, segment in enumerate(segments): + if len(segment.strip()) == 0: + continue + if i == len(segments) - 1: + results.append(segment) + else: + results.append(segment) + last_word = segment.rsplit(maxsplit=2)[-1] + if last_word in self.decimals or last_word in self.multipliers: + results.append("point five") + else: + results.append("and a half") + + s = " ".join(results) + + # put a space at number/letter boundary + s = re.sub(r"([a-z])([0-9])", r"\1 \2", s) + s = re.sub(r"([0-9])([a-z])", r"\1 \2", s) + + # but remove spaces which could be a suffix + s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s) + + return s + + def postprocess(self, s: str): + def combine_cents(m: Match): + try: + currency = m.group(1) + integer = m.group(2) + cents = int(m.group(3)) + return f"{currency}{integer}.{cents:02d}" + except ValueError: + return m.string + + def extract_cents(m: Match): + try: + return f"¢{int(m.group(1))}" + except ValueError: + return m.string + + # apply currency postprocessing; "$2 and ¢7" -> "$2.07" + s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s) + s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s) + + # write "one(s)" instead of "1(s)", just for the readability + s = re.sub(r"\b1(s?)\b", r"one\1", s) + + return s + + def __call__(self, s: str): + s = self.preprocess(s) + s = " ".join(word for word in self.process_words(s.split()) if word is not None) + s = self.postprocess(s) + + return s + + +class EnglishSpellingNormalizer: + """ + Applies British-American spelling mappings as listed in [1]. + + [1] https://www.tysto.com/uk-us-spelling-list.html + """ + + def __init__(self): + mapping_path = os.path.join(os.path.dirname(__file__), "english.json") + self.mapping = json.load(open(mapping_path)) + + def __call__(self, s: str): + return " ".join(self.mapping.get(word, word) for word in s.split()) + + +class EnglishTextNormalizer: + def __init__(self): + self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b" + self.replacers = { + # common contractions + r"\bwon't\b": "will not", + r"\bcan't\b": "can not", + r"\blet's\b": "let us", + r"\bain't\b": "aint", + r"\by'all\b": "you all", + r"\bwanna\b": "want to", + r"\bgotta\b": "got to", + r"\bgonna\b": "going to", + r"\bi'ma\b": "i am going to", + r"\bimma\b": "i am going to", + r"\bwoulda\b": "would have", + r"\bcoulda\b": "could have", + r"\bshoulda\b": "should have", + r"\bma'am\b": "madam", + # contractions in titles/prefixes + r"\bmr\b": "mister ", + r"\bmrs\b": "missus ", + r"\bst\b": "saint ", + r"\bdr\b": "doctor ", + r"\bprof\b": "professor ", + r"\bcapt\b": "captain ", + r"\bgov\b": "governor ", + r"\bald\b": "alderman ", + r"\bgen\b": "general ", + r"\bsen\b": "senator ", + r"\brep\b": "representative ", + r"\bpres\b": "president ", + r"\brev\b": "reverend ", + r"\bhon\b": "honorable ", + r"\basst\b": "assistant ", + r"\bassoc\b": "associate ", + r"\blt\b": "lieutenant ", + r"\bcol\b": "colonel ", + r"\bjr\b": "junior ", + r"\bsr\b": "senior ", + r"\besq\b": "esquire ", + # prefect tenses, ideally it should be any past participles, but it's harder.. + r"'d been\b": " had been", + r"'s been\b": " has been", + r"'d gone\b": " had gone", + r"'s gone\b": " has gone", + r"'d done\b": " had done", # "'s done" is ambiguous + r"'s got\b": " has got", + # general contractions + r"n't\b": " not", + r"'re\b": " are", + r"'s\b": " is", + r"'d\b": " would", + r"'ll\b": " will", + r"'t\b": " not", + r"'ve\b": " have", + r"'m\b": " am", + } + self.standardize_numbers = EnglishNumberNormalizer() + self.standardize_spellings = EnglishSpellingNormalizer() + + def __call__(self, s: str): + s = s.lower() + + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = re.sub(self.ignore_patterns, "", s) + s = re.sub(r"\s+'", "'", s) # standardize when there's a space before an apostrophe + + for pattern, replacement in self.replacers.items(): + s = re.sub(pattern, replacement, s) + + s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits + s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers + s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep some symbols for numerics + + s = self.standardize_numbers(s) + s = self.standardize_spellings(s) + + # now remove prefix/suffix symbols that are not preceded/followed by numbers + s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s) + s = re.sub(r"([^0-9])%", r"\1 ", s) + + s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space + + return s diff --git a/src/models/whisper/whisper/tokenizer.py b/src/models/whisper/whisper/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..88142c43c9ca0d951a36b5b30baf54dd7bc7efbf --- /dev/null +++ b/src/models/whisper/whisper/tokenizer.py @@ -0,0 +1,331 @@ +import os +from dataclasses import dataclass +from functools import lru_cache +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from transformers import GPT2TokenizerFast + +LANGUAGES = { + "en": "english", + "zh": "chinese", + "de": "german", + "es": "spanish", + "ru": "russian", + "ko": "korean", + "fr": "french", + "ja": "japanese", + "pt": "portuguese", + "tr": "turkish", + "pl": "polish", + "ca": "catalan", + "nl": "dutch", + "ar": "arabic", + "sv": "swedish", + "it": "italian", + "id": "indonesian", + "hi": "hindi", + "fi": "finnish", + "vi": "vietnamese", + "iw": "hebrew", + "uk": "ukrainian", + "el": "greek", + "ms": "malay", + "cs": "czech", + "ro": "romanian", + "da": "danish", + "hu": "hungarian", + "ta": "tamil", + "no": "norwegian", + "th": "thai", + "ur": "urdu", + "hr": "croatian", + "bg": "bulgarian", + "lt": "lithuanian", + "la": "latin", + "mi": "maori", + "ml": "malayalam", + "cy": "welsh", + "sk": "slovak", + "te": "telugu", + "fa": "persian", + "lv": "latvian", + "bn": "bengali", + "sr": "serbian", + "az": "azerbaijani", + "sl": "slovenian", + "kn": "kannada", + "et": "estonian", + "mk": "macedonian", + "br": "breton", + "eu": "basque", + "is": "icelandic", + "hy": "armenian", + "ne": "nepali", + "mn": "mongolian", + "bs": "bosnian", + "kk": "kazakh", + "sq": "albanian", + "sw": "swahili", + "gl": "galician", + "mr": "marathi", + "pa": "punjabi", + "si": "sinhala", + "km": "khmer", + "sn": "shona", + "yo": "yoruba", + "so": "somali", + "af": "afrikaans", + "oc": "occitan", + "ka": "georgian", + "be": "belarusian", + "tg": "tajik", + "sd": "sindhi", + "gu": "gujarati", + "am": "amharic", + "yi": "yiddish", + "lo": "lao", + "uz": "uzbek", + "fo": "faroese", + "ht": "haitian creole", + "ps": "pashto", + "tk": "turkmen", + "nn": "nynorsk", + "mt": "maltese", + "sa": "sanskrit", + "lb": "luxembourgish", + "my": "myanmar", + "bo": "tibetan", + "tl": "tagalog", + "mg": "malagasy", + "as": "assamese", + "tt": "tatar", + "haw": "hawaiian", + "ln": "lingala", + "ha": "hausa", + "ba": "bashkir", + "jw": "javanese", + "su": "sundanese", +} + +# language code lookup by name, with a few language aliases +TO_LANGUAGE_CODE = { + **{language: code for code, language in LANGUAGES.items()}, + "burmese": "my", + "valencian": "ca", + "flemish": "nl", + "haitian": "ht", + "letzeburgesch": "lb", + "pushto": "ps", + "panjabi": "pa", + "moldavian": "ro", + "moldovan": "ro", + "sinhalese": "si", + "castilian": "es", +} + + +@dataclass(frozen=True) +class Tokenizer: + """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens""" + + tokenizer: "GPT2TokenizerFast" + language: Optional[str] + sot_sequence: Tuple[int] + + def encode(self, text, **kwargs): + return self.tokenizer.encode(text, **kwargs) + + def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs): + return self.tokenizer.decode(token_ids, **kwargs) + + def decode_with_timestamps(self, tokens) -> str: + """ + Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. + This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". + """ + outputs = [[]] + for token in tokens: + if token >= self.timestamp_begin: + timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>" + outputs.append(timestamp) + outputs.append([]) + else: + outputs[-1].append(token) + outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs] + return "".join(outputs) + + @property + @lru_cache() + def eot(self) -> int: + return self.tokenizer.eos_token_id + + @property + @lru_cache() + def sot(self) -> int: + return self._get_single_token_id("<|startoftranscript|>") + + @property + @lru_cache() + def sot_lm(self) -> int: + return self._get_single_token_id("<|startoflm|>") + + @property + @lru_cache() + def sot_prev(self) -> int: + return self._get_single_token_id("<|startofprev|>") + + @property + @lru_cache() + def no_speech(self) -> int: + return self._get_single_token_id("<|nospeech|>") + + @property + @lru_cache() + def no_timestamps(self) -> int: + return self._get_single_token_id("<|notimestamps|>") + + @property + @lru_cache() + def timestamp_begin(self) -> int: + return self.tokenizer.all_special_ids[-1] + 1 + + @property + @lru_cache() + def language_token(self) -> int: + """Returns the token id corresponding to the value of the `language` field""" + if self.language is None: + raise ValueError(f"This tokenizer does not have language token configured") + + additional_tokens = dict( + zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ) + ) + candidate = f"<|{self.language}|>" + if candidate in additional_tokens: + return additional_tokens[candidate] + + raise KeyError(f"Language {self.language} not found in tokenizer.") + + @property + @lru_cache() + def all_language_tokens(self) -> Tuple[int]: + result = [] + for token, token_id in zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ): + if token.strip("<|>") in LANGUAGES: + result.append(token_id) + return tuple(result) + + @property + @lru_cache() + def all_language_codes(self) -> Tuple[str]: + return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) + + @property + @lru_cache() + def sot_sequence_including_notimestamps(self) -> Tuple[int]: + return tuple(list(self.sot_sequence) + [self.no_timestamps]) + + @property + @lru_cache() + def non_speech_tokens(self) -> Tuple[int]: + """ + Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech + annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. + + - ♪♪♪ + - ( SPEAKING FOREIGN LANGUAGE ) + - [DAVID] Hey there, + + keeping basic punctuations like commas, periods, question marks, exclamation points, etc. + """ + symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』") + symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() + + # symbols that may be a single token or multiple tokens depending on the tokenizer. + # In case they're multiple tokens, suppress the first token, which is safe because: + # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress + # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. + miscellaneous = set("♩♪♫♬♭♮♯") + assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) + + # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word + result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]} + for symbol in symbols + list(miscellaneous): + for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]: + if len(tokens) == 1 or symbol in miscellaneous: + result.add(tokens[0]) + + return tuple(sorted(result)) + + def _get_single_token_id(self, text) -> int: + tokens = self.tokenizer.encode(text) + assert len(tokens) == 1, f"{text} is not encoded as a single token" + return tokens[0] + + +@lru_cache(maxsize=None) +def build_tokenizer(name: str = "gpt2"): + os.environ["TOKENIZERS_PARALLELISM"] = "false" + path = os.path.join(os.path.dirname(__file__), "assets", name) + tokenizer = GPT2TokenizerFast.from_pretrained(path) + + specials = [ + "<|startoftranscript|>", + *[f"<|{lang}|>" for lang in LANGUAGES.keys()], + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nospeech|>", + "<|notimestamps|>", + ] + + tokenizer.add_special_tokens(dict(additional_special_tokens=specials)) + return tokenizer + + +@lru_cache(maxsize=None) +def get_tokenizer( + multilingual: bool, + *, + task: Optional[str] = None, # Literal["transcribe", "translate", None] + language: Optional[str] = None, +) -> Tokenizer: + if language is not None: + language = language.lower() + if language not in LANGUAGES: + if language in TO_LANGUAGE_CODE: + language = TO_LANGUAGE_CODE[language] + else: + raise ValueError(f"Unsupported language: {language}") + + if multilingual: + tokenizer_name = "multilingual" + task = task or "transcribe" + language = language or "en" + else: + tokenizer_name = "gpt2" + task = None + language = None + + tokenizer = build_tokenizer(name=tokenizer_name) + all_special_ids: List[int] = tokenizer.all_special_ids + sot: int = all_special_ids[1] + translate: int = all_special_ids[-6] + transcribe: int = all_special_ids[-5] + + langs = tuple(LANGUAGES.keys()) + sot_sequence = [sot] + if language is not None: + sot_sequence.append(sot + 1 + langs.index(language)) + if task is not None: + sot_sequence.append(transcribe if task == "transcribe" else translate) + + return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence)) diff --git a/src/models/whisper/whisper/transcribe.py b/src/models/whisper/whisper/transcribe.py new file mode 100644 index 0000000000000000000000000000000000000000..745e77554b90c63db69ce7da40156676c7a990de --- /dev/null +++ b/src/models/whisper/whisper/transcribe.py @@ -0,0 +1,207 @@ +import argparse +import os +import warnings +from typing import List, Optional, Tuple, Union, TYPE_CHECKING + +import numpy as np +import torch +import tqdm + +from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram +from .decoding import DecodingOptions, DecodingResult +from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer +from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_txt, write_vtt, write_srt + +if TYPE_CHECKING: + from .model import Whisper + + +def transcribe( + model: "Whisper", + audio: Union[str, np.ndarray, torch.Tensor], + *, + verbose: Optional[bool] = None, + temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), + compression_ratio_threshold: Optional[float] = 2.4, + logprob_threshold: Optional[float] = -1.0, + no_speech_threshold: Optional[float] = 0.6, + condition_on_previous_text: bool = True, + force_extraction: bool = False, + **decode_options, +): + """ + Transcribe an audio file using Whisper + + Parameters + ---------- + model: Whisper + The Whisper model instance + + audio: Union[str, np.ndarray, torch.Tensor] + The path to the audio file to open, or the audio waveform + + verbose: bool + Whether to display the text being decoded to the console. If True, displays all the details, + If False, displays minimal details. If None, does not display anything + + temperature: Union[float, Tuple[float, ...]] + Temperature for sampling. It can be a tuple of temperatures, which will be successfully used + upon failures according to either `compression_ratio_threshold` or `logprob_threshold`. + + compression_ratio_threshold: float + If the gzip compression ratio is above this value, treat as failed + + logprob_threshold: float + If the average log probability over sampled tokens is below this value, treat as failed + + no_speech_threshold: float + If the no_speech probability is higher than this value AND the average log probability + over sampled tokens is below `logprob_threshold`, consider the segment as silent + + condition_on_previous_text: bool + if True, the previous output of the model is provided as a prompt for the next window; + disabling may make the text inconsistent across windows, but the model becomes less prone to + getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. + + decode_options: dict + Keyword arguments to construct `DecodingOptions` instances + + Returns + ------- + A dictionary containing the resulting text ("text") and segment-level details ("segments"), and + the spoken language ("language"), which is detected when `decode_options["language"]` is None. + """ + dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32 + if model.device == torch.device("cpu"): + if torch.cuda.is_available(): + warnings.warn("Performing inference on CPU when CUDA is available") + if dtype == torch.float16: + warnings.warn("FP16 is not supported on CPU; using FP32 instead") + dtype = torch.float32 + + if dtype == torch.float32: + decode_options["fp16"] = False + + mel = log_mel_spectrogram(audio) + + all_segments = [] + def add_segment( + *, start: float, end: float, encoder_embeddings + ): + + all_segments.append( + { + "start": start, + "end": end, + "encoder_embeddings":encoder_embeddings, + } + ) + # show the progress bar when verbose is False (otherwise the transcribed text will be printed) + num_frames = mel.shape[-1] + seek = 0 + previous_seek_value = seek + sample_skip = 3000 # + with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar: + while seek < num_frames: + # seek是开始的帧数 + end_seek = min(seek + sample_skip, num_frames) + segment = pad_or_trim(mel[:,seek:seek+sample_skip], N_FRAMES).to(model.device).to(dtype) + + single = segment.ndim == 2 + if single: + segment = segment.unsqueeze(0) + if dtype == torch.float16: + segment = segment.half() + audio_features, embeddings = model.encoder(segment, include_embeddings = True) + + encoder_embeddings = embeddings + #print(f"encoder_embeddings shape {encoder_embeddings.shape}") + add_segment( + start=seek, + end=end_seek, + #text_tokens=tokens, + #result=result, + encoder_embeddings=encoder_embeddings, + ) + seek+=sample_skip + + return dict(segments=all_segments) + + +def cli(): + from . import available_models + + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe") + parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use") + parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default") + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference") + parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs") + parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages") + + parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')") + parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection") + + parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling") + parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature") + parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero") + parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search") + parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default") + + parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations") + parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.") + parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop") + parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default") + + parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below") + parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed") + parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed") + parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence") + parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") + + args = parser.parse_args().__dict__ + model_name: str = args.pop("model") + model_dir: str = args.pop("model_dir") + output_dir: str = args.pop("output_dir") + device: str = args.pop("device") + os.makedirs(output_dir, exist_ok=True) + + if model_name.endswith(".en") and args["language"] not in {"en", "English"}: + if args["language"] is not None: + warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.") + args["language"] = "en" + + temperature = args.pop("temperature") + temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback") + if temperature_increment_on_fallback is not None: + temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback)) + else: + temperature = [temperature] + + threads = args.pop("threads") + if threads > 0: + torch.set_num_threads(threads) + + from . import load_model + model = load_model(model_name, device=device, download_root=model_dir) + + for audio_path in args.pop("audio"): + result = transcribe(model, audio_path, temperature=temperature, **args) + + audio_basename = os.path.basename(audio_path) + + # save TXT + with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt: + write_txt(result["segments"], file=txt) + + # save VTT + with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt: + write_vtt(result["segments"], file=vtt) + + # save SRT + with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt: + write_srt(result["segments"], file=srt) + + +if __name__ == '__main__': + cli() diff --git a/src/models/whisper/whisper/utils.py b/src/models/whisper/whisper/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..87c91f2e969c4d2ebfc311266bced76a52cb3ef3 --- /dev/null +++ b/src/models/whisper/whisper/utils.py @@ -0,0 +1,87 @@ +import zlib +from typing import Iterator, TextIO + + +def exact_div(x, y): + assert x % y == 0 + return x // y + + +def str2bool(string): + str2val = {"True": True, "False": False} + if string in str2val: + return str2val[string] + else: + raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") + + +def optional_int(string): + return None if string == "None" else int(string) + + +def optional_float(string): + return None if string == "None" else float(string) + + +def compression_ratio(text) -> float: + return len(text) / len(zlib.compress(text.encode("utf-8"))) + + +def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'): + assert seconds >= 0, "non-negative timestamp expected" + milliseconds = round(seconds * 1000.0) + + hours = milliseconds // 3_600_000 + milliseconds -= hours * 3_600_000 + + minutes = milliseconds // 60_000 + milliseconds -= minutes * 60_000 + + seconds = milliseconds // 1_000 + milliseconds -= seconds * 1_000 + + hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" + return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" + + +def write_txt(transcript: Iterator[dict], file: TextIO): + for segment in transcript: + print(segment['text'].strip(), file=file, flush=True) + + +def write_vtt(transcript: Iterator[dict], file: TextIO): + print("WEBVTT\n", file=file) + for segment in transcript: + print( + f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) + + +def write_srt(transcript: Iterator[dict], file: TextIO): + """ + Write a transcript to a file in SRT format. + + Example usage: + from pathlib import Path + from whisper.utils import write_srt + + result = transcribe(model, audio_path, temperature=temperature, **args) + + # save SRT + audio_basename = Path(audio_path).stem + with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt: + write_srt(result["segments"], file=srt) + """ + for i, segment in enumerate(transcript, start=1): + # write srt lines + print( + f"{i}\n" + f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " + f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) diff --git a/src/pipelines/__init__.py b/src/pipelines/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/pipelines/context.py b/src/pipelines/context.py new file mode 100644 index 0000000000000000000000000000000000000000..c00274c8861b5fae3b86af437b5f14998045a5dc --- /dev/null +++ b/src/pipelines/context.py @@ -0,0 +1,76 @@ +# TODO: Adapted from cli +from typing import Callable, List, Optional + +import numpy as np + + +def ordered_halving(val): + bin_str = f"{val:064b}" + bin_flip = bin_str[::-1] + as_int = int(bin_flip, 2) + + return as_int / (1 << 64) + + +def uniform( + step: int = ..., + num_steps: Optional[int] = None, + num_frames: int = ..., + context_size: Optional[int] = None, + context_stride: int = 3, + context_overlap: int = 4, + closed_loop: bool = True, +): + if num_frames <= context_size: + yield list(range(num_frames)) + return + + context_stride = min( + context_stride, int(np.ceil(np.log2(num_frames / context_size))) + 1 + ) + + for context_step in 1 << np.arange(context_stride): + pad = int(round(num_frames * ordered_halving(step))) + for j in range( + int(ordered_halving(step) * context_step) + pad, + num_frames + pad + (0 if closed_loop else -context_overlap), + (context_size * context_step - context_overlap), + ): + yield [ + e % num_frames + for e in range(j, j + context_size * context_step, context_step) + ] + + +def get_context_scheduler(name: str) -> Callable: + if name == "uniform": + return uniform + else: + raise ValueError(f"Unknown context_overlap policy {name}") + + +def get_total_steps( + scheduler, + timesteps: List[int], + num_steps: Optional[int] = None, + num_frames: int = ..., + context_size: Optional[int] = None, + context_stride: int = 3, + context_overlap: int = 4, + closed_loop: bool = True, +): + return sum( + len( + list( + scheduler( + i, + num_steps, + num_frames, + context_size, + context_stride, + context_overlap, + ) + ) + ) + for i in range(len(timesteps)) + ) diff --git a/src/pipelines/pipeline_echo_mimic.py b/src/pipelines/pipeline_echo_mimic.py new file mode 100644 index 0000000000000000000000000000000000000000..6dc6689cfd7add861fa3a3f0ad59458b2c35201e --- /dev/null +++ b/src/pipelines/pipeline_echo_mimic.py @@ -0,0 +1,586 @@ +#!/usr/bin/env python +# -*- coding: UTF-8 -*- +''' +@Project :EchoMimic +@File :pipeline_echo_mimic.py +@Author :juzhen.czy +@Date :2024/3/4 17:44 +''' +# Adapted from https://github.com/magic-research/magic-animate/blob/main/magicanimate/pipelines/pipeline_animation.py +import inspect +import math +from dataclasses import dataclass +from typing import Callable, List, Optional, Union + +import numpy as np +import torch +from diffusers import DiffusionPipeline +import torch.nn.functional as F +from diffusers.image_processor import VaeImageProcessor +from diffusers.schedulers import ( + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, +) +from diffusers.utils import BaseOutput, deprecate, is_accelerate_available, logging +from diffusers.utils.torch_utils import randn_tensor +from einops import rearrange +from tqdm import tqdm +from transformers import CLIPImageProcessor + +from src.models.mutual_self_attention import ReferenceAttentionControl +from src.pipelines.context import get_context_scheduler +from src.pipelines.utils import get_tensor_interpolation_method + +@dataclass +class Audio2VideoPipelineOutput(BaseOutput): + videos: Union[torch.Tensor, np.ndarray] + + +class Audio2VideoPipeline(DiffusionPipeline): + _optional_components = [] + + def __init__( + self, + vae, + reference_unet, + denoising_unet, + audio_guider, + face_locator, + scheduler: Union[ + DDIMScheduler, + PNDMScheduler, + LMSDiscreteScheduler, + EulerDiscreteScheduler, + EulerAncestralDiscreteScheduler, + DPMSolverMultistepScheduler, + ], + image_proj_model=None, + tokenizer=None, + text_encoder=None, + ): + super().__init__() + + self.register_modules( + vae=vae, + reference_unet=reference_unet, + denoising_unet=denoising_unet, + audio_guider=audio_guider, + face_locator=face_locator, + scheduler=scheduler, + image_proj_model=image_proj_model, + tokenizer=tokenizer, + text_encoder=text_encoder, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.clip_image_processor = CLIPImageProcessor() + self.ref_image_processor = VaeImageProcessor( + vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True + ) + + def enable_vae_slicing(self): + self.vae.enable_slicing() + + def disable_vae_slicing(self): + self.vae.disable_slicing() + + def enable_sequential_cpu_offload(self, gpu_id=0): + if is_accelerate_available(): + from accelerate import cpu_offload + else: + raise ImportError("Please install accelerate via `pip install accelerate`") + + device = torch.device(f"cuda:{gpu_id}") + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + if cpu_offloaded_model is not None: + cpu_offload(cpu_offloaded_model, device) + + @property + def _execution_device(self): + if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def decode_latents(self, latents): + video_length = latents.shape[2] + latents = 1 / 0.18215 * latents + latents = rearrange(latents, "b c f h w -> (b f) c h w") + video = [] + for frame_idx in tqdm(range(latents.shape[0])): + video.append(self.vae.decode(latents[frame_idx : frame_idx + 1]).sample) + video = torch.cat(video) + video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length) + video = (video / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + video = video.cpu().float().numpy() + return video + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def prepare_latents( + self, + batch_size, + num_channels_latents, + width, + height, + video_length, + dtype, + device, + generator + ): + shape = ( + batch_size, + num_channels_latents, + # context_frame_length, + video_length, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + latents_seg = randn_tensor( + shape, generator=generator, device=device, dtype=dtype + ) + latents = latents_seg + latents = torch.clamp(latents, -1.5, 1.5) + latents = latents * self.scheduler.init_noise_sigma + return latents + + def _encode_prompt( + self, + prompt, + device, + num_videos_per_prompt, + do_classifier_free_guidance, + negative_prompt, + ): + batch_size = len(prompt) if isinstance(prompt, list) else 1 + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer( + prompt, padding="longest", return_tensors="pt" + ).input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + + if ( + hasattr(self.text_encoder.config, "use_attention_mask") + and self.text_encoder.config.use_attention_mask + ): + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + text_embeddings = self.text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + ) + text_embeddings = text_embeddings[0] + + # duplicate text embeddings for each generation per prompt, using mps friendly method + bs_embed, seq_len, _ = text_embeddings.shape + text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1) + text_embeddings = text_embeddings.view( + bs_embed * num_videos_per_prompt, seq_len, -1 + ) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = text_input_ids.shape[-1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if ( + hasattr(self.text_encoder.config, "use_attention_mask") + and self.text_encoder.config.use_attention_mask + ): + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + uncond_embeddings = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + uncond_embeddings = uncond_embeddings[0] + + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = uncond_embeddings.shape[1] + uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1) + uncond_embeddings = uncond_embeddings.view( + batch_size * num_videos_per_prompt, seq_len, -1 + ) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) + + return text_embeddings + + def interpolate_latents( + self, latents: torch.Tensor, interpolation_factor: int, device + ): + if interpolation_factor < 2: + return latents + + new_latents = torch.zeros( + ( + latents.shape[0], + latents.shape[1], + ((latents.shape[2] - 1) * interpolation_factor) + 1, + latents.shape[3], + latents.shape[4], + ), + device=latents.device, + dtype=latents.dtype, + ) + + org_video_length = latents.shape[2] + rate = [i / interpolation_factor for i in range(interpolation_factor)][1:] + + new_index = 0 + + v0 = None + v1 = None + + for i0, i1 in zip(range(org_video_length), range(org_video_length)[1:]): + v0 = latents[:, :, i0, :, :] + v1 = latents[:, :, i1, :, :] + + new_latents[:, :, new_index, :, :] = v0 + new_index += 1 + + for f in rate: + v = get_tensor_interpolation_method()( + v0.to(device=device), v1.to(device=device), f + ) + new_latents[:, :, new_index, :, :] = v.to(latents.device) + new_index += 1 + + new_latents[:, :, new_index, :, :] = v1 + new_index += 1 + + return new_latents + + @torch.no_grad() + def __call__( + self, + ref_image, + audio_path, + face_mask_tensor, + width, + height, + video_length, + num_inference_steps, + guidance_scale, + num_images_per_prompt=1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + output_type: Optional[str] = "tensor", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: Optional[int] = 1, + context_schedule="uniform", + context_frames=12, + context_stride=1, + context_overlap=0, + context_batch_size=1, + interpolation_factor=1, + audio_sample_rate=16000, + fps=25, + audio_margin=2, + **kwargs, + ): + # Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + device = self._execution_device + + do_classifier_free_guidance = guidance_scale > 1.0 + + # Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + batch_size = 1 + + reference_control_writer = ReferenceAttentionControl( + self.reference_unet, + do_classifier_free_guidance=do_classifier_free_guidance, + mode="write", + batch_size=batch_size, + fusion_blocks="full", + ) + reference_control_reader = ReferenceAttentionControl( + self.denoising_unet, + do_classifier_free_guidance=do_classifier_free_guidance, + mode="read", + batch_size=batch_size, + fusion_blocks="full", + ) + + whisper_feature = self.audio_guider.audio2feat(audio_path) + + whisper_chunks = self.audio_guider.feature2chunks(feature_array=whisper_feature, fps=fps) + + print("whisper_chunks:", whisper_chunks.shape) + audio_frame_num = whisper_chunks.shape[0] + audio_fea_final = torch.Tensor(whisper_chunks).to(dtype=self.vae.dtype, device=self.vae.device) + audio_fea_final = audio_fea_final.unsqueeze(0) + print("audio_fea_final:", audio_fea_final.shape) + video_length = min(video_length, audio_frame_num) + if video_length < audio_frame_num: + audio_fea_final = audio_fea_final[:, :video_length, :, :] + + num_channels_latents = self.denoising_unet.in_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + width, + height, + video_length, + audio_fea_final.dtype, + device, + generator + ) + # print(video_length, latents.shape) + face_locator_tensor = self.face_locator(face_mask_tensor) + uc_face_locator_tensor = torch.zeros_like(face_locator_tensor) + face_locator_tensor = torch.cat([uc_face_locator_tensor, face_locator_tensor], dim=0) + # Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # Prepare ref image latents + ref_image_tensor = self.ref_image_processor.preprocess( + ref_image, height=height, width=width + ) + ref_image_tensor = ref_image_tensor.to( + dtype=self.vae.dtype, device=self.vae.device + ) + ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean + ref_image_latents = ref_image_latents * 0.18215 # (b , 4, h, w) + + context_scheduler = get_context_scheduler(context_schedule) + + # denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + context_queue = list( + context_scheduler( + 0, + num_inference_steps, + latents.shape[2], + context_frames, + context_stride, + context_overlap, + ) + ) + print("ref_image_latents shape:", ref_image_latents.shape) + print("face_mask_tensor shape:", face_mask_tensor.shape) + print("face_locator_tensor shape:", face_locator_tensor.shape) + with self.progress_bar(total=num_inference_steps) as progress_bar: + for t_i, t in enumerate(timesteps): + noise_pred = torch.zeros( + ( + latents.shape[0] * (2 if do_classifier_free_guidance else 1), + *latents.shape[1:], + ), + device=latents.device, + dtype=latents.dtype, + ) + counter = torch.zeros( + (1, 1, latents.shape[2], 1, 1), + device=latents.device, + dtype=latents.dtype, + ) + + # 1. Forward reference image + if t_i == 0: + self.reference_unet( + ref_image_latents, + torch.zeros_like(t), + encoder_hidden_states=None, + return_dict=False, + ) + reference_control_reader.update(reference_control_writer, do_classifier_free_guidance=True) + + + num_context_batches = math.ceil(len(context_queue) / context_batch_size) + + global_context = [] + for j in range(num_context_batches): + global_context.append( + context_queue[ + j * context_batch_size : (j + 1) * context_batch_size + ] + ) + + for context in global_context: + new_context = [[0 for _ in range(len(context[c_j]))] for c_j in range(len(context))] + for c_j in range(len(context)): + for c_i in range(len(context[c_j])): + new_context[c_j][c_i] = (context[c_j][c_i] + t_i * 2) % video_length + + latent_model_input = ( + torch.cat([latents[:, :, c] for c in new_context]) + .to(device) + .repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1) + ) + audio_latents = torch.cat([audio_fea_final[:, c] for c in new_context]).to(device) + audio_latents = torch.cat([torch.zeros_like(audio_latents), audio_latents], 0) + + latent_model_input = self.scheduler.scale_model_input( + latent_model_input, t + ) + pred = self.denoising_unet( + latent_model_input, + t, + encoder_hidden_states=None, + audio_cond_fea=audio_latents, + face_musk_fea=face_locator_tensor, + return_dict=False, + )[0] + + for j, c in enumerate(new_context): + noise_pred[:, :, c] = noise_pred[:, :, c] + pred + counter[:, :, c] = counter[:, :, c] + 1 + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = (noise_pred / counter).chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + latents = self.scheduler.step( + noise_pred, t, latents, **extra_step_kwargs + ).prev_sample + + if t_i == len(timesteps) - 1 or ( + (t_i + 1) > num_warmup_steps and (t_i + 1) % self.scheduler.order == 0 + ): + progress_bar.update() + + reference_control_reader.clear() + reference_control_writer.clear() + + if interpolation_factor > 0: + latents = self.interpolate_latents(latents, interpolation_factor, device) + # Post-processing + images = self.decode_latents(latents) # (b, c, f, h, w) + + # Convert to tensor + if output_type == "tensor": + images = torch.from_numpy(images) + + if not return_dict: + return images + + return Audio2VideoPipelineOutput(videos=images) + + def smooth_f_axis(self, tensor, smoothing_coef=0.2): + """ + 对5维tensor的F轴进行平滑,首尾帧保持原始值,其他帧应用平滑操作。 + + 参数: + tensor -- 输入的5D Tensor,形状为(B, C, F, W, H) + smoothing_coef -- 平滑前后帧的系数,默认为0.3 + + 返回: + smoothed_tensor -- 平滑后的5D Tensor + """ + # 生成平滑核 + weight_kernel = torch.tensor([smoothing_coef, 1 - 2 * smoothing_coef, smoothing_coef], + device=tensor.device, dtype=tensor.dtype).view(1, 1, -1) + + b, c, f, w, h = tensor.size() + + # 对于第三轴(F轴)仅平滑内部帧 (1:-1) + # 首先,移动要平滑的轴到最后一个维度以便应用conv1d + tensor_moved = rearrange(tensor, "b c f h w -> b (c h w) f") + + # 对除了两端帧外的所有帧应用conv1d + internal_frames = F.conv1d(tensor_moved, weight_kernel) + print("tensor:", tensor.shape) + + # 重新整理输出形状为 (B, F, C, W, H) + internal_frames = rearrange(internal_frames, "b (c h w) f -> b c f h w", c=c, h=h, w=w) + print("internal_frames:", internal_frames.shape) + # 将第一帧和最后一帧保持不变,合并结果 + # 首帧 tensor[:, :, 0:1, :, :], 中间帧 internal_frames[:, :, 1:-1, :, :], 最后帧 tensor[:, :, -1:, :, :] + smoothed_tensor = torch.cat( + [tensor[:, :, 0:1, :, :], internal_frames, tensor[:, :, -1:, :, :]], dim=2) + + return smoothed_tensor diff --git a/src/pipelines/pipeline_echo_mimic_pose.py b/src/pipelines/pipeline_echo_mimic_pose.py new file mode 100644 index 0000000000000000000000000000000000000000..fedfa555c3b478645534cb5b0f48ef44398d061a --- /dev/null +++ b/src/pipelines/pipeline_echo_mimic_pose.py @@ -0,0 +1,594 @@ +import inspect +import math +from dataclasses import dataclass +from typing import Callable, List, Optional, Union + +import numpy as np +import torch +from diffusers import DiffusionPipeline +import torch.nn.functional as F +from diffusers.image_processor import VaeImageProcessor +from diffusers.schedulers import ( + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, +) +from diffusers.utils import BaseOutput, deprecate, is_accelerate_available, logging +from diffusers.utils.torch_utils import randn_tensor +from einops import rearrange +from tqdm import tqdm +from transformers import CLIPImageProcessor + +from src.models.mutual_self_attention import ReferenceAttentionControl +from src.pipelines.context import get_context_scheduler +from src.pipelines.utils import get_tensor_interpolation_method + + +@dataclass +class AudioPose2VideoPipelineOutput(BaseOutput): + videos: Union[torch.Tensor, np.ndarray] + + +class AudioPose2VideoPipeline(DiffusionPipeline): + _optional_components = [] + + def __init__( + self, + vae, + reference_unet, + denoising_unet, + audio_guider, + face_locator, + # audio_feature_mapper, + scheduler: Union[ + DDIMScheduler, + PNDMScheduler, + LMSDiscreteScheduler, + EulerDiscreteScheduler, + EulerAncestralDiscreteScheduler, + DPMSolverMultistepScheduler, + ], + image_proj_model=None, + tokenizer=None, + text_encoder=None, + ): + super().__init__() + + self.register_modules( + vae=vae, + reference_unet=reference_unet, + denoising_unet=denoising_unet, + audio_guider=audio_guider, + face_locator=face_locator, + scheduler=scheduler, + image_proj_model=image_proj_model, + tokenizer=tokenizer, + text_encoder=text_encoder, + # audio_feature_mapper=audio_feature_mapper + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.ref_image_processor = VaeImageProcessor( + vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True + ) + + def enable_vae_slicing(self): + self.vae.enable_slicing() + + def disable_vae_slicing(self): + self.vae.disable_slicing() + + def enable_sequential_cpu_offload(self, gpu_id=0): + if is_accelerate_available(): + from accelerate import cpu_offload + else: + raise ImportError("Please install accelerate via `pip install accelerate`") + + device = torch.device(f"cuda:{gpu_id}") + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + if cpu_offloaded_model is not None: + cpu_offload(cpu_offloaded_model, device) + + @property + def _execution_device(self): + if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def decode_latents(self, latents): + video_length = latents.shape[2] + latents = 1 / 0.18215 * latents + latents = rearrange(latents, "b c f h w -> (b f) c h w") + # video = self.vae.decode(latents).sample + video = [] + for frame_idx in tqdm(range(latents.shape[0])): + video.append(self.vae.decode(latents[frame_idx : frame_idx + 1]).sample) + video = torch.cat(video) + video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length) + video = (video / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + video = video.cpu().float().numpy() + return video + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def prepare_latents_bp( + self, + batch_size, + num_channels_latents, + width, + height, + video_length, + dtype, + device, + generator, + latents=None, + ): + shape = ( + batch_size, + num_channels_latents, + video_length, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor( + shape, generator=generator, device=device, dtype=dtype + ) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_latents( + self, + batch_size, + num_channels_latents, + width, + height, + video_length, + dtype, + device, + generator, + context_frame_length + ): + shape = ( + batch_size, + num_channels_latents, + # context_frame_length, + video_length, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + latents_seg = randn_tensor( + shape, generator=generator, device=device, dtype=dtype + ) + latents = latents_seg + # print(latents.min(), latents.max()) + # latents = torch.clamp(latents, -1.5, 1.5) + # latents_seg = torch.zeros_like(latents_seg) + # + # latents_all = [latents_seg.clone() for _ in range(video_length // context_frame_length)] + \ + # [latents_seg.clone()[:, :, :video_length % context_frame_length, :, :]] + # latents = torch.cat(latents_all, 2) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + print(f"latents shape:{latents.shape}, video_length:{video_length}") + return latents + def _encode_prompt( + self, + prompt, + device, + num_videos_per_prompt, + do_classifier_free_guidance, + negative_prompt, + ): + batch_size = len(prompt) if isinstance(prompt, list) else 1 + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer( + prompt, padding="longest", return_tensors="pt" + ).input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + + if ( + hasattr(self.text_encoder.config, "use_attention_mask") + and self.text_encoder.config.use_attention_mask + ): + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + text_embeddings = self.text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + ) + text_embeddings = text_embeddings[0] + + # duplicate text embeddings for each generation per prompt, using mps friendly method + bs_embed, seq_len, _ = text_embeddings.shape + text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1) + text_embeddings = text_embeddings.view( + bs_embed * num_videos_per_prompt, seq_len, -1 + ) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = text_input_ids.shape[-1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if ( + hasattr(self.text_encoder.config, "use_attention_mask") + and self.text_encoder.config.use_attention_mask + ): + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + uncond_embeddings = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + uncond_embeddings = uncond_embeddings[0] + + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = uncond_embeddings.shape[1] + uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1) + uncond_embeddings = uncond_embeddings.view( + batch_size * num_videos_per_prompt, seq_len, -1 + ) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) + + return text_embeddings + + def interpolate_latents( + self, latents: torch.Tensor, interpolation_factor: int, device + ): + if interpolation_factor < 2: + return latents + + new_latents = torch.zeros( + ( + latents.shape[0], + latents.shape[1], + ((latents.shape[2] - 1) * interpolation_factor) + 1, + latents.shape[3], + latents.shape[4], + ), + device=latents.device, + dtype=latents.dtype, + ) + + org_video_length = latents.shape[2] + rate = [i / interpolation_factor for i in range(interpolation_factor)][1:] + + new_index = 0 + + v0 = None + v1 = None + + for i0, i1 in zip(range(org_video_length), range(org_video_length)[1:]): + v0 = latents[:, :, i0, :, :] + v1 = latents[:, :, i1, :, :] + + new_latents[:, :, new_index, :, :] = v0 + new_index += 1 + + for f in rate: + v = get_tensor_interpolation_method()( + v0.to(device=device), v1.to(device=device), f + ) + new_latents[:, :, new_index, :, :] = v.to(latents.device) + new_index += 1 + + new_latents[:, :, new_index, :, :] = v1 + new_index += 1 + + return new_latents + + @torch.no_grad() + def __call__( + self, + ref_image, + audio_path, + face_mask_tensor, + width, + height, + video_length, + num_inference_steps, + guidance_scale, + num_images_per_prompt=1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + output_type: Optional[str] = "tensor", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: Optional[int] = 1, + context_schedule="uniform", + context_frames=12, + context_stride=1, + context_overlap=0, + context_batch_size=1, + interpolation_factor=1, + audio_sample_rate=16000, + fps=25, + audio_margin=2, + **kwargs, + ): + # Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + device = self._execution_device + + do_classifier_free_guidance = guidance_scale > 1.0 + + # Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + batch_size = 1 + + reference_control_writer = ReferenceAttentionControl( + self.reference_unet, + do_classifier_free_guidance=do_classifier_free_guidance, + mode="write", + batch_size=batch_size, + fusion_blocks="full", + ) + reference_control_reader = ReferenceAttentionControl( + self.denoising_unet, + do_classifier_free_guidance=do_classifier_free_guidance, + mode="read", + batch_size=batch_size, + fusion_blocks="full", + ) + + whisper_feature = self.audio_guider.audio2feat(audio_path) + + whisper_chunks = self.audio_guider.feature2chunks(feature_array=whisper_feature, fps=fps) + + audio_frame_num = whisper_chunks.shape[0] + audio_fea_final = torch.Tensor(whisper_chunks).to(dtype=self.vae.dtype, device=self.vae.device) + audio_fea_final = audio_fea_final.unsqueeze(0) + + video_length = min(video_length, audio_frame_num) + if video_length < audio_frame_num: + audio_fea_final = audio_fea_final[:, :video_length, :, :] + + num_channels_latents = self.denoising_unet.in_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + width, + height, + video_length, + audio_fea_final.dtype, + device, + generator, + context_frames + ) + + face_locator_tensor = self.face_locator(face_mask_tensor) + + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # Prepare ref image latents + ref_image_tensor = self.ref_image_processor.preprocess( + ref_image, height=height, width=width + ) # (bs, c, width, height) + ref_image_tensor = ref_image_tensor.to( + dtype=self.vae.dtype, device=self.vae.device + ) + ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean + ref_image_latents = ref_image_latents * 0.18215 # (b , 4, h, w) + + context_scheduler = get_context_scheduler(context_schedule) + + # denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + context_queue = list( + context_scheduler( + 0, + num_inference_steps, + latents.shape[2], + context_frames, + context_stride, + context_overlap, + ) + ) + + with self.progress_bar(total=num_inference_steps) as progress_bar: + for t_i, t in enumerate(timesteps): + + noise_pred = torch.zeros( + ( + latents.shape[0] * (2 if do_classifier_free_guidance else 1), + *latents.shape[1:], + ), + device=latents.device, + dtype=latents.dtype, + ) + counter = torch.zeros( + (1, 1, latents.shape[2], 1, 1), + device=latents.device, + dtype=latents.dtype, + ) + + # 1. Forward reference image + if t_i == 0: + self.reference_unet( + ref_image_latents, + torch.zeros_like(t), + encoder_hidden_states=None, + return_dict=False, + ) + reference_control_reader.update(reference_control_writer, do_classifier_free_guidance=True) + + + num_context_batches = math.ceil(len(context_queue) / context_batch_size) + + global_context = [] + for j in range(num_context_batches): + global_context.append( + context_queue[ + j * context_batch_size : (j + 1) * context_batch_size + ] + ) + + ## refine + for context in global_context: + new_context = [[0 for _ in range(len(context[c_j]))] for c_j in range(len(context))] + for c_j in range(len(context)): + for c_i in range(len(context[c_j])): + new_context[c_j][c_i] = (context[c_j][c_i] + t_i * 3) % video_length + + + latent_model_input = ( + torch.cat([latents[:, :, c] for c in new_context]) + .to(device) + .repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1) + ) + + audio_latents_cond = torch.cat([audio_fea_final[:, c] for c in new_context]).to(device) + audio_latents = torch.cat([torch.zeros_like(audio_latents_cond), audio_latents_cond], 0) + pose_latents_cond = torch.cat([face_locator_tensor[:, :, c] for c in new_context]).to(device) + zero_pose_latents = torch.cat([zero_locator_tensor[:, :, c] for c in new_context]).to(device) + pose_latents = torch.cat([torch.zeros_like(zero_pose_latents), pose_latents_cond], 0) + + latent_model_input = self.scheduler.scale_model_input( + latent_model_input, t + ) + b, c, f, h, w = latent_model_input.shape + pred = self.denoising_unet( + latent_model_input, + t, + encoder_hidden_states=None, + audio_cond_fea=audio_latents if do_classifier_free_guidance else audio_latents_cond, + face_musk_fea=pose_latents if do_classifier_free_guidance else pose_latents_cond, + return_dict=False, + )[0] + + for j, c in enumerate(new_context): + noise_pred[:, :, c] = noise_pred[:, :, c] + pred + counter[:, :, c] = counter[:, :, c] + 1 + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = (noise_pred / counter).chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + latents = self.scheduler.step( + noise_pred, t, latents, **extra_step_kwargs + ).prev_sample + + if t_i == len(timesteps) - 1 or ( + (t_i + 1) > num_warmup_steps and (t_i + 1) % self.scheduler.order == 0 + ): + progress_bar.update() + + reference_control_reader.clear() + reference_control_writer.clear() + + if interpolation_factor > 0: + latents = self.interpolate_latents(latents, interpolation_factor, device) + # Post-processing + images = self.decode_latents(latents) # (b, c, f, h, w) + + # Convert to tensor + if output_type == "tensor": + images = torch.from_numpy(images) + + if not return_dict: + return images + + return AudioPose2VideoPipelineOutput(videos=images) diff --git a/src/pipelines/pipeline_echo_mimic_pose_acc.py b/src/pipelines/pipeline_echo_mimic_pose_acc.py new file mode 100644 index 0000000000000000000000000000000000000000..bd4ab8b7733eb4a1203804b7c839427d682b8b36 --- /dev/null +++ b/src/pipelines/pipeline_echo_mimic_pose_acc.py @@ -0,0 +1,600 @@ +import inspect +import math +from dataclasses import dataclass +from typing import Callable, List, Optional, Union + +import numpy as np +import torch +from diffusers import DiffusionPipeline +import torch.nn.functional as F +from diffusers.image_processor import VaeImageProcessor +from diffusers.schedulers import ( + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, +) +from diffusers.utils import BaseOutput, deprecate, is_accelerate_available, logging +from diffusers.utils.torch_utils import randn_tensor +from einops import rearrange +from tqdm import tqdm +from transformers import CLIPImageProcessor + +from src.models.mutual_self_attention import ReferenceAttentionControl +from src.pipelines.context import get_context_scheduler +from src.pipelines.utils import get_tensor_interpolation_method +from src.utils.step_func import origin_by_velocity_and_sample, psuedo_velocity_wrt_noisy_and_timestep, get_alpha + + +@dataclass +class AudioPose2VideoPipelineOutput(BaseOutput): + videos: Union[torch.Tensor, np.ndarray] + + +class AudioPose2VideoPipeline(DiffusionPipeline): + _optional_components = [] + + def __init__( + self, + vae, + reference_unet, + denoising_unet, + audio_guider, + face_locator, + # audio_feature_mapper, + scheduler: Union[ + DDIMScheduler, + PNDMScheduler, + LMSDiscreteScheduler, + EulerDiscreteScheduler, + EulerAncestralDiscreteScheduler, + DPMSolverMultistepScheduler, + ], + image_proj_model=None, + tokenizer=None, + text_encoder=None, + ): + super().__init__() + + self.register_modules( + vae=vae, + reference_unet=reference_unet, + denoising_unet=denoising_unet, + audio_guider=audio_guider, + face_locator=face_locator, + scheduler=scheduler, + image_proj_model=image_proj_model, + tokenizer=tokenizer, + text_encoder=text_encoder, + # audio_feature_mapper=audio_feature_mapper + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.ref_image_processor = VaeImageProcessor( + vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True + ) + + def enable_vae_slicing(self): + self.vae.enable_slicing() + + def disable_vae_slicing(self): + self.vae.disable_slicing() + + def enable_sequential_cpu_offload(self, gpu_id=0): + if is_accelerate_available(): + from accelerate import cpu_offload + else: + raise ImportError("Please install accelerate via `pip install accelerate`") + + device = torch.device(f"cuda:{gpu_id}") + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + if cpu_offloaded_model is not None: + cpu_offload(cpu_offloaded_model, device) + + @property + def _execution_device(self): + if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def decode_latents(self, latents): + video_length = latents.shape[2] + latents = 1 / 0.18215 * latents + latents = rearrange(latents, "b c f h w -> (b f) c h w") + # video = self.vae.decode(latents).sample + video = [] + for frame_idx in tqdm(range(latents.shape[0])): + video.append(self.vae.decode(latents[frame_idx : frame_idx + 1]).sample) + video = torch.cat(video) + video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length) + video = (video / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + video = video.cpu().float().numpy() + return video + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def prepare_latents_bp( + self, + batch_size, + num_channels_latents, + width, + height, + video_length, + dtype, + device, + generator, + latents=None, + ): + shape = ( + batch_size, + num_channels_latents, + video_length, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor( + shape, generator=generator, device=device, dtype=dtype + ) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_latents( + self, + batch_size, + num_channels_latents, + width, + height, + video_length, + dtype, + device, + generator, + context_frame_length + ): + shape = ( + batch_size, + num_channels_latents, + # context_frame_length, + video_length, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + latents_seg = randn_tensor( + shape, generator=generator, device=device, dtype=dtype + ) + latents = latents_seg + # print(latents.min(), latents.max()) + # latents = torch.clamp(latents, -1.5, 1.5) + # latents_seg = torch.zeros_like(latents_seg) + # + # latents_all = [latents_seg.clone() for _ in range(video_length // context_frame_length)] + \ + # [latents_seg.clone()[:, :, :video_length % context_frame_length, :, :]] + # latents = torch.cat(latents_all, 2) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + print(f"latents shape:{latents.shape}, video_length:{video_length}") + return latents + def _encode_prompt( + self, + prompt, + device, + num_videos_per_prompt, + do_classifier_free_guidance, + negative_prompt, + ): + batch_size = len(prompt) if isinstance(prompt, list) else 1 + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer( + prompt, padding="longest", return_tensors="pt" + ).input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + + if ( + hasattr(self.text_encoder.config, "use_attention_mask") + and self.text_encoder.config.use_attention_mask + ): + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + text_embeddings = self.text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + ) + text_embeddings = text_embeddings[0] + + # duplicate text embeddings for each generation per prompt, using mps friendly method + bs_embed, seq_len, _ = text_embeddings.shape + text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1) + text_embeddings = text_embeddings.view( + bs_embed * num_videos_per_prompt, seq_len, -1 + ) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = text_input_ids.shape[-1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if ( + hasattr(self.text_encoder.config, "use_attention_mask") + and self.text_encoder.config.use_attention_mask + ): + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + uncond_embeddings = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + uncond_embeddings = uncond_embeddings[0] + + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = uncond_embeddings.shape[1] + uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1) + uncond_embeddings = uncond_embeddings.view( + batch_size * num_videos_per_prompt, seq_len, -1 + ) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) + + return text_embeddings + + def interpolate_latents( + self, latents: torch.Tensor, interpolation_factor: int, device + ): + if interpolation_factor < 2: + return latents + + new_latents = torch.zeros( + ( + latents.shape[0], + latents.shape[1], + ((latents.shape[2] - 1) * interpolation_factor) + 1, + latents.shape[3], + latents.shape[4], + ), + device=latents.device, + dtype=latents.dtype, + ) + + org_video_length = latents.shape[2] + rate = [i / interpolation_factor for i in range(interpolation_factor)][1:] + + new_index = 0 + + v0 = None + v1 = None + + for i0, i1 in zip(range(org_video_length), range(org_video_length)[1:]): + v0 = latents[:, :, i0, :, :] + v1 = latents[:, :, i1, :, :] + + new_latents[:, :, new_index, :, :] = v0 + new_index += 1 + + for f in rate: + v = get_tensor_interpolation_method()( + v0.to(device=device), v1.to(device=device), f + ) + new_latents[:, :, new_index, :, :] = v.to(latents.device) + new_index += 1 + + new_latents[:, :, new_index, :, :] = v1 + new_index += 1 + + return new_latents + + @torch.no_grad() + def __call__( + self, + ref_image, + audio_path, + face_mask_tensor, + width, + height, + video_length, + num_inference_steps, + guidance_scale, + num_images_per_prompt=1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + output_type: Optional[str] = "tensor", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: Optional[int] = 1, + context_schedule="uniform", + context_frames=12, + context_stride=1, + context_overlap=0, + context_batch_size=1, + interpolation_factor=1, + audio_sample_rate=16000, + fps=25, + audio_margin=2, + **kwargs, + ): + # Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + device = self._execution_device + + do_classifier_free_guidance = guidance_scale > 1.0 + + # Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + batch_size = 1 + + reference_control_writer = ReferenceAttentionControl( + self.reference_unet, + do_classifier_free_guidance=do_classifier_free_guidance, + mode="write", + batch_size=batch_size, + fusion_blocks="full", + ) + reference_control_reader = ReferenceAttentionControl( + self.denoising_unet, + do_classifier_free_guidance=do_classifier_free_guidance, + mode="read", + batch_size=batch_size, + fusion_blocks="full", + ) + + whisper_feature = self.audio_guider.audio2feat(audio_path) + + whisper_chunks = self.audio_guider.feature2chunks(feature_array=whisper_feature, fps=fps) + + audio_frame_num = whisper_chunks.shape[0] + audio_fea_final = torch.Tensor(whisper_chunks).to(dtype=self.vae.dtype, device=self.vae.device) + audio_fea_final = audio_fea_final.unsqueeze(0) + + video_length = min(video_length, audio_frame_num) + if video_length < audio_frame_num: + audio_fea_final = audio_fea_final[:, :video_length, :, :] + + num_channels_latents = self.denoising_unet.in_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + width, + height, + video_length, + audio_fea_final.dtype, + device, + generator, + context_frames + ) + + face_locator_tensor = self.face_locator(face_mask_tensor) + + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # Prepare ref image latents + ref_image_tensor = self.ref_image_processor.preprocess( + ref_image, height=height, width=width + ) # (bs, c, width, height) + ref_image_tensor = ref_image_tensor.to( + dtype=self.vae.dtype, device=self.vae.device + ) + ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean + ref_image_latents = ref_image_latents * 0.18215 # (b , 4, h, w) + + context_scheduler = get_context_scheduler(context_schedule) + + # denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + context_queue = list( + context_scheduler( + 0, + num_inference_steps, + latents.shape[2], + context_frames, + context_stride, + context_overlap, + ) + ) + + with self.progress_bar(total=num_inference_steps) as progress_bar: + for t_i, t in enumerate(timesteps): + + noise_pred = torch.zeros( + ( + latents.shape[0] * (2 if do_classifier_free_guidance else 1), + *latents.shape[1:], + ), + device=latents.device, + dtype=latents.dtype, + ) + counter = torch.zeros( + (1, 1, latents.shape[2], 1, 1), + device=latents.device, + dtype=latents.dtype, + ) + + # 1. Forward reference image + if t_i == 0: + self.reference_unet( + ref_image_latents, + torch.zeros_like(t), + encoder_hidden_states=None, + return_dict=False, + ) + reference_control_reader.update(reference_control_writer, do_classifier_free_guidance=do_classifier_free_guidance) + + + num_context_batches = math.ceil(len(context_queue) / context_batch_size) + + global_context = [] + for j in range(num_context_batches): + global_context.append( + context_queue[ + j * context_batch_size : (j + 1) * context_batch_size + ] + ) + + ## refine + for context in global_context: + new_context = [[0 for _ in range(len(context[c_j]))] for c_j in range(len(context))] + for c_j in range(len(context)): + for c_i in range(len(context[c_j])): + new_context[c_j][c_i] = (context[c_j][c_i] + t_i * 3) % video_length + + + latent_model_input = ( + torch.cat([latents[:, :, c] for c in new_context]) + .to(device) + .repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1) + ) + + audio_latents_cond = torch.cat([audio_fea_final[:, c] for c in new_context]).to(device) + audio_latents = torch.cat([torch.zeros_like(audio_latents_cond), audio_latents_cond], 0) + pose_latents_cond = torch.cat([face_locator_tensor[:, :, c] for c in new_context]).to(device) + pose_latents = torch.cat([torch.zeros_like(pose_latents_cond), pose_latents_cond], 0) + + latent_model_input = self.scheduler.scale_model_input( + latent_model_input, t + ) + b, c, f, h, w = latent_model_input.shape + pred = self.denoising_unet( + latent_model_input, + t, + encoder_hidden_states=None, + audio_cond_fea=audio_latents if do_classifier_free_guidance else audio_latents_cond, + face_musk_fea=pose_latents if do_classifier_free_guidance else pose_latents_cond, + return_dict=False, + )[0] + + alphas_cumprod = self.scheduler.alphas_cumprod.to(latent_model_input.device) + x_pred = origin_by_velocity_and_sample(pred, latent_model_input, alphas_cumprod, t) + pred = psuedo_velocity_wrt_noisy_and_timestep(latent_model_input, x_pred, alphas_cumprod, t, torch.ones_like(t) * (-1)) + + for j, c in enumerate(new_context): + noise_pred[:, :, c] = noise_pred[:, :, c] + pred + counter[:, :, c] = counter[:, :, c] + 1 + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = (noise_pred / counter).chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + else: + noise_pred = noise_pred / counter + + latents = self.scheduler.step( + noise_pred, t, latents, **extra_step_kwargs + ).prev_sample + + if t_i == len(timesteps) - 1 or ( + (t_i + 1) > num_warmup_steps and (t_i + 1) % self.scheduler.order == 0 + ): + progress_bar.update() + + reference_control_reader.clear() + reference_control_writer.clear() + + if interpolation_factor > 0: + latents = self.interpolate_latents(latents, interpolation_factor, device) + # Post-processing + images = self.decode_latents(latents) # (b, c, f, h, w) + + # Convert to tensor + if output_type == "tensor": + images = torch.from_numpy(images) + + if not return_dict: + return images + + return AudioPose2VideoPipelineOutput(videos=images) diff --git a/src/pipelines/utils.py b/src/pipelines/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3cd5076748d15de4fc4b872bff7ba173f346478d --- /dev/null +++ b/src/pipelines/utils.py @@ -0,0 +1,29 @@ +import torch + +tensor_interpolation = None + + +def get_tensor_interpolation_method(): + return tensor_interpolation + + +def set_tensor_interpolation_method(is_slerp): + global tensor_interpolation + tensor_interpolation = slerp if is_slerp else linear + + +def linear(v1, v2, t): + return (1.0 - t) * v1 + t * v2 + + +def slerp( + v0: torch.Tensor, v1: torch.Tensor, t: float, DOT_THRESHOLD: float = 0.9995 +) -> torch.Tensor: + u0 = v0 / v0.norm() + u1 = v1 / v1.norm() + dot = (u0 * u1).sum() + if dot.abs() > DOT_THRESHOLD: + # logger.info(f'warning: v0 and v1 close to parallel, using linear interpolation instead.') + return (1.0 - t) * v0 + t * v1 + omega = dot.acos() + return (((1.0 - t) * omega).sin() * v0 + (t * omega).sin() * v1) / omega.sin() diff --git a/src/utils/draw_utils.py b/src/utils/draw_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ac81c9fe062144d38e9b8b75ea6a11dc9dd3a5e9 --- /dev/null +++ b/src/utils/draw_utils.py @@ -0,0 +1,191 @@ +import cv2 +import mediapipe as mp +import numpy as np +from mediapipe.framework.formats import landmark_pb2 + +class FaceMeshVisualizer: + def __init__(self, + forehead_edge=False, + upface_only=False, + draw_eye=True, + draw_head=False, + draw_iris=True, + draw_eyebrow=True, + draw_mouse=True, + draw_nose=True, + draw_pupil=True + ): + self.mp_drawing = mp.solutions.drawing_utils + mp_face_mesh = mp.solutions.face_mesh + self.mp_face_mesh = mp_face_mesh + self.forehead_edge = forehead_edge + + DrawingSpec = mp.solutions.drawing_styles.DrawingSpec + f_thick = 2 + f_rad = 1 + right_iris_draw = DrawingSpec(color=(10, 200, 250), thickness=f_thick, circle_radius=f_rad) + right_eye_draw = DrawingSpec(color=(10, 200, 180), thickness=f_thick, circle_radius=f_rad) + right_eyebrow_draw = DrawingSpec(color=(10, 220, 180), thickness=f_thick, circle_radius=f_rad) + left_iris_draw = DrawingSpec(color=(250, 200, 10), thickness=f_thick, circle_radius=f_rad) + left_eye_draw = DrawingSpec(color=(180, 200, 10), thickness=f_thick, circle_radius=f_rad) + left_eyebrow_draw = DrawingSpec(color=(180, 220, 10), thickness=f_thick, circle_radius=f_rad) + head_draw = DrawingSpec(color=(10, 200, 10), thickness=f_thick, circle_radius=f_rad) + nose_draw = DrawingSpec(color=(200, 200, 200), thickness=f_thick, circle_radius=f_rad) + + mouth_draw_obl = DrawingSpec(color=(10, 180, 20), thickness=f_thick, circle_radius=f_rad) + mouth_draw_obr = DrawingSpec(color=(20, 10, 180), thickness=f_thick, circle_radius=f_rad) + + mouth_draw_ibl = DrawingSpec(color=(100, 100, 30), thickness=f_thick, circle_radius=f_rad) + mouth_draw_ibr = DrawingSpec(color=(100, 150, 50), thickness=f_thick, circle_radius=f_rad) + + mouth_draw_otl = DrawingSpec(color=(20, 80, 100), thickness=f_thick, circle_radius=f_rad) + mouth_draw_otr = DrawingSpec(color=(80, 100, 20), thickness=f_thick, circle_radius=f_rad) + + mouth_draw_itl = DrawingSpec(color=(120, 100, 200), thickness=f_thick, circle_radius=f_rad) + mouth_draw_itr = DrawingSpec(color=(150 ,120, 100), thickness=f_thick, circle_radius=f_rad) + + FACEMESH_LIPS_OUTER_BOTTOM_LEFT = [(61,146),(146,91),(91,181),(181,84),(84,17)] + FACEMESH_LIPS_OUTER_BOTTOM_RIGHT = [(17,314),(314,405),(405,321),(321,375),(375,291)] + + FACEMESH_LIPS_INNER_BOTTOM_LEFT = [(78,95),(95,88),(88,178),(178,87),(87,14)] + FACEMESH_LIPS_INNER_BOTTOM_RIGHT = [(14,317),(317,402),(402,318),(318,324),(324,308)] + + FACEMESH_LIPS_OUTER_TOP_LEFT = [(61,185),(185,40),(40,39),(39,37),(37,0)] + FACEMESH_LIPS_OUTER_TOP_RIGHT = [(0,267),(267,269),(269,270),(270,409),(409,291)] + + FACEMESH_LIPS_INNER_TOP_LEFT = [(78,191),(191,80),(80,81),(81,82),(82,13)] + FACEMESH_LIPS_INNER_TOP_RIGHT = [(13,312),(312,311),(311,310),(310,415),(415,308)] + + FACEMESH_CUSTOM_FACE_OVAL = [(176, 149), (150, 136), (356, 454), (58, 132), (152, 148), (361, 288), (251, 389), (132, 93), (389, 356), (400, 377), (136, 172), (377, 152), (323, 361), (172, 58), (454, 323), (365, 379), (379, 378), (148, 176), (93, 234), (397, 365), (149, 150), (288, 397), (234, 127), (378, 400), (127, 162), (162, 21)] + + # mp_face_mesh.FACEMESH_CONTOURS has all the items we care about. + face_connection_spec = {} + + #from IPython import embed + #embed() + if self.forehead_edge: + for edge in mp_face_mesh.FACEMESH_FACE_OVAL: + face_connection_spec[edge] = head_draw + else: + if draw_head: + FACEMESH_CUSTOM_FACE_OVAL_sorted = sorted(FACEMESH_CUSTOM_FACE_OVAL) + if upface_only: + for edge in [FACEMESH_CUSTOM_FACE_OVAL_sorted[edge_idx] for edge_idx in [1,2,9,12,13,16,22,25]]: + face_connection_spec[edge] = head_draw + else: + for edge in FACEMESH_CUSTOM_FACE_OVAL_sorted: + face_connection_spec[edge] = head_draw + + if draw_eye: + for edge in mp_face_mesh.FACEMESH_LEFT_EYE: + face_connection_spec[edge] = left_eye_draw + for edge in mp_face_mesh.FACEMESH_RIGHT_EYE: + face_connection_spec[edge] = right_eye_draw + + if draw_eyebrow: + for edge in mp_face_mesh.FACEMESH_LEFT_EYEBROW: + face_connection_spec[edge] = left_eyebrow_draw + + for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW: + face_connection_spec[edge] = right_eyebrow_draw + + if draw_iris: + for edge in mp_face_mesh.FACEMESH_LEFT_IRIS: + face_connection_spec[edge] = left_iris_draw + for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS: + face_connection_spec[edge] = right_iris_draw + + #for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW: + # face_connection_spec[edge] = right_eyebrow_draw + # for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS: + # face_connection_spec[edge] = right_iris_draw + + # for edge in mp_face_mesh.FACEMESH_LIPS: + # face_connection_spec[edge] = mouth_draw + + if draw_mouse: + for edge in FACEMESH_LIPS_OUTER_BOTTOM_LEFT: + face_connection_spec[edge] = mouth_draw_obl + for edge in FACEMESH_LIPS_OUTER_BOTTOM_RIGHT: + face_connection_spec[edge] = mouth_draw_obr + for edge in FACEMESH_LIPS_INNER_BOTTOM_LEFT: + face_connection_spec[edge] = mouth_draw_ibl + for edge in FACEMESH_LIPS_INNER_BOTTOM_RIGHT: + face_connection_spec[edge] = mouth_draw_ibr + for edge in FACEMESH_LIPS_OUTER_TOP_LEFT: + face_connection_spec[edge] = mouth_draw_otl + for edge in FACEMESH_LIPS_OUTER_TOP_RIGHT: + face_connection_spec[edge] = mouth_draw_otr + for edge in FACEMESH_LIPS_INNER_TOP_LEFT: + face_connection_spec[edge] = mouth_draw_itl + for edge in FACEMESH_LIPS_INNER_TOP_RIGHT: + face_connection_spec[edge] = mouth_draw_itr + + self.face_connection_spec = face_connection_spec + + self.pupil_landmark_spec = {468: right_iris_draw, 473: left_iris_draw} + self.nose_landmark_spec = {4: nose_draw} + + self.draw_pupil = draw_pupil + self.draw_nose = draw_nose + + def draw_points(self, image, landmark_list, drawing_spec, halfwidth: int = 2): + """We have a custom function to draw the pupils because the mp.draw_landmarks method requires a parameter for all + landmarks. Until our PR is merged into mediapipe, we need this separate method.""" + if len(image.shape) != 3: + raise ValueError("Input image must be H,W,C.") + image_rows, image_cols, image_channels = image.shape + if image_channels != 3: # BGR channels + raise ValueError('Input image must contain three channel bgr data.') + for idx, landmark in enumerate(landmark_list.landmark): + if idx not in drawing_spec: + continue + + if ( + (landmark.HasField('visibility') and landmark.visibility < 0.9) or + (landmark.HasField('presence') and landmark.presence < 0.5) + ): + continue + if landmark.x >= 1.0 or landmark.x < 0 or landmark.y >= 1.0 or landmark.y < 0: + continue + + image_x = int(image_cols * landmark.x) + image_y = int(image_rows * landmark.y) + + draw_color = drawing_spec[idx].color + image[image_y - halfwidth : image_y + halfwidth, image_x - halfwidth : image_x + halfwidth, :] = draw_color + + + def draw_landmarks(self, image_size, keypoints, normed=False): + ini_size = [512, 512] + image = np.zeros([ini_size[1], ini_size[0], 3], dtype=np.uint8) + if keypoints is not None: + new_landmarks = landmark_pb2.NormalizedLandmarkList() + for i in range(keypoints.shape[0]): + landmark = new_landmarks.landmark.add() + if normed: + landmark.x = keypoints[i, 0] + landmark.y = keypoints[i, 1] + else: + landmark.x = keypoints[i, 0] / image_size[0] + landmark.y = keypoints[i, 1] / image_size[1] + landmark.z = 1.0 + + self.mp_drawing.draw_landmarks( + image=image, + landmark_list=new_landmarks, + connections=self.face_connection_spec.keys(), + landmark_drawing_spec=None, + connection_drawing_spec=self.face_connection_spec + ) + + if self.draw_pupil: + self.draw_points(image, new_landmarks, self.pupil_landmark_spec, 3) + + if self.draw_nose: + self.draw_points(image, new_landmarks, self.nose_landmark_spec, 3) + + image = cv2.resize(image, (image_size[0], image_size[1])) + + return image + diff --git a/src/utils/face_landmark.py b/src/utils/face_landmark.py new file mode 100644 index 0000000000000000000000000000000000000000..b6580cb2cded9dcfeab46b0d50c8931ed6256669 --- /dev/null +++ b/src/utils/face_landmark.py @@ -0,0 +1,3305 @@ +# Copyright 2023 The MediaPipe Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""MediaPipe face landmarker task.""" + +import dataclasses +import enum +from typing import Callable, Mapping, Optional, List + +import numpy as np + +from mediapipe.framework.formats import classification_pb2 +from mediapipe.framework.formats import landmark_pb2 +from mediapipe.framework.formats import matrix_data_pb2 +from mediapipe.python import packet_creator +from mediapipe.python import packet_getter +from mediapipe.python._framework_bindings import image as image_module +from mediapipe.python._framework_bindings import packet as packet_module +# pylint: disable=unused-import +from mediapipe.tasks.cc.vision.face_geometry.proto import face_geometry_pb2 +# pylint: enable=unused-import +from mediapipe.tasks.cc.vision.face_landmarker.proto import face_landmarker_graph_options_pb2 +from mediapipe.tasks.python.components.containers import category as category_module +from mediapipe.tasks.python.components.containers import landmark as landmark_module +from mediapipe.tasks.python.core import base_options as base_options_module +from mediapipe.tasks.python.core import task_info as task_info_module +from mediapipe.tasks.python.core.optional_dependencies import doc_controls +from mediapipe.tasks.python.vision.core import base_vision_task_api +from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module +from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module + +_BaseOptions = base_options_module.BaseOptions +_FaceLandmarkerGraphOptionsProto = ( + face_landmarker_graph_options_pb2.FaceLandmarkerGraphOptions +) +_LayoutEnum = matrix_data_pb2.MatrixData.Layout +_RunningMode = running_mode_module.VisionTaskRunningMode +_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions +_TaskInfo = task_info_module.TaskInfo + +_IMAGE_IN_STREAM_NAME = 'image_in' +_IMAGE_OUT_STREAM_NAME = 'image_out' +_IMAGE_TAG = 'IMAGE' +_NORM_RECT_STREAM_NAME = 'norm_rect_in' +_NORM_RECT_TAG = 'NORM_RECT' +_NORM_LANDMARKS_STREAM_NAME = 'norm_landmarks' +_NORM_LANDMARKS_TAG = 'NORM_LANDMARKS' +_BLENDSHAPES_STREAM_NAME = 'blendshapes' +_BLENDSHAPES_TAG = 'BLENDSHAPES' +_FACE_GEOMETRY_STREAM_NAME = 'face_geometry' +_FACE_GEOMETRY_TAG = 'FACE_GEOMETRY' +_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.face_landmarker.FaceLandmarkerGraph' +_MICRO_SECONDS_PER_MILLISECOND = 1000 + + +class Blendshapes(enum.IntEnum): + """The 52 blendshape coefficients.""" + + NEUTRAL = 0 + BROW_DOWN_LEFT = 1 + BROW_DOWN_RIGHT = 2 + BROW_INNER_UP = 3 + BROW_OUTER_UP_LEFT = 4 + BROW_OUTER_UP_RIGHT = 5 + CHEEK_PUFF = 6 + CHEEK_SQUINT_LEFT = 7 + CHEEK_SQUINT_RIGHT = 8 + EYE_BLINK_LEFT = 9 + EYE_BLINK_RIGHT = 10 + EYE_LOOK_DOWN_LEFT = 11 + EYE_LOOK_DOWN_RIGHT = 12 + EYE_LOOK_IN_LEFT = 13 + EYE_LOOK_IN_RIGHT = 14 + EYE_LOOK_OUT_LEFT = 15 + EYE_LOOK_OUT_RIGHT = 16 + EYE_LOOK_UP_LEFT = 17 + EYE_LOOK_UP_RIGHT = 18 + EYE_SQUINT_LEFT = 19 + EYE_SQUINT_RIGHT = 20 + EYE_WIDE_LEFT = 21 + EYE_WIDE_RIGHT = 22 + JAW_FORWARD = 23 + JAW_LEFT = 24 + JAW_OPEN = 25 + JAW_RIGHT = 26 + MOUTH_CLOSE = 27 + MOUTH_DIMPLE_LEFT = 28 + MOUTH_DIMPLE_RIGHT = 29 + MOUTH_FROWN_LEFT = 30 + MOUTH_FROWN_RIGHT = 31 + MOUTH_FUNNEL = 32 + MOUTH_LEFT = 33 + MOUTH_LOWER_DOWN_LEFT = 34 + MOUTH_LOWER_DOWN_RIGHT = 35 + MOUTH_PRESS_LEFT = 36 + MOUTH_PRESS_RIGHT = 37 + MOUTH_PUCKER = 38 + MOUTH_RIGHT = 39 + MOUTH_ROLL_LOWER = 40 + MOUTH_ROLL_UPPER = 41 + MOUTH_SHRUG_LOWER = 42 + MOUTH_SHRUG_UPPER = 43 + MOUTH_SMILE_LEFT = 44 + MOUTH_SMILE_RIGHT = 45 + MOUTH_STRETCH_LEFT = 46 + MOUTH_STRETCH_RIGHT = 47 + MOUTH_UPPER_UP_LEFT = 48 + MOUTH_UPPER_UP_RIGHT = 49 + NOSE_SNEER_LEFT = 50 + NOSE_SNEER_RIGHT = 51 + + +class FaceLandmarksConnections: + """The connections between face landmarks.""" + + @dataclasses.dataclass + class Connection: + """The connection class for face landmarks.""" + + start: int + end: int + + FACE_LANDMARKS_LIPS: List[Connection] = [ + Connection(61, 146), + Connection(146, 91), + Connection(91, 181), + Connection(181, 84), + Connection(84, 17), + Connection(17, 314), + Connection(314, 405), + Connection(405, 321), + Connection(321, 375), + Connection(375, 291), + Connection(61, 185), + Connection(185, 40), + Connection(40, 39), + Connection(39, 37), + Connection(37, 0), + Connection(0, 267), + Connection(267, 269), + Connection(269, 270), + Connection(270, 409), + Connection(409, 291), + Connection(78, 95), + Connection(95, 88), + Connection(88, 178), + Connection(178, 87), + Connection(87, 14), + Connection(14, 317), + Connection(317, 402), + Connection(402, 318), + Connection(318, 324), + Connection(324, 308), + Connection(78, 191), + Connection(191, 80), + Connection(80, 81), + Connection(81, 82), + Connection(82, 13), + Connection(13, 312), + Connection(312, 311), + Connection(311, 310), + Connection(310, 415), + Connection(415, 308), + ] + + FACE_LANDMARKS_LEFT_EYE: List[Connection] = [ + Connection(263, 249), + Connection(249, 390), + Connection(390, 373), + Connection(373, 374), + Connection(374, 380), + Connection(380, 381), + Connection(381, 382), + Connection(382, 362), + Connection(263, 466), + Connection(466, 388), + Connection(388, 387), + Connection(387, 386), + Connection(386, 385), + Connection(385, 384), + Connection(384, 398), + Connection(398, 362), + ] + + FACE_LANDMARKS_LEFT_EYEBROW: List[Connection] = [ + Connection(276, 283), + Connection(283, 282), + Connection(282, 295), + Connection(295, 285), + Connection(300, 293), + Connection(293, 334), + Connection(334, 296), + Connection(296, 336), + ] + + FACE_LANDMARKS_LEFT_IRIS: List[Connection] = [ + Connection(474, 475), + Connection(475, 476), + Connection(476, 477), + Connection(477, 474), + ] + + FACE_LANDMARKS_RIGHT_EYE: List[Connection] = [ + Connection(33, 7), + Connection(7, 163), + Connection(163, 144), + Connection(144, 145), + Connection(145, 153), + Connection(153, 154), + Connection(154, 155), + Connection(155, 133), + Connection(33, 246), + Connection(246, 161), + Connection(161, 160), + Connection(160, 159), + Connection(159, 158), + Connection(158, 157), + Connection(157, 173), + Connection(173, 133), + ] + + FACE_LANDMARKS_RIGHT_EYEBROW: List[Connection] = [ + Connection(46, 53), + Connection(53, 52), + Connection(52, 65), + Connection(65, 55), + Connection(70, 63), + Connection(63, 105), + Connection(105, 66), + Connection(66, 107), + ] + + FACE_LANDMARKS_RIGHT_IRIS: List[Connection] = [ + Connection(469, 470), + Connection(470, 471), + Connection(471, 472), + Connection(472, 469), + ] + + FACE_LANDMARKS_FACE_OVAL: List[Connection] = [ + Connection(10, 338), + Connection(338, 297), + Connection(297, 332), + Connection(332, 284), + Connection(284, 251), + Connection(251, 389), + Connection(389, 356), + Connection(356, 454), + Connection(454, 323), + Connection(323, 361), + Connection(361, 288), + Connection(288, 397), + Connection(397, 365), + Connection(365, 379), + Connection(379, 378), + Connection(378, 400), + Connection(400, 377), + Connection(377, 152), + Connection(152, 148), + Connection(148, 176), + Connection(176, 149), + Connection(149, 150), + Connection(150, 136), + Connection(136, 172), + Connection(172, 58), + Connection(58, 132), + Connection(132, 93), + Connection(93, 234), + Connection(234, 127), + Connection(127, 162), + Connection(162, 21), + Connection(21, 54), + Connection(54, 103), + Connection(103, 67), + Connection(67, 109), + Connection(109, 10), + ] + + FACE_LANDMARKS_CONTOURS: List[Connection] = ( + FACE_LANDMARKS_LIPS + + FACE_LANDMARKS_LEFT_EYE + + FACE_LANDMARKS_LEFT_EYEBROW + + FACE_LANDMARKS_RIGHT_EYE + + FACE_LANDMARKS_RIGHT_EYEBROW + + FACE_LANDMARKS_FACE_OVAL + ) + + FACE_LANDMARKS_TESSELATION: List[Connection] = [ + Connection(127, 34), + Connection(34, 139), + Connection(139, 127), + Connection(11, 0), + Connection(0, 37), + Connection(37, 11), + Connection(232, 231), + Connection(231, 120), + Connection(120, 232), + Connection(72, 37), + Connection(37, 39), + Connection(39, 72), + Connection(128, 121), + Connection(121, 47), + Connection(47, 128), + Connection(232, 121), + Connection(121, 128), + Connection(128, 232), + Connection(104, 69), + Connection(69, 67), + Connection(67, 104), + Connection(175, 171), + Connection(171, 148), + Connection(148, 175), + Connection(118, 50), + Connection(50, 101), + Connection(101, 118), + Connection(73, 39), + Connection(39, 40), + Connection(40, 73), + Connection(9, 151), + Connection(151, 108), + Connection(108, 9), + Connection(48, 115), + Connection(115, 131), + Connection(131, 48), + Connection(194, 204), + Connection(204, 211), + Connection(211, 194), + Connection(74, 40), + Connection(40, 185), + Connection(185, 74), + Connection(80, 42), + Connection(42, 183), + Connection(183, 80), + Connection(40, 92), + Connection(92, 186), + Connection(186, 40), + Connection(230, 229), + Connection(229, 118), + Connection(118, 230), + Connection(202, 212), + Connection(212, 214), + Connection(214, 202), + Connection(83, 18), + Connection(18, 17), + Connection(17, 83), + Connection(76, 61), + Connection(61, 146), + Connection(146, 76), + Connection(160, 29), + Connection(29, 30), + Connection(30, 160), + Connection(56, 157), + Connection(157, 173), + Connection(173, 56), + Connection(106, 204), + Connection(204, 194), + Connection(194, 106), + Connection(135, 214), + Connection(214, 192), + Connection(192, 135), + Connection(203, 165), + Connection(165, 98), + Connection(98, 203), + Connection(21, 71), + Connection(71, 68), + Connection(68, 21), + Connection(51, 45), + Connection(45, 4), + Connection(4, 51), + Connection(144, 24), + Connection(24, 23), + Connection(23, 144), + Connection(77, 146), + Connection(146, 91), + Connection(91, 77), + Connection(205, 50), + Connection(50, 187), + Connection(187, 205), + Connection(201, 200), + Connection(200, 18), + Connection(18, 201), + Connection(91, 106), + Connection(106, 182), + Connection(182, 91), + Connection(90, 91), + Connection(91, 181), + Connection(181, 90), + Connection(85, 84), + Connection(84, 17), + Connection(17, 85), + Connection(206, 203), + Connection(203, 36), + Connection(36, 206), + Connection(148, 171), + Connection(171, 140), + Connection(140, 148), + Connection(92, 40), + Connection(40, 39), + Connection(39, 92), + Connection(193, 189), + Connection(189, 244), + Connection(244, 193), + Connection(159, 158), + Connection(158, 28), + Connection(28, 159), + Connection(247, 246), + Connection(246, 161), + Connection(161, 247), + Connection(236, 3), + Connection(3, 196), + Connection(196, 236), + Connection(54, 68), + Connection(68, 104), + Connection(104, 54), + Connection(193, 168), + Connection(168, 8), + Connection(8, 193), + Connection(117, 228), + Connection(228, 31), + Connection(31, 117), + Connection(189, 193), + Connection(193, 55), + Connection(55, 189), + Connection(98, 97), + Connection(97, 99), + Connection(99, 98), + Connection(126, 47), + Connection(47, 100), + Connection(100, 126), + Connection(166, 79), + Connection(79, 218), + Connection(218, 166), + Connection(155, 154), + Connection(154, 26), + Connection(26, 155), + Connection(209, 49), + Connection(49, 131), + Connection(131, 209), + Connection(135, 136), + Connection(136, 150), + Connection(150, 135), + Connection(47, 126), + Connection(126, 217), + Connection(217, 47), + Connection(223, 52), + Connection(52, 53), + Connection(53, 223), + Connection(45, 51), + Connection(51, 134), + Connection(134, 45), + Connection(211, 170), + Connection(170, 140), + Connection(140, 211), + Connection(67, 69), + Connection(69, 108), + Connection(108, 67), + Connection(43, 106), + Connection(106, 91), + Connection(91, 43), + Connection(230, 119), + Connection(119, 120), + Connection(120, 230), + Connection(226, 130), + Connection(130, 247), + Connection(247, 226), + Connection(63, 53), + Connection(53, 52), + Connection(52, 63), + Connection(238, 20), + Connection(20, 242), + Connection(242, 238), + Connection(46, 70), + Connection(70, 156), + Connection(156, 46), + Connection(78, 62), + Connection(62, 96), + Connection(96, 78), + Connection(46, 53), + Connection(53, 63), + Connection(63, 46), + Connection(143, 34), + Connection(34, 227), + Connection(227, 143), + Connection(123, 117), + Connection(117, 111), + Connection(111, 123), + Connection(44, 125), + Connection(125, 19), + Connection(19, 44), + Connection(236, 134), + Connection(134, 51), + Connection(51, 236), + Connection(216, 206), + Connection(206, 205), + Connection(205, 216), + Connection(154, 153), + Connection(153, 22), + Connection(22, 154), + Connection(39, 37), + Connection(37, 167), + Connection(167, 39), + Connection(200, 201), + Connection(201, 208), + Connection(208, 200), + Connection(36, 142), + Connection(142, 100), + Connection(100, 36), + Connection(57, 212), + Connection(212, 202), + Connection(202, 57), + Connection(20, 60), + Connection(60, 99), + Connection(99, 20), + Connection(28, 158), + Connection(158, 157), + Connection(157, 28), + Connection(35, 226), + Connection(226, 113), + Connection(113, 35), + Connection(160, 159), + Connection(159, 27), + Connection(27, 160), + Connection(204, 202), + Connection(202, 210), + Connection(210, 204), + Connection(113, 225), + Connection(225, 46), + Connection(46, 113), + Connection(43, 202), + Connection(202, 204), + Connection(204, 43), + Connection(62, 76), + Connection(76, 77), + Connection(77, 62), + Connection(137, 123), + Connection(123, 116), + Connection(116, 137), + Connection(41, 38), + Connection(38, 72), + Connection(72, 41), + Connection(203, 129), + Connection(129, 142), + Connection(142, 203), + Connection(64, 98), + Connection(98, 240), + Connection(240, 64), + Connection(49, 102), + Connection(102, 64), + Connection(64, 49), + Connection(41, 73), + Connection(73, 74), + Connection(74, 41), + Connection(212, 216), + Connection(216, 207), + Connection(207, 212), + Connection(42, 74), + Connection(74, 184), + Connection(184, 42), + Connection(169, 170), + Connection(170, 211), + Connection(211, 169), + Connection(170, 149), + Connection(149, 176), + Connection(176, 170), + Connection(105, 66), + Connection(66, 69), + Connection(69, 105), + Connection(122, 6), + Connection(6, 168), + Connection(168, 122), + Connection(123, 147), + Connection(147, 187), + Connection(187, 123), + Connection(96, 77), + Connection(77, 90), + Connection(90, 96), + Connection(65, 55), + Connection(55, 107), + Connection(107, 65), + Connection(89, 90), + Connection(90, 180), + Connection(180, 89), + Connection(101, 100), + Connection(100, 120), + Connection(120, 101), + Connection(63, 105), + Connection(105, 104), + Connection(104, 63), + Connection(93, 137), + Connection(137, 227), + Connection(227, 93), + Connection(15, 86), + Connection(86, 85), + Connection(85, 15), + Connection(129, 102), + Connection(102, 49), + Connection(49, 129), + Connection(14, 87), + Connection(87, 86), + Connection(86, 14), + Connection(55, 8), + Connection(8, 9), + Connection(9, 55), + Connection(100, 47), + Connection(47, 121), + Connection(121, 100), + Connection(145, 23), + Connection(23, 22), + Connection(22, 145), + Connection(88, 89), + Connection(89, 179), + Connection(179, 88), + Connection(6, 122), + Connection(122, 196), + Connection(196, 6), + Connection(88, 95), + Connection(95, 96), + Connection(96, 88), + Connection(138, 172), + Connection(172, 136), + Connection(136, 138), + Connection(215, 58), + Connection(58, 172), + Connection(172, 215), + Connection(115, 48), + Connection(48, 219), + Connection(219, 115), + Connection(42, 80), + Connection(80, 81), + Connection(81, 42), + Connection(195, 3), + Connection(3, 51), + Connection(51, 195), + Connection(43, 146), + Connection(146, 61), + Connection(61, 43), + Connection(171, 175), + Connection(175, 199), + Connection(199, 171), + Connection(81, 82), + Connection(82, 38), + Connection(38, 81), + Connection(53, 46), + Connection(46, 225), + Connection(225, 53), + Connection(144, 163), + Connection(163, 110), + Connection(110, 144), + Connection(52, 65), + Connection(65, 66), + Connection(66, 52), + Connection(229, 228), + Connection(228, 117), + Connection(117, 229), + Connection(34, 127), + Connection(127, 234), + Connection(234, 34), + Connection(107, 108), + Connection(108, 69), + Connection(69, 107), + Connection(109, 108), + Connection(108, 151), + Connection(151, 109), + Connection(48, 64), + Connection(64, 235), + Connection(235, 48), + Connection(62, 78), + Connection(78, 191), + Connection(191, 62), + Connection(129, 209), + Connection(209, 126), + Connection(126, 129), + Connection(111, 35), + Connection(35, 143), + Connection(143, 111), + Connection(117, 123), + Connection(123, 50), + Connection(50, 117), + Connection(222, 65), + Connection(65, 52), + Connection(52, 222), + Connection(19, 125), + Connection(125, 141), + Connection(141, 19), + Connection(221, 55), + Connection(55, 65), + Connection(65, 221), + Connection(3, 195), + Connection(195, 197), + Connection(197, 3), + Connection(25, 7), + Connection(7, 33), + Connection(33, 25), + Connection(220, 237), + Connection(237, 44), + Connection(44, 220), + Connection(70, 71), + Connection(71, 139), + Connection(139, 70), + Connection(122, 193), + Connection(193, 245), + Connection(245, 122), + Connection(247, 130), + Connection(130, 33), + Connection(33, 247), + Connection(71, 21), + Connection(21, 162), + Connection(162, 71), + Connection(170, 169), + Connection(169, 150), + Connection(150, 170), + Connection(188, 174), + Connection(174, 196), + Connection(196, 188), + Connection(216, 186), + Connection(186, 92), + Connection(92, 216), + Connection(2, 97), + Connection(97, 167), + Connection(167, 2), + Connection(141, 125), + Connection(125, 241), + Connection(241, 141), + Connection(164, 167), + Connection(167, 37), + Connection(37, 164), + Connection(72, 38), + Connection(38, 12), + Connection(12, 72), + Connection(38, 82), + Connection(82, 13), + Connection(13, 38), + Connection(63, 68), + Connection(68, 71), + Connection(71, 63), + Connection(226, 35), + Connection(35, 111), + Connection(111, 226), + Connection(101, 50), + Connection(50, 205), + Connection(205, 101), + Connection(206, 92), + Connection(92, 165), + Connection(165, 206), + Connection(209, 198), + Connection(198, 217), + Connection(217, 209), + Connection(165, 167), + Connection(167, 97), + Connection(97, 165), + Connection(220, 115), + Connection(115, 218), + Connection(218, 220), + Connection(133, 112), + Connection(112, 243), + Connection(243, 133), + Connection(239, 238), + Connection(238, 241), + Connection(241, 239), + Connection(214, 135), + Connection(135, 169), + Connection(169, 214), + Connection(190, 173), + Connection(173, 133), + Connection(133, 190), + Connection(171, 208), + Connection(208, 32), + Connection(32, 171), + Connection(125, 44), + Connection(44, 237), + Connection(237, 125), + Connection(86, 87), + Connection(87, 178), + Connection(178, 86), + Connection(85, 86), + Connection(86, 179), + Connection(179, 85), + Connection(84, 85), + Connection(85, 180), + Connection(180, 84), + Connection(83, 84), + Connection(84, 181), + Connection(181, 83), + Connection(201, 83), + Connection(83, 182), + Connection(182, 201), + Connection(137, 93), + Connection(93, 132), + Connection(132, 137), + Connection(76, 62), + Connection(62, 183), + Connection(183, 76), + Connection(61, 76), + Connection(76, 184), + Connection(184, 61), + Connection(57, 61), + Connection(61, 185), + Connection(185, 57), + Connection(212, 57), + Connection(57, 186), + Connection(186, 212), + Connection(214, 207), + Connection(207, 187), + Connection(187, 214), + Connection(34, 143), + Connection(143, 156), + Connection(156, 34), + Connection(79, 239), + Connection(239, 237), + Connection(237, 79), + Connection(123, 137), + Connection(137, 177), + Connection(177, 123), + Connection(44, 1), + Connection(1, 4), + Connection(4, 44), + Connection(201, 194), + Connection(194, 32), + Connection(32, 201), + Connection(64, 102), + Connection(102, 129), + Connection(129, 64), + Connection(213, 215), + Connection(215, 138), + Connection(138, 213), + Connection(59, 166), + Connection(166, 219), + Connection(219, 59), + Connection(242, 99), + Connection(99, 97), + Connection(97, 242), + Connection(2, 94), + Connection(94, 141), + Connection(141, 2), + Connection(75, 59), + Connection(59, 235), + Connection(235, 75), + Connection(24, 110), + Connection(110, 228), + Connection(228, 24), + Connection(25, 130), + Connection(130, 226), + Connection(226, 25), + Connection(23, 24), + Connection(24, 229), + Connection(229, 23), + Connection(22, 23), + Connection(23, 230), + Connection(230, 22), + Connection(26, 22), + Connection(22, 231), + Connection(231, 26), + Connection(112, 26), + Connection(26, 232), + Connection(232, 112), + Connection(189, 190), + Connection(190, 243), + Connection(243, 189), + Connection(221, 56), + Connection(56, 190), + Connection(190, 221), + Connection(28, 56), + Connection(56, 221), + Connection(221, 28), + Connection(27, 28), + Connection(28, 222), + Connection(222, 27), + Connection(29, 27), + Connection(27, 223), + Connection(223, 29), + Connection(30, 29), + Connection(29, 224), + Connection(224, 30), + Connection(247, 30), + Connection(30, 225), + Connection(225, 247), + Connection(238, 79), + Connection(79, 20), + Connection(20, 238), + Connection(166, 59), + Connection(59, 75), + Connection(75, 166), + Connection(60, 75), + Connection(75, 240), + Connection(240, 60), + Connection(147, 177), + Connection(177, 215), + Connection(215, 147), + Connection(20, 79), + Connection(79, 166), + Connection(166, 20), + Connection(187, 147), + Connection(147, 213), + Connection(213, 187), + Connection(112, 233), + Connection(233, 244), + Connection(244, 112), + Connection(233, 128), + Connection(128, 245), + Connection(245, 233), + Connection(128, 114), + Connection(114, 188), + Connection(188, 128), + Connection(114, 217), + Connection(217, 174), + Connection(174, 114), + Connection(131, 115), + Connection(115, 220), + Connection(220, 131), + Connection(217, 198), + Connection(198, 236), + Connection(236, 217), + Connection(198, 131), + Connection(131, 134), + Connection(134, 198), + Connection(177, 132), + Connection(132, 58), + Connection(58, 177), + Connection(143, 35), + Connection(35, 124), + Connection(124, 143), + Connection(110, 163), + Connection(163, 7), + Connection(7, 110), + Connection(228, 110), + Connection(110, 25), + Connection(25, 228), + Connection(356, 389), + Connection(389, 368), + Connection(368, 356), + Connection(11, 302), + Connection(302, 267), + Connection(267, 11), + Connection(452, 350), + Connection(350, 349), + Connection(349, 452), + Connection(302, 303), + Connection(303, 269), + Connection(269, 302), + Connection(357, 343), + Connection(343, 277), + Connection(277, 357), + Connection(452, 453), + Connection(453, 357), + Connection(357, 452), + Connection(333, 332), + Connection(332, 297), + Connection(297, 333), + Connection(175, 152), + Connection(152, 377), + Connection(377, 175), + Connection(347, 348), + Connection(348, 330), + Connection(330, 347), + Connection(303, 304), + Connection(304, 270), + Connection(270, 303), + Connection(9, 336), + Connection(336, 337), + Connection(337, 9), + Connection(278, 279), + Connection(279, 360), + Connection(360, 278), + Connection(418, 262), + Connection(262, 431), + Connection(431, 418), + Connection(304, 408), + Connection(408, 409), + Connection(409, 304), + Connection(310, 415), + Connection(415, 407), + Connection(407, 310), + Connection(270, 409), + Connection(409, 410), + Connection(410, 270), + Connection(450, 348), + Connection(348, 347), + Connection(347, 450), + Connection(422, 430), + Connection(430, 434), + Connection(434, 422), + Connection(313, 314), + Connection(314, 17), + Connection(17, 313), + Connection(306, 307), + Connection(307, 375), + Connection(375, 306), + Connection(387, 388), + Connection(388, 260), + Connection(260, 387), + Connection(286, 414), + Connection(414, 398), + Connection(398, 286), + Connection(335, 406), + Connection(406, 418), + Connection(418, 335), + Connection(364, 367), + Connection(367, 416), + Connection(416, 364), + Connection(423, 358), + Connection(358, 327), + Connection(327, 423), + Connection(251, 284), + Connection(284, 298), + Connection(298, 251), + Connection(281, 5), + Connection(5, 4), + Connection(4, 281), + Connection(373, 374), + Connection(374, 253), + Connection(253, 373), + Connection(307, 320), + Connection(320, 321), + Connection(321, 307), + Connection(425, 427), + Connection(427, 411), + Connection(411, 425), + Connection(421, 313), + Connection(313, 18), + Connection(18, 421), + Connection(321, 405), + Connection(405, 406), + Connection(406, 321), + Connection(320, 404), + Connection(404, 405), + Connection(405, 320), + Connection(315, 16), + Connection(16, 17), + Connection(17, 315), + Connection(426, 425), + Connection(425, 266), + Connection(266, 426), + Connection(377, 400), + Connection(400, 369), + Connection(369, 377), + Connection(322, 391), + Connection(391, 269), + Connection(269, 322), + Connection(417, 465), + Connection(465, 464), + Connection(464, 417), + Connection(386, 257), + Connection(257, 258), + Connection(258, 386), + Connection(466, 260), + Connection(260, 388), + Connection(388, 466), + Connection(456, 399), + Connection(399, 419), + Connection(419, 456), + Connection(284, 332), + Connection(332, 333), + Connection(333, 284), + Connection(417, 285), + Connection(285, 8), + Connection(8, 417), + Connection(346, 340), + Connection(340, 261), + Connection(261, 346), + Connection(413, 441), + Connection(441, 285), + Connection(285, 413), + Connection(327, 460), + Connection(460, 328), + Connection(328, 327), + Connection(355, 371), + Connection(371, 329), + Connection(329, 355), + Connection(392, 439), + Connection(439, 438), + Connection(438, 392), + Connection(382, 341), + Connection(341, 256), + Connection(256, 382), + Connection(429, 420), + Connection(420, 360), + Connection(360, 429), + Connection(364, 394), + Connection(394, 379), + Connection(379, 364), + Connection(277, 343), + Connection(343, 437), + Connection(437, 277), + Connection(443, 444), + Connection(444, 283), + Connection(283, 443), + Connection(275, 440), + Connection(440, 363), + Connection(363, 275), + Connection(431, 262), + Connection(262, 369), + Connection(369, 431), + Connection(297, 338), + Connection(338, 337), + Connection(337, 297), + Connection(273, 375), + Connection(375, 321), + Connection(321, 273), + Connection(450, 451), + Connection(451, 349), + Connection(349, 450), + Connection(446, 342), + Connection(342, 467), + Connection(467, 446), + Connection(293, 334), + Connection(334, 282), + Connection(282, 293), + Connection(458, 461), + Connection(461, 462), + Connection(462, 458), + Connection(276, 353), + Connection(353, 383), + Connection(383, 276), + Connection(308, 324), + Connection(324, 325), + Connection(325, 308), + Connection(276, 300), + Connection(300, 293), + Connection(293, 276), + Connection(372, 345), + Connection(345, 447), + Connection(447, 372), + Connection(352, 345), + Connection(345, 340), + Connection(340, 352), + Connection(274, 1), + Connection(1, 19), + Connection(19, 274), + Connection(456, 248), + Connection(248, 281), + Connection(281, 456), + Connection(436, 427), + Connection(427, 425), + Connection(425, 436), + Connection(381, 256), + Connection(256, 252), + Connection(252, 381), + Connection(269, 391), + Connection(391, 393), + Connection(393, 269), + Connection(200, 199), + Connection(199, 428), + Connection(428, 200), + Connection(266, 330), + Connection(330, 329), + Connection(329, 266), + Connection(287, 273), + Connection(273, 422), + Connection(422, 287), + Connection(250, 462), + Connection(462, 328), + Connection(328, 250), + Connection(258, 286), + Connection(286, 384), + Connection(384, 258), + Connection(265, 353), + Connection(353, 342), + Connection(342, 265), + Connection(387, 259), + Connection(259, 257), + Connection(257, 387), + Connection(424, 431), + Connection(431, 430), + Connection(430, 424), + Connection(342, 353), + Connection(353, 276), + Connection(276, 342), + Connection(273, 335), + Connection(335, 424), + Connection(424, 273), + Connection(292, 325), + Connection(325, 307), + Connection(307, 292), + Connection(366, 447), + Connection(447, 345), + Connection(345, 366), + Connection(271, 303), + Connection(303, 302), + Connection(302, 271), + Connection(423, 266), + Connection(266, 371), + Connection(371, 423), + Connection(294, 455), + Connection(455, 460), + Connection(460, 294), + Connection(279, 278), + Connection(278, 294), + Connection(294, 279), + Connection(271, 272), + Connection(272, 304), + Connection(304, 271), + Connection(432, 434), + Connection(434, 427), + Connection(427, 432), + Connection(272, 407), + Connection(407, 408), + Connection(408, 272), + Connection(394, 430), + Connection(430, 431), + Connection(431, 394), + Connection(395, 369), + Connection(369, 400), + Connection(400, 395), + Connection(334, 333), + Connection(333, 299), + Connection(299, 334), + Connection(351, 417), + Connection(417, 168), + Connection(168, 351), + Connection(352, 280), + Connection(280, 411), + Connection(411, 352), + Connection(325, 319), + Connection(319, 320), + Connection(320, 325), + Connection(295, 296), + Connection(296, 336), + Connection(336, 295), + Connection(319, 403), + Connection(403, 404), + Connection(404, 319), + Connection(330, 348), + Connection(348, 349), + Connection(349, 330), + Connection(293, 298), + Connection(298, 333), + Connection(333, 293), + Connection(323, 454), + Connection(454, 447), + Connection(447, 323), + Connection(15, 16), + Connection(16, 315), + Connection(315, 15), + Connection(358, 429), + Connection(429, 279), + Connection(279, 358), + Connection(14, 15), + Connection(15, 316), + Connection(316, 14), + Connection(285, 336), + Connection(336, 9), + Connection(9, 285), + Connection(329, 349), + Connection(349, 350), + Connection(350, 329), + Connection(374, 380), + Connection(380, 252), + Connection(252, 374), + Connection(318, 402), + Connection(402, 403), + Connection(403, 318), + Connection(6, 197), + Connection(197, 419), + Connection(419, 6), + Connection(318, 319), + Connection(319, 325), + Connection(325, 318), + Connection(367, 364), + Connection(364, 365), + Connection(365, 367), + Connection(435, 367), + Connection(367, 397), + Connection(397, 435), + Connection(344, 438), + Connection(438, 439), + Connection(439, 344), + Connection(272, 271), + Connection(271, 311), + Connection(311, 272), + Connection(195, 5), + Connection(5, 281), + Connection(281, 195), + Connection(273, 287), + Connection(287, 291), + Connection(291, 273), + Connection(396, 428), + Connection(428, 199), + Connection(199, 396), + Connection(311, 271), + Connection(271, 268), + Connection(268, 311), + Connection(283, 444), + Connection(444, 445), + Connection(445, 283), + Connection(373, 254), + Connection(254, 339), + Connection(339, 373), + Connection(282, 334), + Connection(334, 296), + Connection(296, 282), + Connection(449, 347), + Connection(347, 346), + Connection(346, 449), + Connection(264, 447), + Connection(447, 454), + Connection(454, 264), + Connection(336, 296), + Connection(296, 299), + Connection(299, 336), + Connection(338, 10), + Connection(10, 151), + Connection(151, 338), + Connection(278, 439), + Connection(439, 455), + Connection(455, 278), + Connection(292, 407), + Connection(407, 415), + Connection(415, 292), + Connection(358, 371), + Connection(371, 355), + Connection(355, 358), + Connection(340, 345), + Connection(345, 372), + Connection(372, 340), + Connection(346, 347), + Connection(347, 280), + Connection(280, 346), + Connection(442, 443), + Connection(443, 282), + Connection(282, 442), + Connection(19, 94), + Connection(94, 370), + Connection(370, 19), + Connection(441, 442), + Connection(442, 295), + Connection(295, 441), + Connection(248, 419), + Connection(419, 197), + Connection(197, 248), + Connection(263, 255), + Connection(255, 359), + Connection(359, 263), + Connection(440, 275), + Connection(275, 274), + Connection(274, 440), + Connection(300, 383), + Connection(383, 368), + Connection(368, 300), + Connection(351, 412), + Connection(412, 465), + Connection(465, 351), + Connection(263, 467), + Connection(467, 466), + Connection(466, 263), + Connection(301, 368), + Connection(368, 389), + Connection(389, 301), + Connection(395, 378), + Connection(378, 379), + Connection(379, 395), + Connection(412, 351), + Connection(351, 419), + Connection(419, 412), + Connection(436, 426), + Connection(426, 322), + Connection(322, 436), + Connection(2, 164), + Connection(164, 393), + Connection(393, 2), + Connection(370, 462), + Connection(462, 461), + Connection(461, 370), + Connection(164, 0), + Connection(0, 267), + Connection(267, 164), + Connection(302, 11), + Connection(11, 12), + Connection(12, 302), + Connection(268, 12), + Connection(12, 13), + Connection(13, 268), + Connection(293, 300), + Connection(300, 301), + Connection(301, 293), + Connection(446, 261), + Connection(261, 340), + Connection(340, 446), + Connection(330, 266), + Connection(266, 425), + Connection(425, 330), + Connection(426, 423), + Connection(423, 391), + Connection(391, 426), + Connection(429, 355), + Connection(355, 437), + Connection(437, 429), + Connection(391, 327), + Connection(327, 326), + Connection(326, 391), + Connection(440, 457), + Connection(457, 438), + Connection(438, 440), + Connection(341, 382), + Connection(382, 362), + Connection(362, 341), + Connection(459, 457), + Connection(457, 461), + Connection(461, 459), + Connection(434, 430), + Connection(430, 394), + Connection(394, 434), + Connection(414, 463), + Connection(463, 362), + Connection(362, 414), + Connection(396, 369), + Connection(369, 262), + Connection(262, 396), + Connection(354, 461), + Connection(461, 457), + Connection(457, 354), + Connection(316, 403), + Connection(403, 402), + Connection(402, 316), + Connection(315, 404), + Connection(404, 403), + Connection(403, 315), + Connection(314, 405), + Connection(405, 404), + Connection(404, 314), + Connection(313, 406), + Connection(406, 405), + Connection(405, 313), + Connection(421, 418), + Connection(418, 406), + Connection(406, 421), + Connection(366, 401), + Connection(401, 361), + Connection(361, 366), + Connection(306, 408), + Connection(408, 407), + Connection(407, 306), + Connection(291, 409), + Connection(409, 408), + Connection(408, 291), + Connection(287, 410), + Connection(410, 409), + Connection(409, 287), + Connection(432, 436), + Connection(436, 410), + Connection(410, 432), + Connection(434, 416), + Connection(416, 411), + Connection(411, 434), + Connection(264, 368), + Connection(368, 383), + Connection(383, 264), + Connection(309, 438), + Connection(438, 457), + Connection(457, 309), + Connection(352, 376), + Connection(376, 401), + Connection(401, 352), + Connection(274, 275), + Connection(275, 4), + Connection(4, 274), + Connection(421, 428), + Connection(428, 262), + Connection(262, 421), + Connection(294, 327), + Connection(327, 358), + Connection(358, 294), + Connection(433, 416), + Connection(416, 367), + Connection(367, 433), + Connection(289, 455), + Connection(455, 439), + Connection(439, 289), + Connection(462, 370), + Connection(370, 326), + Connection(326, 462), + Connection(2, 326), + Connection(326, 370), + Connection(370, 2), + Connection(305, 460), + Connection(460, 455), + Connection(455, 305), + Connection(254, 449), + Connection(449, 448), + Connection(448, 254), + Connection(255, 261), + Connection(261, 446), + Connection(446, 255), + Connection(253, 450), + Connection(450, 449), + Connection(449, 253), + Connection(252, 451), + Connection(451, 450), + Connection(450, 252), + Connection(256, 452), + Connection(452, 451), + Connection(451, 256), + Connection(341, 453), + Connection(453, 452), + Connection(452, 341), + Connection(413, 464), + Connection(464, 463), + Connection(463, 413), + Connection(441, 413), + Connection(413, 414), + Connection(414, 441), + Connection(258, 442), + Connection(442, 441), + Connection(441, 258), + Connection(257, 443), + Connection(443, 442), + Connection(442, 257), + Connection(259, 444), + Connection(444, 443), + Connection(443, 259), + Connection(260, 445), + Connection(445, 444), + Connection(444, 260), + Connection(467, 342), + Connection(342, 445), + Connection(445, 467), + Connection(459, 458), + Connection(458, 250), + Connection(250, 459), + Connection(289, 392), + Connection(392, 290), + Connection(290, 289), + Connection(290, 328), + Connection(328, 460), + Connection(460, 290), + Connection(376, 433), + Connection(433, 435), + Connection(435, 376), + Connection(250, 290), + Connection(290, 392), + Connection(392, 250), + Connection(411, 416), + Connection(416, 433), + Connection(433, 411), + Connection(341, 463), + Connection(463, 464), + Connection(464, 341), + Connection(453, 464), + Connection(464, 465), + Connection(465, 453), + Connection(357, 465), + Connection(465, 412), + Connection(412, 357), + Connection(343, 412), + Connection(412, 399), + Connection(399, 343), + Connection(360, 363), + Connection(363, 440), + Connection(440, 360), + Connection(437, 399), + Connection(399, 456), + Connection(456, 437), + Connection(420, 456), + Connection(456, 363), + Connection(363, 420), + Connection(401, 435), + Connection(435, 288), + Connection(288, 401), + Connection(372, 383), + Connection(383, 353), + Connection(353, 372), + Connection(339, 255), + Connection(255, 249), + Connection(249, 339), + Connection(448, 261), + Connection(261, 255), + Connection(255, 448), + Connection(133, 243), + Connection(243, 190), + Connection(190, 133), + Connection(133, 155), + Connection(155, 112), + Connection(112, 133), + Connection(33, 246), + Connection(246, 247), + Connection(247, 33), + Connection(33, 130), + Connection(130, 25), + Connection(25, 33), + Connection(398, 384), + Connection(384, 286), + Connection(286, 398), + Connection(362, 398), + Connection(398, 414), + Connection(414, 362), + Connection(362, 463), + Connection(463, 341), + Connection(341, 362), + Connection(263, 359), + Connection(359, 467), + Connection(467, 263), + Connection(263, 249), + Connection(249, 255), + Connection(255, 263), + Connection(466, 467), + Connection(467, 260), + Connection(260, 466), + Connection(75, 60), + Connection(60, 166), + Connection(166, 75), + Connection(238, 239), + Connection(239, 79), + Connection(79, 238), + Connection(162, 127), + Connection(127, 139), + Connection(139, 162), + Connection(72, 11), + Connection(11, 37), + Connection(37, 72), + Connection(121, 232), + Connection(232, 120), + Connection(120, 121), + Connection(73, 72), + Connection(72, 39), + Connection(39, 73), + Connection(114, 128), + Connection(128, 47), + Connection(47, 114), + Connection(233, 232), + Connection(232, 128), + Connection(128, 233), + Connection(103, 104), + Connection(104, 67), + Connection(67, 103), + Connection(152, 175), + Connection(175, 148), + Connection(148, 152), + Connection(119, 118), + Connection(118, 101), + Connection(101, 119), + Connection(74, 73), + Connection(73, 40), + Connection(40, 74), + Connection(107, 9), + Connection(9, 108), + Connection(108, 107), + Connection(49, 48), + Connection(48, 131), + Connection(131, 49), + Connection(32, 194), + Connection(194, 211), + Connection(211, 32), + Connection(184, 74), + Connection(74, 185), + Connection(185, 184), + Connection(191, 80), + Connection(80, 183), + Connection(183, 191), + Connection(185, 40), + Connection(40, 186), + Connection(186, 185), + Connection(119, 230), + Connection(230, 118), + Connection(118, 119), + Connection(210, 202), + Connection(202, 214), + Connection(214, 210), + Connection(84, 83), + Connection(83, 17), + Connection(17, 84), + Connection(77, 76), + Connection(76, 146), + Connection(146, 77), + Connection(161, 160), + Connection(160, 30), + Connection(30, 161), + Connection(190, 56), + Connection(56, 173), + Connection(173, 190), + Connection(182, 106), + Connection(106, 194), + Connection(194, 182), + Connection(138, 135), + Connection(135, 192), + Connection(192, 138), + Connection(129, 203), + Connection(203, 98), + Connection(98, 129), + Connection(54, 21), + Connection(21, 68), + Connection(68, 54), + Connection(5, 51), + Connection(51, 4), + Connection(4, 5), + Connection(145, 144), + Connection(144, 23), + Connection(23, 145), + Connection(90, 77), + Connection(77, 91), + Connection(91, 90), + Connection(207, 205), + Connection(205, 187), + Connection(187, 207), + Connection(83, 201), + Connection(201, 18), + Connection(18, 83), + Connection(181, 91), + Connection(91, 182), + Connection(182, 181), + Connection(180, 90), + Connection(90, 181), + Connection(181, 180), + Connection(16, 85), + Connection(85, 17), + Connection(17, 16), + Connection(205, 206), + Connection(206, 36), + Connection(36, 205), + Connection(176, 148), + Connection(148, 140), + Connection(140, 176), + Connection(165, 92), + Connection(92, 39), + Connection(39, 165), + Connection(245, 193), + Connection(193, 244), + Connection(244, 245), + Connection(27, 159), + Connection(159, 28), + Connection(28, 27), + Connection(30, 247), + Connection(247, 161), + Connection(161, 30), + Connection(174, 236), + Connection(236, 196), + Connection(196, 174), + Connection(103, 54), + Connection(54, 104), + Connection(104, 103), + Connection(55, 193), + Connection(193, 8), + Connection(8, 55), + Connection(111, 117), + Connection(117, 31), + Connection(31, 111), + Connection(221, 189), + Connection(189, 55), + Connection(55, 221), + Connection(240, 98), + Connection(98, 99), + Connection(99, 240), + Connection(142, 126), + Connection(126, 100), + Connection(100, 142), + Connection(219, 166), + Connection(166, 218), + Connection(218, 219), + Connection(112, 155), + Connection(155, 26), + Connection(26, 112), + Connection(198, 209), + Connection(209, 131), + Connection(131, 198), + Connection(169, 135), + Connection(135, 150), + Connection(150, 169), + Connection(114, 47), + Connection(47, 217), + Connection(217, 114), + Connection(224, 223), + Connection(223, 53), + Connection(53, 224), + Connection(220, 45), + Connection(45, 134), + Connection(134, 220), + Connection(32, 211), + Connection(211, 140), + Connection(140, 32), + Connection(109, 67), + Connection(67, 108), + Connection(108, 109), + Connection(146, 43), + Connection(43, 91), + Connection(91, 146), + Connection(231, 230), + Connection(230, 120), + Connection(120, 231), + Connection(113, 226), + Connection(226, 247), + Connection(247, 113), + Connection(105, 63), + Connection(63, 52), + Connection(52, 105), + Connection(241, 238), + Connection(238, 242), + Connection(242, 241), + Connection(124, 46), + Connection(46, 156), + Connection(156, 124), + Connection(95, 78), + Connection(78, 96), + Connection(96, 95), + Connection(70, 46), + Connection(46, 63), + Connection(63, 70), + Connection(116, 143), + Connection(143, 227), + Connection(227, 116), + Connection(116, 123), + Connection(123, 111), + Connection(111, 116), + Connection(1, 44), + Connection(44, 19), + Connection(19, 1), + Connection(3, 236), + Connection(236, 51), + Connection(51, 3), + Connection(207, 216), + Connection(216, 205), + Connection(205, 207), + Connection(26, 154), + Connection(154, 22), + Connection(22, 26), + Connection(165, 39), + Connection(39, 167), + Connection(167, 165), + Connection(199, 200), + Connection(200, 208), + Connection(208, 199), + Connection(101, 36), + Connection(36, 100), + Connection(100, 101), + Connection(43, 57), + Connection(57, 202), + Connection(202, 43), + Connection(242, 20), + Connection(20, 99), + Connection(99, 242), + Connection(56, 28), + Connection(28, 157), + Connection(157, 56), + Connection(124, 35), + Connection(35, 113), + Connection(113, 124), + Connection(29, 160), + Connection(160, 27), + Connection(27, 29), + Connection(211, 204), + Connection(204, 210), + Connection(210, 211), + Connection(124, 113), + Connection(113, 46), + Connection(46, 124), + Connection(106, 43), + Connection(43, 204), + Connection(204, 106), + Connection(96, 62), + Connection(62, 77), + Connection(77, 96), + Connection(227, 137), + Connection(137, 116), + Connection(116, 227), + Connection(73, 41), + Connection(41, 72), + Connection(72, 73), + Connection(36, 203), + Connection(203, 142), + Connection(142, 36), + Connection(235, 64), + Connection(64, 240), + Connection(240, 235), + Connection(48, 49), + Connection(49, 64), + Connection(64, 48), + Connection(42, 41), + Connection(41, 74), + Connection(74, 42), + Connection(214, 212), + Connection(212, 207), + Connection(207, 214), + Connection(183, 42), + Connection(42, 184), + Connection(184, 183), + Connection(210, 169), + Connection(169, 211), + Connection(211, 210), + Connection(140, 170), + Connection(170, 176), + Connection(176, 140), + Connection(104, 105), + Connection(105, 69), + Connection(69, 104), + Connection(193, 122), + Connection(122, 168), + Connection(168, 193), + Connection(50, 123), + Connection(123, 187), + Connection(187, 50), + Connection(89, 96), + Connection(96, 90), + Connection(90, 89), + Connection(66, 65), + Connection(65, 107), + Connection(107, 66), + Connection(179, 89), + Connection(89, 180), + Connection(180, 179), + Connection(119, 101), + Connection(101, 120), + Connection(120, 119), + Connection(68, 63), + Connection(63, 104), + Connection(104, 68), + Connection(234, 93), + Connection(93, 227), + Connection(227, 234), + Connection(16, 15), + Connection(15, 85), + Connection(85, 16), + Connection(209, 129), + Connection(129, 49), + Connection(49, 209), + Connection(15, 14), + Connection(14, 86), + Connection(86, 15), + Connection(107, 55), + Connection(55, 9), + Connection(9, 107), + Connection(120, 100), + Connection(100, 121), + Connection(121, 120), + Connection(153, 145), + Connection(145, 22), + Connection(22, 153), + Connection(178, 88), + Connection(88, 179), + Connection(179, 178), + Connection(197, 6), + Connection(6, 196), + Connection(196, 197), + Connection(89, 88), + Connection(88, 96), + Connection(96, 89), + Connection(135, 138), + Connection(138, 136), + Connection(136, 135), + Connection(138, 215), + Connection(215, 172), + Connection(172, 138), + Connection(218, 115), + Connection(115, 219), + Connection(219, 218), + Connection(41, 42), + Connection(42, 81), + Connection(81, 41), + Connection(5, 195), + Connection(195, 51), + Connection(51, 5), + Connection(57, 43), + Connection(43, 61), + Connection(61, 57), + Connection(208, 171), + Connection(171, 199), + Connection(199, 208), + Connection(41, 81), + Connection(81, 38), + Connection(38, 41), + Connection(224, 53), + Connection(53, 225), + Connection(225, 224), + Connection(24, 144), + Connection(144, 110), + Connection(110, 24), + Connection(105, 52), + Connection(52, 66), + Connection(66, 105), + Connection(118, 229), + Connection(229, 117), + Connection(117, 118), + Connection(227, 34), + Connection(34, 234), + Connection(234, 227), + Connection(66, 107), + Connection(107, 69), + Connection(69, 66), + Connection(10, 109), + Connection(109, 151), + Connection(151, 10), + Connection(219, 48), + Connection(48, 235), + Connection(235, 219), + Connection(183, 62), + Connection(62, 191), + Connection(191, 183), + Connection(142, 129), + Connection(129, 126), + Connection(126, 142), + Connection(116, 111), + Connection(111, 143), + Connection(143, 116), + Connection(118, 117), + Connection(117, 50), + Connection(50, 118), + Connection(223, 222), + Connection(222, 52), + Connection(52, 223), + Connection(94, 19), + Connection(19, 141), + Connection(141, 94), + Connection(222, 221), + Connection(221, 65), + Connection(65, 222), + Connection(196, 3), + Connection(3, 197), + Connection(197, 196), + Connection(45, 220), + Connection(220, 44), + Connection(44, 45), + Connection(156, 70), + Connection(70, 139), + Connection(139, 156), + Connection(188, 122), + Connection(122, 245), + Connection(245, 188), + Connection(139, 71), + Connection(71, 162), + Connection(162, 139), + Connection(149, 170), + Connection(170, 150), + Connection(150, 149), + Connection(122, 188), + Connection(188, 196), + Connection(196, 122), + Connection(206, 216), + Connection(216, 92), + Connection(92, 206), + Connection(164, 2), + Connection(2, 167), + Connection(167, 164), + Connection(242, 141), + Connection(141, 241), + Connection(241, 242), + Connection(0, 164), + Connection(164, 37), + Connection(37, 0), + Connection(11, 72), + Connection(72, 12), + Connection(12, 11), + Connection(12, 38), + Connection(38, 13), + Connection(13, 12), + Connection(70, 63), + Connection(63, 71), + Connection(71, 70), + Connection(31, 226), + Connection(226, 111), + Connection(111, 31), + Connection(36, 101), + Connection(101, 205), + Connection(205, 36), + Connection(203, 206), + Connection(206, 165), + Connection(165, 203), + Connection(126, 209), + Connection(209, 217), + Connection(217, 126), + Connection(98, 165), + Connection(165, 97), + Connection(97, 98), + Connection(237, 220), + Connection(220, 218), + Connection(218, 237), + Connection(237, 239), + Connection(239, 241), + Connection(241, 237), + Connection(210, 214), + Connection(214, 169), + Connection(169, 210), + Connection(140, 171), + Connection(171, 32), + Connection(32, 140), + Connection(241, 125), + Connection(125, 237), + Connection(237, 241), + Connection(179, 86), + Connection(86, 178), + Connection(178, 179), + Connection(180, 85), + Connection(85, 179), + Connection(179, 180), + Connection(181, 84), + Connection(84, 180), + Connection(180, 181), + Connection(182, 83), + Connection(83, 181), + Connection(181, 182), + Connection(194, 201), + Connection(201, 182), + Connection(182, 194), + Connection(177, 137), + Connection(137, 132), + Connection(132, 177), + Connection(184, 76), + Connection(76, 183), + Connection(183, 184), + Connection(185, 61), + Connection(61, 184), + Connection(184, 185), + Connection(186, 57), + Connection(57, 185), + Connection(185, 186), + Connection(216, 212), + Connection(212, 186), + Connection(186, 216), + Connection(192, 214), + Connection(214, 187), + Connection(187, 192), + Connection(139, 34), + Connection(34, 156), + Connection(156, 139), + Connection(218, 79), + Connection(79, 237), + Connection(237, 218), + Connection(147, 123), + Connection(123, 177), + Connection(177, 147), + Connection(45, 44), + Connection(44, 4), + Connection(4, 45), + Connection(208, 201), + Connection(201, 32), + Connection(32, 208), + Connection(98, 64), + Connection(64, 129), + Connection(129, 98), + Connection(192, 213), + Connection(213, 138), + Connection(138, 192), + Connection(235, 59), + Connection(59, 219), + Connection(219, 235), + Connection(141, 242), + Connection(242, 97), + Connection(97, 141), + Connection(97, 2), + Connection(2, 141), + Connection(141, 97), + Connection(240, 75), + Connection(75, 235), + Connection(235, 240), + Connection(229, 24), + Connection(24, 228), + Connection(228, 229), + Connection(31, 25), + Connection(25, 226), + Connection(226, 31), + Connection(230, 23), + Connection(23, 229), + Connection(229, 230), + Connection(231, 22), + Connection(22, 230), + Connection(230, 231), + Connection(232, 26), + Connection(26, 231), + Connection(231, 232), + Connection(233, 112), + Connection(112, 232), + Connection(232, 233), + Connection(244, 189), + Connection(189, 243), + Connection(243, 244), + Connection(189, 221), + Connection(221, 190), + Connection(190, 189), + Connection(222, 28), + Connection(28, 221), + Connection(221, 222), + Connection(223, 27), + Connection(27, 222), + Connection(222, 223), + Connection(224, 29), + Connection(29, 223), + Connection(223, 224), + Connection(225, 30), + Connection(30, 224), + Connection(224, 225), + Connection(113, 247), + Connection(247, 225), + Connection(225, 113), + Connection(99, 60), + Connection(60, 240), + Connection(240, 99), + Connection(213, 147), + Connection(147, 215), + Connection(215, 213), + Connection(60, 20), + Connection(20, 166), + Connection(166, 60), + Connection(192, 187), + Connection(187, 213), + Connection(213, 192), + Connection(243, 112), + Connection(112, 244), + Connection(244, 243), + Connection(244, 233), + Connection(233, 245), + Connection(245, 244), + Connection(245, 128), + Connection(128, 188), + Connection(188, 245), + Connection(188, 114), + Connection(114, 174), + Connection(174, 188), + Connection(134, 131), + Connection(131, 220), + Connection(220, 134), + Connection(174, 217), + Connection(217, 236), + Connection(236, 174), + Connection(236, 198), + Connection(198, 134), + Connection(134, 236), + Connection(215, 177), + Connection(177, 58), + Connection(58, 215), + Connection(156, 143), + Connection(143, 124), + Connection(124, 156), + Connection(25, 110), + Connection(110, 7), + Connection(7, 25), + Connection(31, 228), + Connection(228, 25), + Connection(25, 31), + Connection(264, 356), + Connection(356, 368), + Connection(368, 264), + Connection(0, 11), + Connection(11, 267), + Connection(267, 0), + Connection(451, 452), + Connection(452, 349), + Connection(349, 451), + Connection(267, 302), + Connection(302, 269), + Connection(269, 267), + Connection(350, 357), + Connection(357, 277), + Connection(277, 350), + Connection(350, 452), + Connection(452, 357), + Connection(357, 350), + Connection(299, 333), + Connection(333, 297), + Connection(297, 299), + Connection(396, 175), + Connection(175, 377), + Connection(377, 396), + Connection(280, 347), + Connection(347, 330), + Connection(330, 280), + Connection(269, 303), + Connection(303, 270), + Connection(270, 269), + Connection(151, 9), + Connection(9, 337), + Connection(337, 151), + Connection(344, 278), + Connection(278, 360), + Connection(360, 344), + Connection(424, 418), + Connection(418, 431), + Connection(431, 424), + Connection(270, 304), + Connection(304, 409), + Connection(409, 270), + Connection(272, 310), + Connection(310, 407), + Connection(407, 272), + Connection(322, 270), + Connection(270, 410), + Connection(410, 322), + Connection(449, 450), + Connection(450, 347), + Connection(347, 449), + Connection(432, 422), + Connection(422, 434), + Connection(434, 432), + Connection(18, 313), + Connection(313, 17), + Connection(17, 18), + Connection(291, 306), + Connection(306, 375), + Connection(375, 291), + Connection(259, 387), + Connection(387, 260), + Connection(260, 259), + Connection(424, 335), + Connection(335, 418), + Connection(418, 424), + Connection(434, 364), + Connection(364, 416), + Connection(416, 434), + Connection(391, 423), + Connection(423, 327), + Connection(327, 391), + Connection(301, 251), + Connection(251, 298), + Connection(298, 301), + Connection(275, 281), + Connection(281, 4), + Connection(4, 275), + Connection(254, 373), + Connection(373, 253), + Connection(253, 254), + Connection(375, 307), + Connection(307, 321), + Connection(321, 375), + Connection(280, 425), + Connection(425, 411), + Connection(411, 280), + Connection(200, 421), + Connection(421, 18), + Connection(18, 200), + Connection(335, 321), + Connection(321, 406), + Connection(406, 335), + Connection(321, 320), + Connection(320, 405), + Connection(405, 321), + Connection(314, 315), + Connection(315, 17), + Connection(17, 314), + Connection(423, 426), + Connection(426, 266), + Connection(266, 423), + Connection(396, 377), + Connection(377, 369), + Connection(369, 396), + Connection(270, 322), + Connection(322, 269), + Connection(269, 270), + Connection(413, 417), + Connection(417, 464), + Connection(464, 413), + Connection(385, 386), + Connection(386, 258), + Connection(258, 385), + Connection(248, 456), + Connection(456, 419), + Connection(419, 248), + Connection(298, 284), + Connection(284, 333), + Connection(333, 298), + Connection(168, 417), + Connection(417, 8), + Connection(8, 168), + Connection(448, 346), + Connection(346, 261), + Connection(261, 448), + Connection(417, 413), + Connection(413, 285), + Connection(285, 417), + Connection(326, 327), + Connection(327, 328), + Connection(328, 326), + Connection(277, 355), + Connection(355, 329), + Connection(329, 277), + Connection(309, 392), + Connection(392, 438), + Connection(438, 309), + Connection(381, 382), + Connection(382, 256), + Connection(256, 381), + Connection(279, 429), + Connection(429, 360), + Connection(360, 279), + Connection(365, 364), + Connection(364, 379), + Connection(379, 365), + Connection(355, 277), + Connection(277, 437), + Connection(437, 355), + Connection(282, 443), + Connection(443, 283), + Connection(283, 282), + Connection(281, 275), + Connection(275, 363), + Connection(363, 281), + Connection(395, 431), + Connection(431, 369), + Connection(369, 395), + Connection(299, 297), + Connection(297, 337), + Connection(337, 299), + Connection(335, 273), + Connection(273, 321), + Connection(321, 335), + Connection(348, 450), + Connection(450, 349), + Connection(349, 348), + Connection(359, 446), + Connection(446, 467), + Connection(467, 359), + Connection(283, 293), + Connection(293, 282), + Connection(282, 283), + Connection(250, 458), + Connection(458, 462), + Connection(462, 250), + Connection(300, 276), + Connection(276, 383), + Connection(383, 300), + Connection(292, 308), + Connection(308, 325), + Connection(325, 292), + Connection(283, 276), + Connection(276, 293), + Connection(293, 283), + Connection(264, 372), + Connection(372, 447), + Connection(447, 264), + Connection(346, 352), + Connection(352, 340), + Connection(340, 346), + Connection(354, 274), + Connection(274, 19), + Connection(19, 354), + Connection(363, 456), + Connection(456, 281), + Connection(281, 363), + Connection(426, 436), + Connection(436, 425), + Connection(425, 426), + Connection(380, 381), + Connection(381, 252), + Connection(252, 380), + Connection(267, 269), + Connection(269, 393), + Connection(393, 267), + Connection(421, 200), + Connection(200, 428), + Connection(428, 421), + Connection(371, 266), + Connection(266, 329), + Connection(329, 371), + Connection(432, 287), + Connection(287, 422), + Connection(422, 432), + Connection(290, 250), + Connection(250, 328), + Connection(328, 290), + Connection(385, 258), + Connection(258, 384), + Connection(384, 385), + Connection(446, 265), + Connection(265, 342), + Connection(342, 446), + Connection(386, 387), + Connection(387, 257), + Connection(257, 386), + Connection(422, 424), + Connection(424, 430), + Connection(430, 422), + Connection(445, 342), + Connection(342, 276), + Connection(276, 445), + Connection(422, 273), + Connection(273, 424), + Connection(424, 422), + Connection(306, 292), + Connection(292, 307), + Connection(307, 306), + Connection(352, 366), + Connection(366, 345), + Connection(345, 352), + Connection(268, 271), + Connection(271, 302), + Connection(302, 268), + Connection(358, 423), + Connection(423, 371), + Connection(371, 358), + Connection(327, 294), + Connection(294, 460), + Connection(460, 327), + Connection(331, 279), + Connection(279, 294), + Connection(294, 331), + Connection(303, 271), + Connection(271, 304), + Connection(304, 303), + Connection(436, 432), + Connection(432, 427), + Connection(427, 436), + Connection(304, 272), + Connection(272, 408), + Connection(408, 304), + Connection(395, 394), + Connection(394, 431), + Connection(431, 395), + Connection(378, 395), + Connection(395, 400), + Connection(400, 378), + Connection(296, 334), + Connection(334, 299), + Connection(299, 296), + Connection(6, 351), + Connection(351, 168), + Connection(168, 6), + Connection(376, 352), + Connection(352, 411), + Connection(411, 376), + Connection(307, 325), + Connection(325, 320), + Connection(320, 307), + Connection(285, 295), + Connection(295, 336), + Connection(336, 285), + Connection(320, 319), + Connection(319, 404), + Connection(404, 320), + Connection(329, 330), + Connection(330, 349), + Connection(349, 329), + Connection(334, 293), + Connection(293, 333), + Connection(333, 334), + Connection(366, 323), + Connection(323, 447), + Connection(447, 366), + Connection(316, 15), + Connection(15, 315), + Connection(315, 316), + Connection(331, 358), + Connection(358, 279), + Connection(279, 331), + Connection(317, 14), + Connection(14, 316), + Connection(316, 317), + Connection(8, 285), + Connection(285, 9), + Connection(9, 8), + Connection(277, 329), + Connection(329, 350), + Connection(350, 277), + Connection(253, 374), + Connection(374, 252), + Connection(252, 253), + Connection(319, 318), + Connection(318, 403), + Connection(403, 319), + Connection(351, 6), + Connection(6, 419), + Connection(419, 351), + Connection(324, 318), + Connection(318, 325), + Connection(325, 324), + Connection(397, 367), + Connection(367, 365), + Connection(365, 397), + Connection(288, 435), + Connection(435, 397), + Connection(397, 288), + Connection(278, 344), + Connection(344, 439), + Connection(439, 278), + Connection(310, 272), + Connection(272, 311), + Connection(311, 310), + Connection(248, 195), + Connection(195, 281), + Connection(281, 248), + Connection(375, 273), + Connection(273, 291), + Connection(291, 375), + Connection(175, 396), + Connection(396, 199), + Connection(199, 175), + Connection(312, 311), + Connection(311, 268), + Connection(268, 312), + Connection(276, 283), + Connection(283, 445), + Connection(445, 276), + Connection(390, 373), + Connection(373, 339), + Connection(339, 390), + Connection(295, 282), + Connection(282, 296), + Connection(296, 295), + Connection(448, 449), + Connection(449, 346), + Connection(346, 448), + Connection(356, 264), + Connection(264, 454), + Connection(454, 356), + Connection(337, 336), + Connection(336, 299), + Connection(299, 337), + Connection(337, 338), + Connection(338, 151), + Connection(151, 337), + Connection(294, 278), + Connection(278, 455), + Connection(455, 294), + Connection(308, 292), + Connection(292, 415), + Connection(415, 308), + Connection(429, 358), + Connection(358, 355), + Connection(355, 429), + Connection(265, 340), + Connection(340, 372), + Connection(372, 265), + Connection(352, 346), + Connection(346, 280), + Connection(280, 352), + Connection(295, 442), + Connection(442, 282), + Connection(282, 295), + Connection(354, 19), + Connection(19, 370), + Connection(370, 354), + Connection(285, 441), + Connection(441, 295), + Connection(295, 285), + Connection(195, 248), + Connection(248, 197), + Connection(197, 195), + Connection(457, 440), + Connection(440, 274), + Connection(274, 457), + Connection(301, 300), + Connection(300, 368), + Connection(368, 301), + Connection(417, 351), + Connection(351, 465), + Connection(465, 417), + Connection(251, 301), + Connection(301, 389), + Connection(389, 251), + Connection(394, 395), + Connection(395, 379), + Connection(379, 394), + Connection(399, 412), + Connection(412, 419), + Connection(419, 399), + Connection(410, 436), + Connection(436, 322), + Connection(322, 410), + Connection(326, 2), + Connection(2, 393), + Connection(393, 326), + Connection(354, 370), + Connection(370, 461), + Connection(461, 354), + Connection(393, 164), + Connection(164, 267), + Connection(267, 393), + Connection(268, 302), + Connection(302, 12), + Connection(12, 268), + Connection(312, 268), + Connection(268, 13), + Connection(13, 312), + Connection(298, 293), + Connection(293, 301), + Connection(301, 298), + Connection(265, 446), + Connection(446, 340), + Connection(340, 265), + Connection(280, 330), + Connection(330, 425), + Connection(425, 280), + Connection(322, 426), + Connection(426, 391), + Connection(391, 322), + Connection(420, 429), + Connection(429, 437), + Connection(437, 420), + Connection(393, 391), + Connection(391, 326), + Connection(326, 393), + Connection(344, 440), + Connection(440, 438), + Connection(438, 344), + Connection(458, 459), + Connection(459, 461), + Connection(461, 458), + Connection(364, 434), + Connection(434, 394), + Connection(394, 364), + Connection(428, 396), + Connection(396, 262), + Connection(262, 428), + Connection(274, 354), + Connection(354, 457), + Connection(457, 274), + Connection(317, 316), + Connection(316, 402), + Connection(402, 317), + Connection(316, 315), + Connection(315, 403), + Connection(403, 316), + Connection(315, 314), + Connection(314, 404), + Connection(404, 315), + Connection(314, 313), + Connection(313, 405), + Connection(405, 314), + Connection(313, 421), + Connection(421, 406), + Connection(406, 313), + Connection(323, 366), + Connection(366, 361), + Connection(361, 323), + Connection(292, 306), + Connection(306, 407), + Connection(407, 292), + Connection(306, 291), + Connection(291, 408), + Connection(408, 306), + Connection(291, 287), + Connection(287, 409), + Connection(409, 291), + Connection(287, 432), + Connection(432, 410), + Connection(410, 287), + Connection(427, 434), + Connection(434, 411), + Connection(411, 427), + Connection(372, 264), + Connection(264, 383), + Connection(383, 372), + Connection(459, 309), + Connection(309, 457), + Connection(457, 459), + Connection(366, 352), + Connection(352, 401), + Connection(401, 366), + Connection(1, 274), + Connection(274, 4), + Connection(4, 1), + Connection(418, 421), + Connection(421, 262), + Connection(262, 418), + Connection(331, 294), + Connection(294, 358), + Connection(358, 331), + Connection(435, 433), + Connection(433, 367), + Connection(367, 435), + Connection(392, 289), + Connection(289, 439), + Connection(439, 392), + Connection(328, 462), + Connection(462, 326), + Connection(326, 328), + Connection(94, 2), + Connection(2, 370), + Connection(370, 94), + Connection(289, 305), + Connection(305, 455), + Connection(455, 289), + Connection(339, 254), + Connection(254, 448), + Connection(448, 339), + Connection(359, 255), + Connection(255, 446), + Connection(446, 359), + Connection(254, 253), + Connection(253, 449), + Connection(449, 254), + Connection(253, 252), + Connection(252, 450), + Connection(450, 253), + Connection(252, 256), + Connection(256, 451), + Connection(451, 252), + Connection(256, 341), + Connection(341, 452), + Connection(452, 256), + Connection(414, 413), + Connection(413, 463), + Connection(463, 414), + Connection(286, 441), + Connection(441, 414), + Connection(414, 286), + Connection(286, 258), + Connection(258, 441), + Connection(441, 286), + Connection(258, 257), + Connection(257, 442), + Connection(442, 258), + Connection(257, 259), + Connection(259, 443), + Connection(443, 257), + Connection(259, 260), + Connection(260, 444), + Connection(444, 259), + Connection(260, 467), + Connection(467, 445), + Connection(445, 260), + Connection(309, 459), + Connection(459, 250), + Connection(250, 309), + Connection(305, 289), + Connection(289, 290), + Connection(290, 305), + Connection(305, 290), + Connection(290, 460), + Connection(460, 305), + Connection(401, 376), + Connection(376, 435), + Connection(435, 401), + Connection(309, 250), + Connection(250, 392), + Connection(392, 309), + Connection(376, 411), + Connection(411, 433), + Connection(433, 376), + Connection(453, 341), + Connection(341, 464), + Connection(464, 453), + Connection(357, 453), + Connection(453, 465), + Connection(465, 357), + Connection(343, 357), + Connection(357, 412), + Connection(412, 343), + Connection(437, 343), + Connection(343, 399), + Connection(399, 437), + Connection(344, 360), + Connection(360, 440), + Connection(440, 344), + Connection(420, 437), + Connection(437, 456), + Connection(456, 420), + Connection(360, 420), + Connection(420, 363), + Connection(363, 360), + Connection(361, 401), + Connection(401, 288), + Connection(288, 361), + Connection(265, 372), + Connection(372, 353), + Connection(353, 265), + Connection(390, 339), + Connection(339, 249), + Connection(249, 390), + Connection(339, 448), + Connection(448, 255), + Connection(255, 339), + ] + + +@dataclasses.dataclass +class FaceLandmarkerResult: + """The face landmarks detection result from FaceLandmarker, where each vector element represents a single face detected in the image. + + Attributes: + face_landmarks: Detected face landmarks in normalized image coordinates. + face_blendshapes: Optional face blendshapes results. + facial_transformation_matrixes: Optional facial transformation matrix. + """ + + face_landmarks: List[List[landmark_module.NormalizedLandmark]] + face_blendshapes: List[List[category_module.Category]] + facial_transformation_matrixes: List[np.ndarray] + + +def _build_landmarker_result( + output_packets: Mapping[str, packet_module.Packet] +) -> FaceLandmarkerResult: + """Constructs a `FaceLandmarkerResult` from output packets.""" + face_landmarks_proto_list = packet_getter.get_proto_list( + output_packets[_NORM_LANDMARKS_STREAM_NAME] + ) + + face_landmarks_results = [] + for proto in face_landmarks_proto_list: + face_landmarks = landmark_pb2.NormalizedLandmarkList() + face_landmarks.MergeFrom(proto) + face_landmarks_list = [] + for face_landmark in face_landmarks.landmark: + face_landmarks_list.append( + landmark_module.NormalizedLandmark.create_from_pb2(face_landmark) + ) + face_landmarks_results.append(face_landmarks_list) + + face_blendshapes_results = [] + if _BLENDSHAPES_STREAM_NAME in output_packets: + face_blendshapes_proto_list = packet_getter.get_proto_list( + output_packets[_BLENDSHAPES_STREAM_NAME] + ) + for proto in face_blendshapes_proto_list: + face_blendshapes_categories = [] + face_blendshapes_classifications = classification_pb2.ClassificationList() + face_blendshapes_classifications.MergeFrom(proto) + for face_blendshapes in face_blendshapes_classifications.classification: + face_blendshapes_categories.append( + category_module.Category( + index=face_blendshapes.index, + score=face_blendshapes.score, + display_name=face_blendshapes.display_name, + category_name=face_blendshapes.label, + ) + ) + face_blendshapes_results.append(face_blendshapes_categories) + + facial_transformation_matrixes_results = [] + if _FACE_GEOMETRY_STREAM_NAME in output_packets: + facial_transformation_matrixes_proto_list = packet_getter.get_proto_list( + output_packets[_FACE_GEOMETRY_STREAM_NAME] + ) + for proto in facial_transformation_matrixes_proto_list: + if hasattr(proto, 'pose_transform_matrix'): + matrix_data = matrix_data_pb2.MatrixData() + matrix_data.MergeFrom(proto.pose_transform_matrix) + matrix = np.array(matrix_data.packed_data) + matrix = matrix.reshape((matrix_data.rows, matrix_data.cols)) + matrix = ( + matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T + ) + facial_transformation_matrixes_results.append(matrix) + + return FaceLandmarkerResult( + face_landmarks_results, + face_blendshapes_results, + facial_transformation_matrixes_results, + ) + +def _build_landmarker_result2( + output_packets: Mapping[str, packet_module.Packet] +) -> FaceLandmarkerResult: + """Constructs a `FaceLandmarkerResult` from output packets.""" + face_landmarks_proto_list = packet_getter.get_proto_list( + output_packets[_NORM_LANDMARKS_STREAM_NAME] + ) + + face_landmarks_results = [] + for proto in face_landmarks_proto_list: + face_landmarks = landmark_pb2.NormalizedLandmarkList() + face_landmarks.MergeFrom(proto) + face_landmarks_list = [] + for face_landmark in face_landmarks.landmark: + face_landmarks_list.append( + landmark_module.NormalizedLandmark.create_from_pb2(face_landmark) + ) + face_landmarks_results.append(face_landmarks_list) + + face_blendshapes_results = [] + if _BLENDSHAPES_STREAM_NAME in output_packets: + face_blendshapes_proto_list = packet_getter.get_proto_list( + output_packets[_BLENDSHAPES_STREAM_NAME] + ) + for proto in face_blendshapes_proto_list: + face_blendshapes_categories = [] + face_blendshapes_classifications = classification_pb2.ClassificationList() + face_blendshapes_classifications.MergeFrom(proto) + for face_blendshapes in face_blendshapes_classifications.classification: + face_blendshapes_categories.append( + category_module.Category( + index=face_blendshapes.index, + score=face_blendshapes.score, + display_name=face_blendshapes.display_name, + category_name=face_blendshapes.label, + ) + ) + face_blendshapes_results.append(face_blendshapes_categories) + + facial_transformation_matrixes_results = [] + if _FACE_GEOMETRY_STREAM_NAME in output_packets: + facial_transformation_matrixes_proto_list = packet_getter.get_proto_list( + output_packets[_FACE_GEOMETRY_STREAM_NAME] + ) + for proto in facial_transformation_matrixes_proto_list: + if hasattr(proto, 'pose_transform_matrix'): + matrix_data = matrix_data_pb2.MatrixData() + matrix_data.MergeFrom(proto.pose_transform_matrix) + matrix = np.array(matrix_data.packed_data) + matrix = matrix.reshape((matrix_data.rows, matrix_data.cols)) + matrix = ( + matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T + ) + facial_transformation_matrixes_results.append(matrix) + + return FaceLandmarkerResult( + face_landmarks_results, + face_blendshapes_results, + facial_transformation_matrixes_results, + ), facial_transformation_matrixes_proto_list[0].mesh + +@dataclasses.dataclass +class FaceLandmarkerOptions: + """Options for the face landmarker task. + + Attributes: + base_options: Base options for the face landmarker task. + running_mode: The running mode of the task. Default to the image mode. + FaceLandmarker has three running modes: 1) The image mode for detecting + face landmarks on single image inputs. 2) The video mode for detecting + face landmarks on the decoded frames of a video. 3) The live stream mode + for detecting face landmarks on the live stream of input data, such as + from camera. In this mode, the "result_callback" below must be specified + to receive the detection results asynchronously. + num_faces: The maximum number of faces that can be detected by the + FaceLandmarker. + min_face_detection_confidence: The minimum confidence score for the face + detection to be considered successful. + min_face_presence_confidence: The minimum confidence score of face presence + score in the face landmark detection. + min_tracking_confidence: The minimum confidence score for the face tracking + to be considered successful. + output_face_blendshapes: Whether FaceLandmarker outputs face blendshapes + classification. Face blendshapes are used for rendering the 3D face model. + output_facial_transformation_matrixes: Whether FaceLandmarker outputs facial + transformation_matrix. Facial transformation matrix is used to transform + the face landmarks in canonical face to the detected face, so that users + can apply face effects on the detected landmarks. + result_callback: The user-defined result callback for processing live stream + data. The result callback should only be specified when the running mode + is set to the live stream mode. + """ + + base_options: _BaseOptions + running_mode: _RunningMode = _RunningMode.IMAGE + num_faces: int = 1 + min_face_detection_confidence: float = 0.5 + min_face_presence_confidence: float = 0.5 + min_tracking_confidence: float = 0.5 + output_face_blendshapes: bool = False + output_facial_transformation_matrixes: bool = False + result_callback: Optional[ + Callable[[FaceLandmarkerResult, image_module.Image, int], None] + ] = None + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _FaceLandmarkerGraphOptionsProto: + """Generates an FaceLandmarkerGraphOptions protobuf object.""" + base_options_proto = self.base_options.to_pb2() + base_options_proto.use_stream_mode = ( + False if self.running_mode == _RunningMode.IMAGE else True + ) + + # Initialize the face landmarker options from base options. + face_landmarker_options_proto = _FaceLandmarkerGraphOptionsProto( + base_options=base_options_proto + ) + + # Configure face detector options. + face_landmarker_options_proto.face_detector_graph_options.num_faces = ( + self.num_faces + ) + face_landmarker_options_proto.face_detector_graph_options.min_detection_confidence = ( + self.min_face_detection_confidence + ) + + # Configure face landmark detector options. + face_landmarker_options_proto.min_tracking_confidence = ( + self.min_tracking_confidence + ) + face_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = ( + self.min_face_detection_confidence + ) + return face_landmarker_options_proto + + +class FaceLandmarker(base_vision_task_api.BaseVisionTaskApi): + """Class that performs face landmarks detection on images.""" + + @classmethod + def create_from_model_path(cls, model_path: str) -> 'FaceLandmarker': + """Creates an `FaceLandmarker` object from a TensorFlow Lite model and the default `FaceLandmarkerOptions`. + + Note that the created `FaceLandmarker` instance is in image mode, for + detecting face landmarks on single image inputs. + + Args: + model_path: Path to the model. + + Returns: + `FaceLandmarker` object that's created from the model file and the + default `FaceLandmarkerOptions`. + + Raises: + ValueError: If failed to create `FaceLandmarker` object from the + provided file such as invalid file path. + RuntimeError: If other types of error occurred. + """ + base_options = _BaseOptions(model_asset_path=model_path) + options = FaceLandmarkerOptions( + base_options=base_options, running_mode=_RunningMode.IMAGE + ) + return cls.create_from_options(options) + + @classmethod + def create_from_options( + cls, options: FaceLandmarkerOptions + ) -> 'FaceLandmarker': + """Creates the `FaceLandmarker` object from face landmarker options. + + Args: + options: Options for the face landmarker task. + + Returns: + `FaceLandmarker` object that's created from `options`. + + Raises: + ValueError: If failed to create `FaceLandmarker` object from + `FaceLandmarkerOptions` such as missing the model. + RuntimeError: If other types of error occurred. + """ + + def packets_callback(output_packets: Mapping[str, packet_module.Packet]): + if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty(): + return + + image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME]) + if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty(): + return + + if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty(): + empty_packet = output_packets[_NORM_LANDMARKS_STREAM_NAME] + options.result_callback( + FaceLandmarkerResult([], [], []), + image, + empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND, + ) + return + + face_landmarks_result = _build_landmarker_result(output_packets) + timestamp = output_packets[_NORM_LANDMARKS_STREAM_NAME].timestamp + options.result_callback( + face_landmarks_result, + image, + timestamp.value // _MICRO_SECONDS_PER_MILLISECOND, + ) + + output_streams = [ + ':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]), + ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]), + ] + + if options.output_face_blendshapes: + output_streams.append( + ':'.join([_BLENDSHAPES_TAG, _BLENDSHAPES_STREAM_NAME]) + ) + if options.output_facial_transformation_matrixes: + output_streams.append( + ':'.join([_FACE_GEOMETRY_TAG, _FACE_GEOMETRY_STREAM_NAME]) + ) + + task_info = _TaskInfo( + task_graph=_TASK_GRAPH_NAME, + input_streams=[ + ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]), + ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]), + ], + output_streams=output_streams, + task_options=options, + ) + return cls( + task_info.generate_graph_config( + enable_flow_limiting=options.running_mode + == _RunningMode.LIVE_STREAM + ), + options.running_mode, + packets_callback if options.result_callback else None, + ) + + def detect( + self, + image: image_module.Image, + image_processing_options: Optional[_ImageProcessingOptions] = None, + ) -> FaceLandmarkerResult: + """Performs face landmarks detection on the given image. + + Only use this method when the FaceLandmarker is created with the image + running mode. + + The image can be of any size with format RGB or RGBA. + TODO: Describes how the input image will be preprocessed after the yuv + support is implemented. + + Args: + image: MediaPipe Image. + image_processing_options: Options for image processing. + + Returns: + The face landmarks detection results. + + Raises: + ValueError: If any of the input arguments is invalid. + RuntimeError: If face landmarker detection failed to run. + """ + + normalized_rect = self.convert_to_normalized_rect( + image_processing_options, image, roi_allowed=False + ) + output_packets = self._process_image_data({ + _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image), + _NORM_RECT_STREAM_NAME: packet_creator.create_proto( + normalized_rect.to_pb2() + ), + }) + + if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty(): + return FaceLandmarkerResult([], [], []) + + return _build_landmarker_result2(output_packets) + + def detect_for_video( + self, + image: image_module.Image, + timestamp_ms: int, + image_processing_options: Optional[_ImageProcessingOptions] = None, + ): + """Performs face landmarks detection on the provided video frame. + + Only use this method when the FaceLandmarker is created with the video + running mode. + + Only use this method when the FaceLandmarker is created with the video + running mode. It's required to provide the video frame's timestamp (in + milliseconds) along with the video frame. The input timestamps should be + monotonically increasing for adjacent calls of this method. + + Args: + image: MediaPipe Image. + timestamp_ms: The timestamp of the input video frame in milliseconds. + image_processing_options: Options for image processing. + + Returns: + The face landmarks detection results. + + Raises: + ValueError: If any of the input arguments is invalid. + RuntimeError: If face landmarker detection failed to run. + """ + normalized_rect = self.convert_to_normalized_rect( + image_processing_options, image, roi_allowed=False + ) + output_packets = self._process_video_data({ + _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND + ), + _NORM_RECT_STREAM_NAME: packet_creator.create_proto( + normalized_rect.to_pb2() + ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND), + }) + + if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty(): + return FaceLandmarkerResult([], [], []) + + return _build_landmarker_result2(output_packets) + + def detect_async( + self, + image: image_module.Image, + timestamp_ms: int, + image_processing_options: Optional[_ImageProcessingOptions] = None, + ) -> None: + """Sends live image data to perform face landmarks detection. + + The results will be available via the "result_callback" provided in the + FaceLandmarkerOptions. Only use this method when the FaceLandmarker is + created with the live stream running mode. + + Only use this method when the FaceLandmarker is created with the live + stream running mode. The input timestamps should be monotonically increasing + for adjacent calls of this method. This method will return immediately after + the input image is accepted. The results will be available via the + `result_callback` provided in the `FaceLandmarkerOptions`. The + `detect_async` method is designed to process live stream data such as + camera input. To lower the overall latency, face landmarker may drop the + input images if needed. In other words, it's not guaranteed to have output + per input image. + + The `result_callback` provides: + - The face landmarks detection results. + - The input image that the face landmarker runs on. + - The input timestamp in milliseconds. + + Args: + image: MediaPipe Image. + timestamp_ms: The timestamp of the input image in milliseconds. + image_processing_options: Options for image processing. + + Raises: + ValueError: If the current input timestamp is smaller than what the + face landmarker has already processed. + """ + normalized_rect = self.convert_to_normalized_rect( + image_processing_options, image, roi_allowed=False + ) + self._send_live_stream_data({ + _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND + ), + _NORM_RECT_STREAM_NAME: packet_creator.create_proto( + normalized_rect.to_pb2() + ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND), + }) \ No newline at end of file diff --git a/src/utils/img_utils.py b/src/utils/img_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..59f09647ba7a69d0da4581cd3c8c40e0f85ff0f1 --- /dev/null +++ b/src/utils/img_utils.py @@ -0,0 +1,84 @@ +from PIL import Image +import cv2 +import numpy as np +from imageio_ffmpeg import get_ffmpeg_exe +import pathlib +import os +from IPython import embed + +def pil_to_cv2(pil): + return cv2.cvtColor(np.array(pil).astype(np.uint8), cv2.COLOR_RGB2BGR) + +def cv2_to_pil(cv2_img): + return Image.fromarray(cv2.cvtColor(cv2_img, cv2.COLOR_BGR2RGB).astype(np.uint8)) + +def center_crop_cv2(cv2_pic): + h, w = cv2_pic.shape[0], cv2_pic.shape[1] + if h > w: + return cv2_pic[(h - w) // 2 : (h - w) // 2 + w, :] + else: + return cv2_pic[:, (w - h) // 2 : (w - h) // 2 + h] + +def pils_from_video(video_path): + cap = cv2.VideoCapture(video_path) + pils = [] + while True: + ret, frame = cap.read() + if not ret: + break + + frame = cv2.resize(center_crop_cv2(frame), (512, 512)) + pils.append(cv2_to_pil(frame)) + + return pils + +def save_videos_from_pils(pils, path, fps=24): + width, height = pils[0].size + print(width, height) + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + + pathlib.Path(path).parent.mkdir(exist_ok=True, parents=True) + + output_name = pathlib.Path(path).stem + temp_output_path = path.replace(output_name, output_name + '-temp') + videowrite = cv2.VideoWriter(temp_output_path, fourcc, fps, (height, width)) + for pil in pils: + frame = pil_to_cv2(pil) + print(frame.shape, frame.min(), frame.max()) + videowrite.write(frame) + + videowrite.release() + """ + embed() + + cmd = (f'{get_ffmpeg_exe()} -i "{temp_output_path}"' + f'-map 0:v -map 1:a -c:v h264 -shortest -y "{path}" -loglevel quiet') + os.system(cmd) + os.remove(temp_output_path) + """ +def save_video_from_cv2_list(pic_cv2_list, output_path, fps=30.0): + pathlib.Path(output_path).parent.mkdir(exist_ok=True, parents=True) + + num_frames = len(pic_cv2_list) + height, width = pic_cv2_list[0].shape[:2] + + #video_tensor = video_tensor[0, ...] + #_, num_frames, height, width = video_tensor.shape + + output_name = pathlib.Path(output_path).stem + temp_output_path = output_path.replace(output_name, output_name) + video_writer = cv2.VideoWriter(temp_output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height)) + + for i in range(num_frames): + #frame_tensor = video_tensor[:, i, ...] # [c, h, w] + #frame_tensor = frame_tensor.permute(1, 2, 0) # [h, w, c] + + #frame_image = (frame_tensor * 255).numpy().astype(np.uint8) + #frame_image = cv2.cvtColor(frame_image, cv2.COLOR_RGB2BGR) + frame_image = pic_cv2_list[i].astype(np.uint8) + video_writer.write(frame_image) + + video_writer.release() + + +#ffmpeg -i input_file -c:v libx264 -crf 20 -c:a aac -strict experimental -b:a 192k output_file \ No newline at end of file diff --git a/src/utils/motion_utils.py b/src/utils/motion_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ffefa65aa976fca973f717a88b95f050e62b9ea7 --- /dev/null +++ b/src/utils/motion_utils.py @@ -0,0 +1,239 @@ +from skimage.transform import estimate_transform, AffineTransform +import numpy as np +from IPython import embed +import mediapipe as mp +import copy + +mp_face_mesh = mp.solutions.face_mesh + + +FACEMESH_LEFT_EYE = [i for i in mp_face_mesh.FACEMESH_LEFT_EYE] +FACEMESH_RIGHT_EYE = [i for i in mp_face_mesh.FACEMESH_RIGHT_EYE] +FACEMESH_LEFT_EYEBROW = [i for i in mp_face_mesh.FACEMESH_LEFT_EYEBROW] +FACEMESH_RIGHT_EYEBROW = [i for i in mp_face_mesh.FACEMESH_RIGHT_EYEBROW] + +# copy from draw_utils +FACEMESH_LIPS_OUTER_BOTTOM_LEFT = [(61,146),(146,91),(91,181),(181,84),(84,17)] +FACEMESH_LIPS_OUTER_BOTTOM_RIGHT = [(17,314),(314,405),(405,321),(321,375),(375,291)] +FACEMESH_LIPS_INNER_BOTTOM_LEFT = [(78,95),(95,88),(88,178),(178,87),(87,14)] +FACEMESH_LIPS_INNER_BOTTOM_RIGHT = [(14,317),(317,402),(402,318),(318,324),(324,308)] +FACEMESH_LIPS_OUTER_TOP_LEFT = [(61,185),(185,40),(40,39),(39,37),(37,0)] +FACEMESH_LIPS_OUTER_TOP_RIGHT = [(0,267),(267,269),(269,270),(270,409),(409,291)] +FACEMESH_LIPS_INNER_TOP_LEFT = [(78,191),(191,80),(80,81),(81,82),(82,13)] +FACEMESH_LIPS_INNER_TOP_RIGHT = [(13,312),(312,311),(311,310),(310,415),(415,308)] +FACEMESH_MOUSE = \ + FACEMESH_LIPS_OUTER_BOTTOM_LEFT + \ + FACEMESH_LIPS_OUTER_BOTTOM_RIGHT + \ + FACEMESH_LIPS_INNER_BOTTOM_LEFT + \ + FACEMESH_LIPS_INNER_BOTTOM_RIGHT + \ + FACEMESH_LIPS_OUTER_TOP_LEFT + \ + FACEMESH_LIPS_OUTER_TOP_RIGHT + \ + FACEMESH_LIPS_INNER_TOP_LEFT + \ + FACEMESH_LIPS_INNER_TOP_RIGHT + +LANDMARK_IDXES_DICT = { + "left_eye" : sorted(list(set([j for i in FACEMESH_LEFT_EYE for j in i])) + [473]), + "right_eye" : sorted(list(set([j for i in FACEMESH_RIGHT_EYE for j in i])) + [468]), + "mouse" : sorted(list(set([j for i in FACEMESH_MOUSE for j in i]))), + "nose" : sorted(list(set([1,4,5,274,275,281,44,45,51,220,440]))), + "left_eyebow" : sorted(list(set([j for i in FACEMESH_LEFT_EYEBROW for j in i]))), + "right_eyebow" : sorted(list(set([j for i in FACEMESH_RIGHT_EYEBROW for j in i]))), +} + +def create_perspective_matrix(aspect_ratio): + kDegreesToRadians = np.pi / 180. + near = 1 + far = 10000 + perspective_matrix = np.zeros(16, dtype=np.float32) + + # Standard perspective projection matrix calculations. + f = 1.0 / np.tan(kDegreesToRadians * 63 / 2.) + + denom = 1.0 / (near - far) + perspective_matrix[0] = f / aspect_ratio + perspective_matrix[5] = f + perspective_matrix[10] = (near + far) * denom + perspective_matrix[11] = -1. + perspective_matrix[14] = 1. * far * near * denom + + # If the environment's origin point location is in the top left corner, + # then skip additional flip along Y-axis is required to render correctly. + + perspective_matrix[5] *= -1. + return perspective_matrix + + +def project_points_with_trans(points_3d, transformation_matrix, image_shape): + P = create_perspective_matrix(image_shape[1] / image_shape[0]).reshape(4, 4).T + L, N, _ = points_3d.shape + projected_points = np.zeros((L, N, 2)) + #embed() + for i in range(L): + points_3d_frame = points_3d[i] + ones = np.ones((points_3d_frame.shape[0], 1)) + points_3d_homogeneous = np.hstack([points_3d_frame, ones]) + transformed_points = points_3d_homogeneous @ transformation_matrix[i].T @ P + projected_points_frame = transformed_points[:, :2] / transformed_points[:, 3, np.newaxis] # -1 ~ 1 + projected_points_frame[:, 0] = (projected_points_frame[:, 0] + 1) * 0.5 * image_shape[1] + projected_points_frame[:, 1] = (projected_points_frame[:, 1] + 1) * 0.5 * image_shape[0] + projected_points[i] = projected_points_frame + return projected_points + +def project_vertices_from_ref2tgt(ref_lmks3d, tgt_trans_mat): + #eye_point_idxes + projected_vertices = project_points_with_trans(ref_lmks3d[np.newaxis, ...], tgt_trans_mat[np.newaxis, ...], [512, 512])[0] + return projected_vertices + + +def old_motion_sync_old(sequence_driver_det, reference_det): + assert type(sequence_driver_det) is list + assert type(sequence_driver_det[0]) is type(reference_det) + + lmks3d_mean = sum([i["lmks3d"] for i in sequence_driver_det]) / len(sequence_driver_det) + overall_transform = estimate_transform('affine', lmks3d_mean, reference_det["lmks3d"]) + + eye_idxes_all = LANDMARK_IDXES_DICT["left_eye"] + LANDMARK_IDXES_DICT["right_eye"] + for det_id in range(len(sequence_driver_det)): + trans = estimate_transform('affine', sequence_driver_det[det_id]["lmks"][eye_idxes_all], sequence_driver_det[det_id]["lmks3d"][eye_idxes_all]) + sequence_driver_det[det_id]["lmks3d"] = np.vstack([ + sequence_driver_det[det_id]["lmks3d"], + trans(sequence_driver_det[det_id]["lmks"][-10:]) + ]) + + trans_mats = [] + for det in sequence_driver_det: + trans_mats.append(det["trans_mat"] @ np.linalg.inv(sequence_driver_det[0]["trans_mat"])) + + trans_mats_smooth = [] + smooth_margin = 2 + for tm_itx in range(len(trans_mats)): + smooth_idxes = [i for i in range(tm_itx - smooth_margin, tm_itx + smooth_margin + 1) if i >= 0 and i < len(trans_mats)] + tm = sum([trans_mats[i] for i in smooth_idxes]) / len(smooth_idxes) + trans_mats_smooth.append(tm) + + lmks3d_smooth = [] + smooth_margin = 1 + for det_itx in range(len(sequence_driver_det)): + smooth_idxes = [i for i in range(det_itx - smooth_margin, det_itx + smooth_margin + 1) if i >= 0 and i < len(sequence_driver_det)] + lmks3d_smooth.append(sum([sequence_driver_det[i]["lmks3d"] for i in smooth_idxes]) / len(smooth_idxes)) + + for det_itx, lmks3d in enumerate(lmks3d_smooth): + sequence_driver_det[det_itx]["lmks3d"] = lmks3d + + projected_vertices_list = [] + for det_itx in range(len(sequence_driver_det)): + aligned_3d = overall_transform(sequence_driver_det[det_itx]["lmks3d"]) + tmat = reference_det["trans_mat"] @ trans_mats_smooth[det_itx] + projected_vertices = project_vertices_from_ref2tgt(aligned_3d, tmat) + projected_vertices_list.append(projected_vertices) + + # note : use normed=False after motion_sync, when draw(ing)_landmarks + # kps_image = vis.draw_landmarks((512, 512), projected_vertices, normed=False) + return projected_vertices_list + + +def motion_sync(sequence_driver_det, reference_det, per_landmark_align=True): + assert type(sequence_driver_det) is list + assert type(sequence_driver_det[0]) is type(reference_det) + + eye_idxes_all = [i for i in sorted(list(set(LANDMARK_IDXES_DICT["left_eye"] + LANDMARK_IDXES_DICT["right_eye"]))) if i < len(reference_det["lmks3d"])] + for det_id in range(len(sequence_driver_det)): + trans_iris = estimate_transform('affine', sequence_driver_det[det_id]["lmks"][eye_idxes_all], sequence_driver_det[det_id]["lmks3d"][eye_idxes_all]) + sequence_driver_det[det_id]["lmks3d"] = np.vstack([ + sequence_driver_det[det_id]["lmks3d"], + trans_iris(sequence_driver_det[det_id]["lmks"][-10:]) + ]) + + trans_iris = estimate_transform('affine', reference_det["lmks"][eye_idxes_all], reference_det["lmks3d"][eye_idxes_all]) + reference_det["lmks3d"] = np.vstack([ + reference_det["lmks3d"], + trans_iris(reference_det["lmks"][-10:]) + ]) + + lmks3d_mean = sum([i["lmks3d"] for i in sequence_driver_det]) / len(sequence_driver_det) + + landmark_trans_dict = {} + for landmark_name, landmark_idxes in LANDMARK_IDXES_DICT.items(): + rf_lm = reference_det["lmks3d"][landmark_idxes] + dr_lm = lmks3d_mean[landmark_idxes] + landmark_trans_dict[landmark_name] = estimate_transform('affine', dr_lm, rf_lm) + + #embed() + overall_transform = estimate_transform('affine', lmks3d_mean, reference_det["lmks3d"]) + #embed() + #lmks3d_mean = sum([i["lmks3d"] for i in sequence_driver_det]) / len(sequence_driver_det) + #overall_transform = estimate_transform('affine', lmks3d_mean, reference_det["lmks3d"]) + + #driver_start_center = sequence_driver_det[0]["lmks3d"].mean(axis=0) + #reference_center = reference_det["lmks3d"].mean(axis=0) + #driver_start_size = ((sequence_driver_det[0]["lmks3d"] - driver_start_center)**2).sum()**(0.5) + #reference_size = ((reference_det["lmks3d"] - reference_center)**2).sum()**(0.5) + + #reference_det_lmks3d_rescale = (reference_det["lmks3d"] - reference_center) / reference_size * driver_start_size + driver_start_center + #reference_transform_back = estimate_transform('affine', reference_det_lmks3d_rescale, reference_det["lmks3d"]) + + #driver_lmks3d_mean = sum([i["lmks3d"] for i in sequence_driver_det]) / len(sequence_driver_det) + #facial_transform = estimate_transform('affine', driver_lmks3d_mean, reference_det_lmks3d_rescale) + + + #for det_id in range(len(sequence_driver_det)): + # trans = estimate_transform('affine', sequence_driver_det[det_id]["lmks"][:-10], sequence_driver_det[det_id]["lmks3d"]) + # sequence_driver_det[det_id]["lmks3d"] = trans(sequence_driver_det[det_id]["lmks"]) + + trans_mats = [] + for det in sequence_driver_det: + trans_mats.append(det["trans_mat"] @ np.linalg.inv(sequence_driver_det[0]["trans_mat"])) + + trans_mats_smooth = [] + smooth_margin = 2 + for tm_itx in range(len(trans_mats)): + smooth_idxes = [i for i in range(tm_itx - smooth_margin, tm_itx + smooth_margin + 1) if i >= 0 and i < len(trans_mats)] + tm = sum([trans_mats[i] for i in smooth_idxes]) / len(smooth_idxes) + trans_mats_smooth.append(tm) + + lmks3d_smooth = [] + smooth_margin = 1 + for det_itx in range(len(sequence_driver_det)): + smooth_idxes = [i for i in range(det_itx - smooth_margin, det_itx + smooth_margin + 1) if i >= 0 and i < len(sequence_driver_det)] + lmks3d_smooth.append(sum([sequence_driver_det[i]["lmks3d"] for i in smooth_idxes]) / len(smooth_idxes)) + + for det_itx, lmks3d in enumerate(lmks3d_smooth): + sequence_driver_det[det_itx]["lmks3d"] = lmks3d + + projected_vertices_list = [] + for det_itx in range(len(sequence_driver_det)): + #aligned_3d = overall_transform(sequence_driver_det[det_itx]["lmks3d"]) + aligned_3d = copy.deepcopy(sequence_driver_det[det_itx]["lmks3d"]) + if per_landmark_align: + for landmark_name, landmark_idxes in LANDMARK_IDXES_DICT.items(): + dr_lm = sequence_driver_det[det_itx]["lmks3d"][landmark_idxes] + lm_trans = landmark_trans_dict[landmark_name] + aligned_3d[landmark_idxes] = lm_trans(dr_lm) + + #aligned_3d = lmks3d_mean + tmat = trans_mats_smooth[det_itx] @ reference_det["trans_mat"] + projected_vertices = project_vertices_from_ref2tgt(aligned_3d, tmat) + projected_vertices_list.append(projected_vertices) + + continue + + trans_ref_aligned_to_driver = (sequence_driver_det[det_itx]["trans_mat"]) @ np.linalg.inv(reference_det["trans_mat"]) + ref_aligned_to_driver = AffineTransform(trans_ref_aligned_to_driver)(reference_det["lmks3d"]) + det["trans_mat"] @ np.linalg.inv(sequence_driver_det[0]["trans_mat"]) + aligned_3d = sequence_driver_det[det_itx]["lmks3d"] + + + #facial_transform(sequence_driver_det[det_itx]["lmks3d"]) + #tmat = reference_det["trans_mat"] @ trans_mats_smooth[det_itx] + + tmat = sequence_driver_det[det_itx]["trans_mat"] @ trans_mats_smooth[det_itx] #@ reference_transform_back.params + projected_vertices = project_vertices_from_ref2tgt(aligned_3d, tmat) + #embed() + #projected_vertices = reference_transform_back(projected_vertices) + projected_vertices_list.append(projected_vertices) + + # note : use normed=False after motion_sync, when draw(ing)_landmarks + # kps_image = vis.draw_landmarks((512, 512), projected_vertices, normed=False) + return projected_vertices_list + + \ No newline at end of file diff --git a/src/utils/mp_models/blaze_face_short_range.tflite b/src/utils/mp_models/blaze_face_short_range.tflite new file mode 100644 index 0000000000000000000000000000000000000000..2645898ee18d8bf53746df830303779c9deabc7d --- /dev/null +++ b/src/utils/mp_models/blaze_face_short_range.tflite @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b4578f35940bf5a1a655214a1cce5cab13eba73c1297cd78e1a04c2380b0152f +size 229746 diff --git a/src/utils/mp_models/face_landmarker_v2_with_blendshapes.task b/src/utils/mp_models/face_landmarker_v2_with_blendshapes.task new file mode 100644 index 0000000000000000000000000000000000000000..fedb14de6d2b6708a56c04ae259783e23404c1aa --- /dev/null +++ b/src/utils/mp_models/face_landmarker_v2_with_blendshapes.task @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:64184e229b263107bc2b804c6625db1341ff2bb731874b0bcc2fe6544e0bc9ff +size 3758596 diff --git a/src/utils/mp_utils.py b/src/utils/mp_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..cb4385128d1d188fc9023ee6c3df51fb8be39e2f --- /dev/null +++ b/src/utils/mp_utils.py @@ -0,0 +1,95 @@ +import os +import numpy as np +import cv2 +import time +from tqdm import tqdm +import multiprocessing +import glob + +import mediapipe as mp +from mediapipe import solutions +from mediapipe.framework.formats import landmark_pb2 +from mediapipe.tasks import python +from mediapipe.tasks.python import vision +from . import face_landmark + +CUR_DIR = os.path.dirname(__file__) + + +class LMKExtractor(): + def __init__(self, FPS=25): + # Create an FaceLandmarker object. + self.mode = mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE + base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/face_landmarker_v2_with_blendshapes.task')) + base_options.delegate = mp.tasks.BaseOptions.Delegate.CPU + options = vision.FaceLandmarkerOptions(base_options=base_options, + running_mode=self.mode, + output_face_blendshapes=True, + output_facial_transformation_matrixes=True, + num_faces=1) + self.detector = face_landmark.FaceLandmarker.create_from_options(options) + self.last_ts = 0 + self.frame_ms = int(1000 / FPS) + + det_base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/blaze_face_short_range.tflite')) + det_options = vision.FaceDetectorOptions(base_options=det_base_options) + self.det_detector = vision.FaceDetector.create_from_options(det_options) + + + def __call__(self, img): + frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame) + t0 = time.time() + if self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.VIDEO: + det_result = self.det_detector.detect(image) + if len(det_result.detections) != 1: + return None + self.last_ts += self.frame_ms + try: + detection_result, mesh3d = self.detector.detect_for_video(image, timestamp_ms=self.last_ts) + except: + return None + elif self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE: + # det_result = self.det_detector.detect(image) + + # if len(det_result.detections) != 1: + # return None + try: + detection_result, mesh3d = self.detector.detect(image) + except: + return None + + + bs_list = detection_result.face_blendshapes + if len(bs_list) == 1: + bs = bs_list[0] + bs_values = [] + for index in range(len(bs)): + bs_values.append(bs[index].score) + bs_values = bs_values[1:] # remove neutral + trans_mat = detection_result.facial_transformation_matrixes[0] + face_landmarks_list = detection_result.face_landmarks + face_landmarks = face_landmarks_list[0] + lmks = [] + for index in range(len(face_landmarks)): + x = face_landmarks[index].x + y = face_landmarks[index].y + z = face_landmarks[index].z + lmks.append([x, y, z]) + lmks = np.array(lmks) + + lmks3d = np.array(mesh3d.vertex_buffer) + lmks3d = lmks3d.reshape(-1, 5)[:, :3] + mp_tris = np.array(mesh3d.index_buffer).reshape(-1, 3) + 1 + + return { + "lmks": lmks, + 'lmks3d': lmks3d, + "trans_mat": trans_mat, + 'faces': mp_tris, + "bs": bs_values + } + else: + # print('multiple faces in the image: {}'.format(img_path)) + return None + \ No newline at end of file diff --git a/src/utils/step_func.py b/src/utils/step_func.py new file mode 100644 index 0000000000000000000000000000000000000000..dd74f5b0329c1289703fd7f658c649b495e4ebb2 --- /dev/null +++ b/src/utils/step_func.py @@ -0,0 +1,37 @@ +import torch +import numpy as np + +def get_alpha(alphas_cumprod, timestep): + timestep_lt_zero_mask = torch.lt(timestep, 0).to(alphas_cumprod.dtype) + normal_alpha = alphas_cumprod[torch.clip(timestep, 0)] + one_alpha = torch.ones_like(normal_alpha).to(normal_alpha.dtype).to(normal_alpha.dtype) + return normal_alpha * (1 - timestep_lt_zero_mask) + one_alpha * timestep_lt_zero_mask + +def psuedo_velocity_wrt_noisy_and_timestep(noisy_images, noisy_images_pre, alphas_cumprod, timestep, timestep_prev): + alpha_prod_t = get_alpha(alphas_cumprod, timestep).view(-1, 1, 1, 1, 1).detach() + beta_prod_t = 1 - alpha_prod_t + alpha_prod_t_prev = get_alpha(alphas_cumprod, timestep_prev).view(-1, 1, 1, 1, 1).detach() + beta_prod_t_prev = 1 - alpha_prod_t_prev + + a_s = (alpha_prod_t_prev ** (0.5)).to(noisy_images.dtype) + a_t = (alpha_prod_t ** (0.5)).to(noisy_images.dtype) + b_s = (beta_prod_t_prev ** (0.5)).to(noisy_images.dtype) + b_t = (beta_prod_t ** (0.5)).to(noisy_images.dtype) + + psuedo_velocity = (noisy_images_pre - ( + a_s * a_t + b_s * b_t + ) * noisy_images) / ( + b_s * a_t - a_s * b_t + ) + + return psuedo_velocity + +def origin_by_velocity_and_sample(velocity, noisy_images, alphas_cumprod, timestep): + alpha_prod_t = get_alpha(alphas_cumprod, timestep).view(-1, 1, 1, 1, 1).detach() + beta_prod_t = 1 - alpha_prod_t + a_t = (alpha_prod_t ** (0.5)).to(noisy_images.dtype) + b_t = (beta_prod_t ** (0.5)).to(noisy_images.dtype) + + pred_original_sample = a_t * noisy_images - b_t * velocity + return pred_original_sample + \ No newline at end of file diff --git a/src/utils/util.py b/src/utils/util.py new file mode 100644 index 0000000000000000000000000000000000000000..6a057953a0fc4d6b635fc5daf67b39e7ffb86000 --- /dev/null +++ b/src/utils/util.py @@ -0,0 +1,165 @@ +import importlib +import os +import os.path as osp +import shutil +import sys +from pathlib import Path + +import av +import numpy as np +import torch +import torchvision +from einops import rearrange +from PIL import Image + + +def seed_everything(seed): + import random + + import numpy as np + + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed % (2**32)) + random.seed(seed) + + +def import_filename(filename): + spec = importlib.util.spec_from_file_location("mymodule", filename) + module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = module + spec.loader.exec_module(module) + return module + + +def delete_additional_ckpt(base_path, num_keep): + dirs = [] + for d in os.listdir(base_path): + if d.startswith("checkpoint-"): + dirs.append(d) + num_tot = len(dirs) + if num_tot <= num_keep: + return + # ensure ckpt is sorted and delete the ealier! + del_dirs = sorted(dirs, key=lambda x: int(x.split("-")[-1]))[: num_tot - num_keep] + for d in del_dirs: + path_to_dir = osp.join(base_path, d) + if osp.exists(path_to_dir): + shutil.rmtree(path_to_dir) + + +def save_videos_from_pil(pil_images, path, fps=8, audio_path=None): + import av + + save_fmt = Path(path).suffix + os.makedirs(os.path.dirname(path), exist_ok=True) + width, height = pil_images[0].size + + if save_fmt == ".mp4": + codec = "libx264" + container = av.open(path, "w") + stream = container.add_stream(codec, rate=fps) + + stream.width = width + stream.height = height + + for pil_image in pil_images: + # pil_image = Image.fromarray(image_arr).convert("RGB") + av_frame = av.VideoFrame.from_image(pil_image) + container.mux(stream.encode(av_frame)) + container.mux(stream.encode()) + container.close() + + elif save_fmt == ".gif": + pil_images[0].save( + fp=path, + format="GIF", + append_images=pil_images[1:], + save_all=True, + duration=(1 / fps * 1000), + loop=0, + ) + else: + raise ValueError("Unsupported file type. Use .mp4 or .gif.") + + +def save_videos_grid(videos: torch.Tensor, path: str, audio_path=None, rescale=False, n_rows=6, fps=8): + videos = rearrange(videos, "b c t h w -> t b c h w") + height, width = videos.shape[-2:] + outputs = [] + + for x in videos: + x = torchvision.utils.make_grid(x, nrow=n_rows) # (c h w) + x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) # (h w c) + if rescale: + x = (x + 1.0) / 2.0 # -1,1 -> 0,1 + x = (x * 255).numpy().astype(np.uint8) + x = Image.fromarray(x) + + outputs.append(x) + + os.makedirs(os.path.dirname(path), exist_ok=True) + + save_videos_from_pil(outputs, path, fps, audio_path=audio_path) + + +def read_frames(video_path): + container = av.open(video_path) + + video_stream = next(s for s in container.streams if s.type == "video") + frames = [] + for packet in container.demux(video_stream): + for frame in packet.decode(): + image = Image.frombytes( + "RGB", + (frame.width, frame.height), + frame.to_rgb().to_ndarray(), + ) + frames.append(image) + + return frames + + +def get_fps(video_path): + container = av.open(video_path) + video_stream = next(s for s in container.streams if s.type == "video") + fps = video_stream.average_rate + container.close() + return fps + + +def crop_and_pad(image, rect): + x0, y0, x1, y1 = rect + h, w = image.shape[:2] + + # 确保坐标在图像范围内 + x0, y0 = max(0, x0), max(0, y0) + x1, y1 = min(w, x1), min(h, y1) + + # 计算原始框的宽度和高度 + width = x1 - x0 + height = y1 - y0 + + # 使用较小的边长作为裁剪正方形的边长 + side_length = min(width, height) + + # 计算正方形框中心点 + center_x = (x0 + x1) // 2 + center_y = (y0 + y1) // 2 + + # 重新计算正方形框的坐标 + new_x0 = max(0, center_x - side_length // 2) + new_y0 = max(0, center_y - side_length // 2) + new_x1 = min(w, new_x0 + side_length) + new_y1 = min(h, new_y0 + side_length) + + # 最终裁剪框的尺寸修正(确保是正方形) + if (new_x1 - new_x0) != (new_y1 - new_y0): + side_length = min(new_x1 - new_x0, new_y1 - new_y0) + new_x1 = new_x0 + side_length + new_y1 = new_y0 + side_length + + # 裁剪图像 + cropped_image = image[new_y0:new_y1, new_x0:new_x1] + + return cropped_image \ No newline at end of file diff --git a/webgui.py b/webgui.py new file mode 100644 index 0000000000000000000000000000000000000000..5e46781141356e9daa73516523fe6373d0c18f2a --- /dev/null +++ b/webgui.py @@ -0,0 +1,269 @@ +#!/usr/bin/env python +# -*- coding: UTF-8 -*- +''' +webui +''' + +import os +import random +from datetime import datetime +from pathlib import Path + +import cv2 +import numpy as np +import torch +from diffusers import AutoencoderKL, DDIMScheduler +from omegaconf import OmegaConf +from PIL import Image +from src.models.unet_2d_condition import UNet2DConditionModel +from src.models.unet_3d_echo import EchoUNet3DConditionModel +from src.models.whisper.audio2feature import load_audio_model +from src.pipelines.pipeline_echo_mimic import Audio2VideoPipeline +from src.utils.util import save_videos_grid, crop_and_pad +from src.models.face_locator import FaceLocator +from moviepy.editor import VideoFileClip, AudioFileClip +from facenet_pytorch import MTCNN +import argparse + +import gradio as gr + +default_values = { + "width": 512, + "height": 512, + "length": 1200, + "seed": 420, + "facemask_dilation_ratio": 0.1, + "facecrop_dilation_ratio": 0.5, + "context_frames": 12, + "context_overlap": 3, + "cfg": 2.5, + "steps": 30, + "sample_rate": 16000, + "fps": 24, + "device": "cuda" +} + +ffmpeg_path = os.getenv('FFMPEG_PATH') +if ffmpeg_path is None: + print("please download ffmpeg-static and export to FFMPEG_PATH. \nFor example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static") +elif ffmpeg_path not in os.getenv('PATH'): + print("add ffmpeg to path") + os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}" + + +config_path = "./configs/prompts/animation.yaml" +config = OmegaConf.load(config_path) +if config.weight_dtype == "fp16": + weight_dtype = torch.float16 +else: + weight_dtype = torch.float32 + +device = "cuda" +if not torch.cuda.is_available(): + device = "cpu" + +inference_config_path = config.inference_config +infer_config = OmegaConf.load(inference_config_path) + +############# model_init started ############# +## vae init +vae = AutoencoderKL.from_pretrained(config.pretrained_vae_path).to("cuda", dtype=weight_dtype) + +## reference net init +reference_unet = UNet2DConditionModel.from_pretrained( + config.pretrained_base_model_path, + subfolder="unet", +).to(dtype=weight_dtype, device=device) +reference_unet.load_state_dict(torch.load(config.reference_unet_path, map_location="cpu")) + +## denoising net init +if os.path.exists(config.motion_module_path): + ### stage1 + stage2 + denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d( + config.pretrained_base_model_path, + config.motion_module_path, + subfolder="unet", + unet_additional_kwargs=infer_config.unet_additional_kwargs, + ).to(dtype=weight_dtype, device=device) +else: + ### only stage1 + denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d( + config.pretrained_base_model_path, + "", + subfolder="unet", + unet_additional_kwargs={ + "use_motion_module": False, + "unet_use_temporal_attention": False, + "cross_attention_dim": infer_config.unet_additional_kwargs.cross_attention_dim + } + ).to(dtype=weight_dtype, device=device) + +denoising_unet.load_state_dict(torch.load(config.denoising_unet_path, map_location="cpu"), strict=False) + +## face locator init +face_locator = FaceLocator(320, conditioning_channels=1, block_out_channels=(16, 32, 96, 256)).to(dtype=weight_dtype, device="cuda") +face_locator.load_state_dict(torch.load(config.face_locator_path)) + +## load audio processor params +audio_processor = load_audio_model(model_path=config.audio_model_path, device=device) + +## load face detector params +face_detector = MTCNN(image_size=320, margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, device=device) + +############# model_init finished ############# + +sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs) +scheduler = DDIMScheduler(**sched_kwargs) + +pipe = Audio2VideoPipeline( + vae=vae, + reference_unet=reference_unet, + denoising_unet=denoising_unet, + audio_guider=audio_processor, + face_locator=face_locator, + scheduler=scheduler, +).to("cuda", dtype=weight_dtype) + +def select_face(det_bboxes, probs): + ## max face from faces that the prob is above 0.8 + ## box: xyxy + if det_bboxes is None or probs is None: + return None + filtered_bboxes = [] + for bbox_i in range(len(det_bboxes)): + if probs[bbox_i] > 0.8: + filtered_bboxes.append(det_bboxes[bbox_i]) + if len(filtered_bboxes) == 0: + return None + sorted_bboxes = sorted(filtered_bboxes, key=lambda x:(x[3]-x[1]) * (x[2] - x[0]), reverse=True) + return sorted_bboxes[0] + +def process_video(uploaded_img, uploaded_audio, width, height, length, seed, facemask_dilation_ratio, facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, device): + + if seed is not None and seed > -1: + generator = torch.manual_seed(seed) + else: + generator = torch.manual_seed(random.randint(100, 1000000)) + + #### face musk prepare + face_img = cv2.imread(uploaded_img) + face_mask = np.zeros((face_img.shape[0], face_img.shape[1])).astype('uint8') + det_bboxes, probs = face_detector.detect(face_img) + select_bbox = select_face(det_bboxes, probs) + if select_bbox is None: + face_mask[:, :] = 255 + else: + xyxy = select_bbox[:4] + xyxy = np.round(xyxy).astype('int') + rb, re, cb, ce = xyxy[1], xyxy[3], xyxy[0], xyxy[2] + r_pad = int((re - rb) * facemask_dilation_ratio) + c_pad = int((ce - cb) * facemask_dilation_ratio) + face_mask[rb - r_pad : re + r_pad, cb - c_pad : ce + c_pad] = 255 + + #### face crop + r_pad_crop = int((re - rb) * facecrop_dilation_ratio) + c_pad_crop = int((ce - cb) * facecrop_dilation_ratio) + crop_rect = [max(0, cb - c_pad_crop), max(0, rb - r_pad_crop), min(ce + c_pad_crop, face_img.shape[1]), min(re + r_pad_crop, face_img.shape[0])] + face_img = crop_and_pad(face_img, crop_rect) + face_mask = crop_and_pad(face_mask, crop_rect) + face_img = cv2.resize(face_img, (width, height)) + face_mask = cv2.resize(face_mask, (width, height)) + + ref_image_pil = Image.fromarray(face_img[:, :, [2, 1, 0]]) + face_mask_tensor = torch.Tensor(face_mask).to(dtype=weight_dtype, device="cuda").unsqueeze(0).unsqueeze(0).unsqueeze(0) / 255.0 + + video = pipe( + ref_image_pil, + uploaded_audio, + face_mask_tensor, + width, + height, + length, + steps, + cfg, + generator=generator, + audio_sample_rate=sample_rate, + context_frames=context_frames, + fps=fps, + context_overlap=context_overlap + ).videos + + save_dir = Path("output/tmp") + save_dir.mkdir(exist_ok=True, parents=True) + output_video_path = save_dir / "output_video.mp4" + save_videos_grid(video, str(output_video_path), n_rows=1, fps=fps) + + video_clip = VideoFileClip(str(output_video_path)) + audio_clip = AudioFileClip(uploaded_audio) + final_output_path = save_dir / "output_video_with_audio.mp4" + video_clip = video_clip.set_audio(audio_clip) + video_clip.write_videofile(str(final_output_path), codec="libx264", audio_codec="aac") + + return final_output_path + +with gr.Blocks() as demo: + gr.Markdown('# EchoMimic') + gr.Markdown('![]()') + with gr.Row(): + with gr.Column(): + uploaded_img = gr.Image(type="filepath", label="Reference Image") + uploaded_audio = gr.Audio(type="filepath", label="Input Audio") + with gr.Column(): + output_video = gr.Video() + + with gr.Accordion("Configuration", open=False): + width = gr.Slider(label="Width", minimum=128, maximum=1024, value=default_values["width"]) + height = gr.Slider(label="Height", minimum=128, maximum=1024, value=default_values["height"]) + length = gr.Slider(label="Length", minimum=100, maximum=5000, value=default_values["length"]) + seed = gr.Slider(label="Seed", minimum=0, maximum=10000, value=default_values["seed"]) + facemask_dilation_ratio = gr.Slider(label="Facemask Dilation Ratio", minimum=0.0, maximum=1.0, step=0.01, value=default_values["facemask_dilation_ratio"]) + facecrop_dilation_ratio = gr.Slider(label="Facecrop Dilation Ratio", minimum=0.0, maximum=1.0, step=0.01, value=default_values["facecrop_dilation_ratio"]) + context_frames = gr.Slider(label="Context Frames", minimum=0, maximum=50, step=1, value=default_values["context_frames"]) + context_overlap = gr.Slider(label="Context Overlap", minimum=0, maximum=10, step=1, value=default_values["context_overlap"]) + cfg = gr.Slider(label="CFG", minimum=0.0, maximum=10.0, step=0.1, value=default_values["cfg"]) + steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=default_values["steps"]) + sample_rate = gr.Slider(label="Sample Rate", minimum=8000, maximum=48000, step=1000, value=default_values["sample_rate"]) + fps = gr.Slider(label="FPS", minimum=1, maximum=60, step=1, value=default_values["fps"]) + device = gr.Radio(label="Device", choices=["cuda", "cpu"], value=default_values["device"]) + + generate_button = gr.Button("Generate Video") + + def generate_video(uploaded_img, uploaded_audio, width, height, length, seed, facemask_dilation_ratio, facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, device): + + final_output_path = process_video( + uploaded_img, uploaded_audio, width, height, length, seed, facemask_dilation_ratio, facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, device + ) + output_video= final_output_path + return final_output_path + + generate_button.click( + generate_video, + inputs=[ + uploaded_img, + uploaded_audio, + width, + height, + length, + seed, + facemask_dilation_ratio, + facecrop_dilation_ratio, + context_frames, + context_overlap, + cfg, + steps, + sample_rate, + fps, + device + ], + outputs=output_video + ) +parser = argparse.ArgumentParser(description='EchoMimic') +parser.add_argument('--server_name', type=str, default='0.0.0.0', help='Server name') +parser.add_argument('--server_port', type=int, default=7680, help='Server port') +args = parser.parse_args() + +# demo.launch(server_name=args.server_name, server_port=args.server_port, inbrowser=True) + +if __name__ == '__main__': + #demo.launch(server_name='0.0.0.0') + demo.launch(server_name=args.server_name, server_port=args.server_port, inbrowser=True)