import cv2 import einops import numpy as np import torch import random import gradio as gr import os import albumentations as A from PIL import Image import torchvision.transforms as T from datasets.data_utils import * from cldm.model import create_model, load_state_dict from cldm.ddim_hacked import DDIMSampler from omegaconf import OmegaConf from cldm.hack import disable_verbosity, enable_sliced_attention cv2.setNumThreads(0) cv2.ocl.setUseOpenCL(False) save_memory = False disable_verbosity() if save_memory: enable_sliced_attention() config = OmegaConf.load('./configs/demo.yaml') model_ckpt = config.pretrained_model model_config = config.config_file model = create_model(model_config ).cpu() model.load_state_dict(load_state_dict(model_ckpt, location='cuda')) model = model.cuda() ddim_sampler = DDIMSampler(model) def crop_back( pred, tar_image, extra_sizes, tar_box_yyxx_crop): H1, W1, H2, W2 = extra_sizes y1,y2,x1,x2 = tar_box_yyxx_crop pred = cv2.resize(pred, (W2, H2)) m = 3 # maigin_pixel if W1 == H1: tar_image[y1+m :y2-m, x1+m:x2-m, :] = pred[m:-m, m:-m] return tar_image if W1 < W2: pad1 = int((W2 - W1) / 2) pad2 = W2 - W1 - pad1 pred = pred[:,pad1: -pad2, :] else: pad1 = int((H2 - H1) / 2) pad2 = H2 - H1 - pad1 pred = pred[pad1: -pad2, :, :] tar_image[y1+m :y2-m, x1+m:x2-m, :] = pred[m:-m, m:-m] return tar_image def inference_single_image(ref_image, ref_mask, tar_image, tar_mask, num_samples, strength, ddim_steps, scale, seed, ): item = process_pairs(ref_image, ref_mask, tar_image, tar_mask) ref = item['ref'] hint = item['hint'] num_samples = 1 control = torch.from_numpy(hint.copy()).float().cuda() control = torch.stack([control for _ in range(num_samples)], dim=0) control = einops.rearrange(control, 'b h w c -> b c h w').clone() clip_input = torch.from_numpy(ref.copy()).float().cuda() clip_input = torch.stack([clip_input for _ in range(num_samples)], dim=0) clip_input = einops.rearrange(clip_input, 'b h w c -> b c h w').clone() H,W = 512,512 cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning( clip_input )]} un_cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([torch.zeros((1,3,224,224))] * num_samples)]} shape = (4, H // 8, W // 8) if save_memory: model.low_vram_shift(is_diffusing=True) model.control_scales = ([strength] * 13) samples, _ = ddim_sampler.sample(ddim_steps, num_samples, shape, cond, verbose=False, eta=0, unconditional_guidance_scale=scale, unconditional_conditioning=un_cond) if save_memory: model.low_vram_shift(is_diffusing=False) x_samples = model.decode_first_stage(samples) x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy() result = x_samples[0][:,:,::-1] result = np.clip(result,0,255) pred = x_samples[0] pred = np.clip(pred,0,255)[1:,:,:] sizes = item['extra_sizes'] tar_box_yyxx_crop = item['tar_box_yyxx_crop'] tar_image = crop_back(pred, tar_image, sizes, tar_box_yyxx_crop) return tar_image def process_pairs(ref_image, ref_mask, tar_image, tar_mask, max_ratio = 0.8): # ========= Reference =========== # ref expand ref_box_yyxx = get_bbox_from_mask(ref_mask) # ref filter mask ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1) masked_ref_image = ref_image * ref_mask_3 + np.ones_like(ref_image) * 255 * (1-ref_mask_3) y1,y2,x1,x2 = ref_box_yyxx masked_ref_image = masked_ref_image[y1:y2,x1:x2,:] ref_mask = ref_mask[y1:y2,x1:x2] ratio = np.random.randint(11, 15) / 10 #11,13 masked_ref_image, ref_mask = expand_image_mask(masked_ref_image, ref_mask, ratio=ratio) ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1) # to square and resize masked_ref_image = pad_to_square(masked_ref_image, pad_value = 255, random = False) masked_ref_image = cv2.resize(masked_ref_image.astype(np.uint8), (224,224) ).astype(np.uint8) ref_mask_3 = pad_to_square(ref_mask_3 * 255, pad_value = 0, random = False) ref_mask_3 = cv2.resize(ref_mask_3.astype(np.uint8), (224,224) ).astype(np.uint8) ref_mask = ref_mask_3[:,:,0] # collage aug masked_ref_image_compose, ref_mask_compose = masked_ref_image, ref_mask ref_mask_3 = np.stack([ref_mask_compose,ref_mask_compose,ref_mask_compose],-1) ref_image_collage = sobel(masked_ref_image_compose, ref_mask_compose/255) # ========= Target =========== tar_box_yyxx = get_bbox_from_mask(tar_mask) tar_box_yyxx = expand_bbox(tar_mask, tar_box_yyxx, ratio=[1.1,1.2]) #1.1 1.3 # crop tar_box_yyxx_crop = expand_bbox(tar_image, tar_box_yyxx, ratio=[1.3, 3.0]) tar_box_yyxx_crop = box2squre(tar_image, tar_box_yyxx_crop) # crop box y1,y2,x1,x2 = tar_box_yyxx_crop cropped_target_image = tar_image[y1:y2,x1:x2,:] cropped_tar_mask = tar_mask[y1:y2,x1:x2] tar_box_yyxx = box_in_box(tar_box_yyxx, tar_box_yyxx_crop) y1,y2,x1,x2 = tar_box_yyxx # collage ref_image_collage = cv2.resize(ref_image_collage.astype(np.uint8), (x2-x1, y2-y1)) ref_mask_compose = cv2.resize(ref_mask_compose.astype(np.uint8), (x2-x1, y2-y1)) ref_mask_compose = (ref_mask_compose > 128).astype(np.uint8) collage = cropped_target_image.copy() collage[y1:y2,x1:x2,:] = ref_image_collage collage_mask = cropped_target_image.copy() * 0.0 collage_mask[y1:y2,x1:x2,:] = 1.0 collage_mask = np.stack([cropped_tar_mask,cropped_tar_mask,cropped_tar_mask],-1) # the size before pad H1, W1 = collage.shape[0], collage.shape[1] cropped_target_image = pad_to_square(cropped_target_image, pad_value = 0, random = False).astype(np.uint8) collage = pad_to_square(collage, pad_value = 0, random = False).astype(np.uint8) collage_mask = pad_to_square(collage_mask, pad_value = 0, random = False).astype(np.uint8) # the size after pad H2, W2 = collage.shape[0], collage.shape[1] cropped_target_image = cv2.resize(cropped_target_image.astype(np.uint8), (512,512)).astype(np.float32) collage = cv2.resize(collage.astype(np.uint8), (512,512)).astype(np.float32) collage_mask = (cv2.resize(collage_mask.astype(np.uint8), (512,512)).astype(np.float32) > 0.5).astype(np.float32) masked_ref_image = masked_ref_image / 255 cropped_target_image = cropped_target_image / 127.5 - 1.0 collage = collage / 127.5 - 1.0 collage = np.concatenate([collage, collage_mask[:,:,:1] ] , -1) item = dict(ref=masked_ref_image.copy(), jpg=cropped_target_image.copy(), hint=collage.copy(), extra_sizes=np.array([H1, W1, H2, W2]), tar_box_yyxx_crop=np.array( tar_box_yyxx_crop ) ) return item ref_dir='./examples/Gradio/FG' image_dir='./examples/Gradio/BG' ref_list=[os.path.join(ref_dir,file) for file in os.listdir(ref_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file ] ref_list.sort() image_list=[os.path.join(image_dir,file) for file in os.listdir(image_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file] image_list.sort() def mask_image(image, mask): blanc = np.ones_like(image) * 255 mask = np.stack([mask,mask,mask],-1) / 255 masked_image = mask * ( 0.5 * blanc + 0.5 * image) + (1-mask) * image return masked_image.astype(np.uint8) def run_local(base, ref, *args): image = base["image"].convert("RGB") mask = base["mask"].convert("L") ref_image = ref["image"].convert("RGB") ref_mask = ref["mask"].convert("L") image = np.asarray(image) mask = np.asarray(mask) mask = np.where(mask > 128, 255, 0).astype(np.uint8) ref_image = np.asarray(ref_image) ref_mask = np.asarray(ref_mask) ref_mask = np.where(ref_mask > 128, 1, 0).astype(np.uint8) processed_item = process_pairs(ref_image.copy(), ref_mask.copy(), image.copy(), mask.copy(), max_ratio = 0.8) masked_ref = (processed_item['ref']*255) mased_image = mask_image(image, mask) #synthesis = image synthesis = inference_single_image(ref_image.copy(), ref_mask.copy(), image.copy(), mask.copy(), *args) synthesis = torch.from_numpy(synthesis).permute(2, 0, 1) synthesis = synthesis.permute(1, 2, 0).numpy() masked_ref = cv2.resize(masked_ref.astype(np.uint8), (512,512)) return [synthesis] with gr.Blocks() as demo: with gr.Column(): gr.Markdown("# Play with AnyDoor to Teleport your Target Objects! ") with gr.Row(): baseline_gallery = gr.Gallery(label='Output', show_label=True, elem_id="gallery", columns=1, height=768) with gr.Accordion("Advanced Option", open=True): num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=30, step=1) scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=3.0, step=0.1) seed = gr.Slider(label="Seed", minimum=-1, maximum=999999999, step=1, value=-1) gr.Markdown(" Higher guidance-scale makes higher fidelity, while lower guidance-scale leads to more harmonized blending.") gr.Markdown("# Upload / Select Images for the Background (left) and Reference Object (right)") gr.Markdown("### Your could draw coarse masks on the background to indicate the desired location and shape.") gr.Markdown("### Do not forget to annotate the target object on the reference image.") with gr.Row(): base = gr.Image(label="Background", source="upload", tool="sketch", type="pil", height=512, brush_color='#FFFFFF', mask_opacity=0.5) ref = gr.Image(label="Reference", source="upload", tool="sketch", type="pil", height=512, brush_color='#FFFFFF', mask_opacity=0.5) run_local_button = gr.Button(label="Generate", value="Run") with gr.Row(): with gr.Column(): gr.Examples(image_list, inputs=[base],label="Examples - Background Image",examples_per_page=16) with gr.Column(): gr.Examples(ref_list, inputs=[ref],label="Examples - Reference Object",examples_per_page=16) run_local_button.click(fn=run_local, inputs=[base, ref, num_samples, strength, ddim_steps, scale, seed, ], outputs=[baseline_gallery] ) demo.launch(server_name="0.0.0.0")