from typing import Union from torchvision.transforms import ToTensor from torchvision.utils import save_image from tqdm import tqdm import torch from torch.optim.adam import Adam import torch.nn.functional as nnf import numpy as np from PIL import Image def load_512(image_path, left=0, right=0, top=0, bottom=0): if type(image_path) is str: image = np.array(Image.open(image_path))[:, :, :3] else: image = image_path h, w, c = image.shape left = min(left, w-1) right = min(right, w - left - 1) top = min(top, h - left - 1) bottom = min(bottom, h - top - 1) image = image[top:h-bottom, left:w-right] h, w, c = image.shape if h < w: offset = (w - h) // 2 image = image[:, offset:offset + h] elif w < h: offset = (h - w) // 2 image = image[offset:offset + w] image = np.array(Image.fromarray(image).resize((512, 512))) return image def invert_image(args, ldm_stable, ldm_stable_config, prompts, exp_path): print("Start null text inversion") null_inversion = NullInversion(ldm_stable, ldm_stable_config) (image_gt, image_enc), x_t, uncond_embeddings = null_inversion.invert(args.real_image_path, prompts[0], offsets=(0,0,0,0), verbose=True) save_image(ToTensor()(image_gt), f"{exp_path}/real_image.jpg") save_image(ToTensor()(image_enc), f"{exp_path}/image_enc.jpg") print("End null text inversion") return x_t, uncond_embeddings class NullInversion: def __init__(self, model, model_config): self.model = model self.model_config = model_config self.tokenizer = self.model.tokenizer self.model.scheduler.set_timesteps(self.model_config["num_diffusion_steps"]) self.prompt = None self.context = None def prev_step(self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, sample: Union[torch.FloatTensor, np.ndarray]): prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps alpha_prod_t = self.scheduler.alphas_cumprod[timestep] alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod beta_prod_t = 1 - alpha_prod_t pred_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * model_output prev_sample = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction return prev_sample def next_step(self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, sample: Union[torch.FloatTensor, np.ndarray]): timestep, next_timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999), timestep alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep] beta_prod_t = 1 - alpha_prod_t next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction return next_sample def get_noise_pred_single(self, latents, t, context): noise_pred = self.model.unet(latents, t, encoder_hidden_states=context)["sample"] return noise_pred def get_noise_pred(self, latents, t, is_forward=True, context=None): latents_input = torch.cat([latents] * 2) if context is None: context = self.context guidance_scale = 1 if is_forward else self.model_config["guidance_scale"] noise_pred = self.model.unet(latents_input, t, encoder_hidden_states=context)["sample"] noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond) if is_forward: latents = self.next_step(noise_pred, t, latents) else: latents = self.prev_step(noise_pred, t, latents) return latents @torch.no_grad() def latent2image(self, latents, return_type='np'): latents = 1 / 0.18215 * latents.detach() image = self.model.vae.decode(latents)['sample'] if return_type == 'np': image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy()[0] image = (image * 255).astype(np.uint8) return image @torch.no_grad() def image2latent(self, image): with torch.no_grad(): if type(image) is Image: image = np.array(image) if type(image) is torch.Tensor and image.dim() == 4: latents = image else: image = torch.from_numpy(image).float() / 127.5 - 1 image = image.permute(2, 0, 1).unsqueeze(0).to(self.model.device) latents = self.model.vae.encode(image)['latent_dist'].mean latents = latents * 0.18215 return latents @torch.no_grad() def init_prompt(self, prompt: str): uncond_input = self.model.tokenizer( [""], padding="max_length", max_length=self.model.tokenizer.model_max_length, return_tensors="pt" ) uncond_embeddings = self.model.text_encoder(uncond_input.input_ids.to(self.model.device))[0] text_input = self.model.tokenizer( [prompt], padding="max_length", max_length=self.model.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_embeddings = self.model.text_encoder(text_input.input_ids.to(self.model.device))[0] self.context = torch.cat([uncond_embeddings, text_embeddings]) self.prompt = prompt @torch.no_grad() def ddim_loop(self, latent): uncond_embeddings, cond_embeddings = self.context.chunk(2) all_latent = [latent] latent = latent.clone().detach() for i in tqdm(range(self.model_config["num_diffusion_steps"])): t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1] noise_pred = self.get_noise_pred_single(latent, t, cond_embeddings) latent = self.next_step(noise_pred, t, latent) all_latent.append(latent) return all_latent @property def scheduler(self): return self.model.scheduler @torch.no_grad() def ddim_inversion(self, image): latent = self.image2latent(image) image_rec = self.latent2image(latent) ddim_latents = self.ddim_loop(latent) return image_rec, ddim_latents def null_optimization(self, latents, num_inner_steps, epsilon): uncond_embeddings, cond_embeddings = self.context.chunk(2) uncond_embeddings_list = [] latent_cur = latents[-1] with tqdm(total=num_inner_steps * (self.model_config["num_diffusion_steps"])) as bar: for i in range(self.model_config["num_diffusion_steps"]): uncond_embeddings = uncond_embeddings.clone().detach() uncond_embeddings.requires_grad = True optimizer = Adam([uncond_embeddings], lr=1e-2 * (1. - i / 100.)) latent_prev = latents[len(latents) - i - 2] t = self.model.scheduler.timesteps[i] with torch.no_grad(): noise_pred_cond = self.get_noise_pred_single(latent_cur, t, cond_embeddings) for j in range(num_inner_steps): noise_pred_uncond = self.get_noise_pred_single(latent_cur, t, uncond_embeddings) noise_pred = noise_pred_uncond + self.model_config["guidance_scale"] * (noise_pred_cond - noise_pred_uncond) latents_prev_rec = self.prev_step(noise_pred, t, latent_cur) loss = nnf.mse_loss(latents_prev_rec, latent_prev) optimizer.zero_grad() loss.backward() optimizer.step() loss_item = loss.item() bar.update() if loss_item < epsilon + i * 2e-5: break bar.update(num_inner_steps - j - 1) uncond_embeddings_list.append(uncond_embeddings[:1].detach()) with torch.no_grad(): context = torch.cat([uncond_embeddings, cond_embeddings]) latent_cur = self.get_noise_pred(latent_cur, t, False, context) # bar.close() return uncond_embeddings_list def invert(self, image_path: str, prompt: str, offsets=(0,0,0,0), num_inner_steps=10, early_stop_epsilon=1e-5, verbose=False): self.init_prompt(prompt) image_gt = load_512(image_path, *offsets) if verbose: print("DDIM inversion...") image_rec, ddim_latents = self.ddim_inversion(image_gt) if verbose: print("Null-text optimization...") uncond_embeddings = self.null_optimization(ddim_latents, num_inner_steps, early_stop_epsilon) return (image_gt, image_rec), ddim_latents[-1], uncond_embeddings