from typing import Optional, Union, Tuple, List, Callable, Dict from tqdm import tqdm import torch from diffusers import DDIMScheduler from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import ( StableDiffusionXLPipelineOutput, ) import torch.nn.functional as nnf import numpy as np import utils.ptp_nulltext.ptp_sd.ptp_utils as ptp_utils from torch.optim.adam import Adam from PIL import Image device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu') NUM_DDIM_STEPS=50 GUIDANCE_SCALE=5.0 class NullInversion_sdxl: 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 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 image2latent(self, image): return self.model( prompt="", image=image, strength=0.0, )[0][0] @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 range(NUM_DDIM_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) ddim_latents = self.ddim_loop(latent) return 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] bar = tqdm(total=num_inner_steps * NUM_DDIM_STEPS) for i in range(NUM_DDIM_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 + 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 for j in range(j + 1, num_inner_steps): bar.update() 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: str, prompt: str, offsets=(0,0,0,0), num_inner_steps=10, early_stop_epsilon=1e-5, verbose=False): self.init_prompt(prompt) ptp_utils.register_attention_control(self.model, None) if verbose: print("DDIM inversion...") ddim_latents = self.ddim_inversion(image) if verbose: print("Null-text optimization...") uncond_embeddings = self.null_optimization(ddim_latents, num_inner_steps, early_stop_epsilon) output = StableDiffusionXLPipelineOutput(images=ddim_latents[-1]) output.uncond_embeddings = uncond_embeddings return output def __init__(self, model): # scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, # set_alpha_to_one=False) self.model = model self.tokenizer = self.model.tokenizer self.model.scheduler.set_timesteps(NUM_DDIM_STEPS) self.prompt = None self.context = None