from __future__ import annotations import math import random import sys from argparse import ArgumentParser from tqdm.auto import trange import einops import gradio as gr import k_diffusion as K import numpy as np import torch import torch.nn as nn from einops import rearrange from omegaconf import OmegaConf from PIL import Image, ImageOps, ImageFilter from torch import autocast import cv2 import imageio sys.path.append("./stable_diffusion") from stable_diffusion.ldm.util import instantiate_from_config class CFGDenoiser(nn.Module): def __init__(self, model): super().__init__() self.inner_model = model def forward(self, z_0, z_1, sigma, cond, uncond, text_cfg_scale, image_cfg_scale): cfg_z_0 = einops.repeat(z_0, "1 ... -> n ...", n=3) cfg_z_1 = einops.repeat(z_1, "1 ... -> n ...", n=3) cfg_sigma = einops.repeat(sigma, "1 ... -> n ...", n=3) cfg_cond = { "c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], uncond["c_crossattn"][0]])], "c_concat": [torch.cat([cond["c_concat"][0], cond["c_concat"][0], uncond["c_concat"][0]])], } output_0, output_1 = self.inner_model(cfg_z_0, cfg_z_1, cfg_sigma, cond=cfg_cond) out_cond_0, out_img_cond_0, out_uncond_0 = output_0.chunk(3) out_cond_1, _, _ = output_1.chunk(3) return out_uncond_0 + text_cfg_scale * (out_cond_0 - out_img_cond_0) + image_cfg_scale * (out_img_cond_0 - out_uncond_0), \ out_cond_1 def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] if vae_ckpt is not None: print(f"Loading VAE from {vae_ckpt}") vae_sd = torch.load(vae_ckpt, map_location="cpu")["state_dict"] sd = { k: vae_sd[k[len("first_stage_model.") :]] if k.startswith("first_stage_model.") else v for k, v in sd.items() } model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=True) if len(m) > 0 and verbose: print("missing keys:") print(m) if len(u) > 0 and verbose: print("unexpected keys:") print(u) return model def append_dims(x, target_dims): """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" dims_to_append = target_dims - x.ndim if dims_to_append < 0: raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') return x[(...,) + (None,) * dims_to_append] class CompVisDenoiser(K.external.CompVisDenoiser): def __init__(self, model, quantize=False, device='cpu'): super().__init__( model, quantize, device) def get_eps(self, *args, **kwargs): return self.inner_model.apply_model(*args, **kwargs) def forward(self, input_0, input_1, sigma, **kwargs): c_out, c_in = [append_dims(x, input_0.ndim) for x in self.get_scalings(sigma)] # eps_0, eps_1 = self.get_eps(input_0 * c_in, input_1 * c_in, self.sigma_to_t(sigma), **kwargs) eps_0, eps_1 = self.get_eps(input_0 * c_in, self.sigma_to_t(sigma), **kwargs) return input_0 + eps_0 * c_out, eps_1 def to_d(x, sigma, denoised): """Converts a denoiser output to a Karras ODE derivative.""" return (x - denoised) / append_dims(sigma, x.ndim) def default_noise_sampler(x): return lambda sigma, sigma_next: torch.randn_like(x) def get_ancestral_step(sigma_from, sigma_to, eta=1.): """Calculates the noise level (sigma_down) to step down to and the amount of noise to add (sigma_up) when doing an ancestral sampling step.""" if not eta: return sigma_to, 0. sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 return sigma_down, sigma_up def decode_mask(mask, height = 256, width = 256): mask = nn.functional.interpolate(mask, size=(height, width), mode="bilinear", align_corners=False) mask = torch.where(mask > 0, 1, -1) # Thresholding step mask = torch.clamp((mask + 1.0) / 2.0, min=0.0, max=1.0) mask = 255.0 * rearrange(mask, "1 c h w -> h w c") mask = torch.cat([mask, mask, mask], dim=-1) mask = mask.type(torch.uint8).cpu().numpy() return mask @torch.no_grad() def sample_euler_ancestral(model, x_0, x_1, sigmas, height, width, extra_args=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with Euler method steps.""" extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x_0) if noise_sampler is None else noise_sampler s_in = x_0.new_ones([x_0.shape[0]]) mask_list = [] image_list = [] for i in trange(len(sigmas) - 1, disable=disable): denoised_0, denoised_1 = model(x_0, x_1, sigmas[i] * s_in, **extra_args) image_list.append(denoised_0) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) d_0 = to_d(x_0, sigmas[i], denoised_0) # Euler method dt = sigma_down - sigmas[i] x_0 = x_0 + d_0 * dt if sigmas[i + 1] > 0: x_0 = x_0 + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up x_1 = denoised_1 mask_list.append(decode_mask(x_1, height, width)) image_list = torch.cat(image_list, dim=0) return x_0, x_1, image_list, mask_list parser = ArgumentParser() parser.add_argument("--resolution", default=512, type=int) parser.add_argument("--config", default="configs/generate_diffree.yaml", type=str) parser.add_argument("--ckpt", default="checkpoints/epoch=000041-step=000010999.ckpt", type=str) parser.add_argument("--vae-ckpt", default=None, type=str) args = parser.parse_args() config = OmegaConf.load(args.config) model = load_model_from_config(config, args.ckpt, args.vae_ckpt) model.eval().cuda() model_wrap = CompVisDenoiser(model) model_wrap_cfg = CFGDenoiser(model_wrap) null_token = model.get_learned_conditioning([""]) def generate( input_image: Image.Image, instruction: str, steps: int, randomize_seed: bool, seed: int, randomize_cfg: bool, text_cfg_scale: float, image_cfg_scale: float, ): seed = random.randint(0, 100000) if randomize_seed else seed text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale width, height = input_image.size factor = args.resolution / max(width, height) factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height) width = int((width * factor) // 64) * 64 height = int((height * factor) // 64) * 64 input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS) input_image_copy = input_image.convert("RGB") if instruction == "": return [input_image, seed] with torch.no_grad(), autocast("cuda"), model.ema_scope(): cond = {} cond["c_crossattn"] = [model.get_learned_conditioning([instruction])] input_image = 2 * torch.tensor(np.array(input_image)).float() / 255 - 1 input_image = rearrange(input_image, "h w c -> 1 c h w").to(model.device) cond["c_concat"] = [model.encode_first_stage(input_image).mode()] uncond = {} uncond["c_crossattn"] = [null_token] uncond["c_concat"] = [torch.zeros_like(cond["c_concat"][0])] sigmas = model_wrap.get_sigmas(steps) extra_args = { "cond": cond, "uncond": uncond, "text_cfg_scale": text_cfg_scale, "image_cfg_scale": image_cfg_scale, } torch.manual_seed(seed) z_0 = torch.randn_like(cond["c_concat"][0]) * sigmas[0] z_1 = torch.randn_like(cond["c_concat"][0]) * sigmas[0] z_0, z_1, image_list, mask_list = sample_euler_ancestral(model_wrap_cfg, z_0, z_1, sigmas, height, width, extra_args=extra_args) x_0 = model.decode_first_stage(z_0) if model.first_stage_downsample: x_1 = nn.functional.interpolate(z_1, size=(height, width), mode="bilinear", align_corners=False) x_1 = torch.where(x_1 > 0, 1, -1) # Thresholding step else: x_1 = model.decode_first_stage(z_1) x_0 = torch.clamp((x_0 + 1.0) / 2.0, min=0.0, max=1.0) x_1 = torch.clamp((x_1 + 1.0) / 2.0, min=0.0, max=1.0) x_0 = 255.0 * rearrange(x_0, "1 c h w -> h w c") x_1 = 255.0 * rearrange(x_1, "1 c h w -> h w c") x_1 = torch.cat([x_1, x_1, x_1], dim=-1) edited_image = Image.fromarray(x_0.type(torch.uint8).cpu().numpy()) edited_mask = Image.fromarray(x_1.type(torch.uint8).cpu().numpy()) image_video = [] batch_size = 50 for i in range(0, len(image_list), batch_size): if i + batch_size < len(image_list): tmp_image_list = image_list[i:i+batch_size] else: tmp_image_list = image_list[i:] tmp_image_list = model.decode_first_stage(tmp_image_list) tmp_image_list = torch.clamp((tmp_image_list + 1.0) / 2.0, min=0.0, max=1.0) tmp_image_list = 255.0 * rearrange(tmp_image_list, "b c h w -> b h w c") tmp_image_list = tmp_image_list.type(torch.uint8).cpu().numpy() # image list to image for image in tmp_image_list: image_video.append(image) # for i,image in enumerate(mask_list): # Image.fromarray(image).save(f"test/mask_{i}.png") image_video_path = "image.mp4" fps = 30 with imageio.get_writer(image_video_path, fps=fps) as video: for image in image_video: video.append_data(image) # 对edited_mask做膨胀 edited_mask_copy = edited_mask.copy() kernel = np.ones((3, 3), np.uint8) edited_mask = cv2.dilate(np.array(edited_mask), kernel, iterations=3) edited_mask = Image.fromarray(edited_mask) m_img = edited_mask.filter(ImageFilter.GaussianBlur(radius=3)) m_img = np.asarray(m_img).astype('float') / 255.0 img_np = np.asarray(input_image_copy).astype('float') / 255.0 ours_np = np.asarray(edited_image).astype('float') / 255.0 mix_image_np = m_img * ours_np + (1 - m_img) * img_np mix_image = Image.fromarray((mix_image_np * 255).astype(np.uint8)).convert('RGB') red = np.array(mix_image).astype('float') * 1 red[:, :, 0] = 180.0 red[:, :, 2] = 0 red[:, :, 1] = 0 mix_result_with_red_mask = np.array(mix_image) mix_result_with_red_mask = Image.fromarray( (mix_result_with_red_mask.astype('float') * (1 - m_img.astype('float') / 2.0) + m_img.astype('float') / 2.0 * red).astype('uint8')) mask_video_path = "mask.mp4" fps = 30 with imageio.get_writer(mask_video_path, fps=fps) as video: for image in mask_list: video.append_data(image) return [int(seed), text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask_copy, mask_video_path, image_video_path, input_image_copy, mix_result_with_red_mask] def reset(): return [100, "Randomize Seed", 1372, "Fix CFG", 7.5, 1.5, None, None, None, None, None, None, None] def get_example(): return [ ["test/dufu.png", "sunglasses", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5], ["test/dufu.png", "black and white suit", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5], ["test/dufu.png", "blue medical mask", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5], ["test/girl.jpeg", "diamond necklace", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5], ["test/girl.jpeg", "shiny golden crown", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5], ["test/girl.jpeg", "swimming duckling", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5], ["test/girl.jpeg", "reflective sunglasses", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5], ["test/girl.jpeg", "the queen's crown", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5], ["test/girl.jpeg", "gorgeous yellow gown", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5], ["test/iron_man.jpg", "sunglasses", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5] ] with gr.Blocks(css="footer {visibility: hidden}") as demo: with gr.Row(): gr.Markdown( "
Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model
" # noqa ) with gr.Row(): with gr.Column(scale=1, min_width=100): with gr.Row(): input_image = gr.Image(label="Input Image", type="pil", interactive=True) with gr.Row(): instruction = gr.Textbox(lines=1, label="Object description", interactive=True) with gr.Row(): steps = gr.Number(value=100, precision=0, label="Steps", interactive=True) randomize_seed = gr.Radio( ["Fix Seed", "Randomize Seed"], value="Randomize Seed", type="index", label="Seed Selection", show_label=False, interactive=True, ) seed = gr.Number(value=1372, precision=0, label="Seed", interactive=True) randomize_cfg = gr.Radio( ["Fix CFG", "Randomize CFG"], value="Fix CFG", type="index", label="CFG Selection", show_label=False, interactive=True, ) text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True) image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True) with gr.Row(): generate_button = gr.Button("Generate") reset_button = gr.Button("Reset") with gr.Column(scale=1, min_width=100): with gr.Column(): mix_image = gr.Image(label=f"Mix Image", type="pil", interactive=False) with gr.Column(): edited_mask = gr.Image(label=f"Output Mask", type="pil", interactive=False) with gr.Accordion('More outputs', open=False): with gr.Row(): image_video = gr.Video(label="Real-time Image Output") mask_video = gr.Video(label="Real-time Mask Output") with gr.Row(): original_image = gr.Image(label=f"Original Image", type="pil", interactive=False) edited_image = gr.Image(label=f"Output Image", type="pil", interactive=False) mix_result_with_red_mask = gr.Image(label=f"Mix Image With Red Mask", type="pil", interactive=False) with gr.Row(): gr.Examples( examples=get_example(), fn=generate, inputs=[input_image, instruction, steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale], outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask, mask_video, image_video, original_image, mix_result_with_red_mask], cache_examples=True, ) generate_button.click( fn=generate, inputs=[ input_image, instruction, steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, ], outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask, mask_video, image_video, original_image, mix_result_with_red_mask], ) reset_button.click( fn=reset, inputs=[], outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask, mask_video, image_video, original_image, mix_result_with_red_mask], ) # demo.queue(concurrency_count=1) # demo.launch(share=True) demo.queue().launch()