import os import torch import collections import torch.nn as nn from functools import partial from transformers import CLIPTextModel, CLIPTokenizer, logging from diffusers import AutoencoderKL, PNDMScheduler, EulerDiscreteScheduler, DPMSolverMultistepScheduler from models.unet_2d_condition import UNet2DConditionModel # suppress partial model loading warning logging.set_verbosity_error() class RegionDiffusion(nn.Module): def __init__(self, device): super().__init__() try: with open('./TOKEN', 'r') as f: self.token = f.read().replace('\n', '') # remove the last \n! print(f'[INFO] loaded hugging face access token from ./TOKEN!') except FileNotFoundError as e: self.token = True print(f'[INFO] try to load hugging face access token from the default place, make sure you have run `huggingface-cli login`.') self.device = device self.num_train_timesteps = 1000 self.clip_gradient = False print(f'[INFO] loading stable diffusion...') local_pretrained_dir = "runwayml/stable-diffusion-v1-5" if not os.path.isdir(local_pretrained_dir): save_pretrained = True load_paths = 'runwayml/stable-diffusion-v1-5' os.makedirs(local_pretrained_dir, exist_ok=True) else: save_pretrained = False load_paths = local_pretrained_dir # 1. Load the autoencoder model which will be used to decode the latents into image space. self.vae = AutoencoderKL.from_pretrained( load_paths, subfolder="vae", use_auth_token=self.token).to(self.device) # 2. Load the tokenizer and text encoder to tokenize and encode the text. self.tokenizer = CLIPTokenizer.from_pretrained( load_paths, subfolder='tokenizer', use_auth_token=self.token) self.text_encoder = CLIPTextModel.from_pretrained( load_paths, subfolder='text_encoder', use_auth_token=self.token).to(self.device) # 3. The UNet model for generating the latents. self.unet = UNet2DConditionModel.from_pretrained( load_paths, subfolder="unet", use_auth_token=self.token).to(self.device) if save_pretrained: self.vae.save_pretrained(os.path.join(local_pretrained_dir, 'vae')) self.tokenizer.save_pretrained( os.path.join(local_pretrained_dir, 'tokenizer')) self.text_encoder.save_pretrained( os.path.join(local_pretrained_dir, 'text_encoder')) self.unet.save_pretrained( os.path.join(local_pretrained_dir, 'unet')) self.scheduler = PNDMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=self.num_train_timesteps, skip_prk_steps=True, steps_offset=1) self.alphas_cumprod = self.scheduler.alphas_cumprod.to(self.device) self.masks = [] self.attention_maps = None self.color_loss = torch.nn.functional.mse_loss print(f'[INFO] loaded stable diffusion!') def get_text_embeds(self, prompt, negative_prompt): # prompt, negative_prompt: [str] # Tokenize text and get embeddings text_input = self.tokenizer( prompt, padding='max_length', max_length=self.tokenizer.model_max_length, truncation=True, return_tensors='pt') with torch.no_grad(): text_embeddings = self.text_encoder( text_input.input_ids.to(self.device))[0] # Do the same for unconditional embeddings uncond_input = self.tokenizer(negative_prompt, padding='max_length', max_length=self.tokenizer.model_max_length, return_tensors='pt') with torch.no_grad(): uncond_embeddings = self.text_encoder( uncond_input.input_ids.to(self.device))[0] # Cat for final embeddings text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) return text_embeddings def get_text_embeds_list(self, prompts): # prompts: [list] text_embeddings = [] for prompt in prompts: # Tokenize text and get embeddings text_input = self.tokenizer( [prompt], padding='max_length', max_length=self.tokenizer.model_max_length, truncation=True, return_tensors='pt') with torch.no_grad(): text_embeddings.append(self.text_encoder( text_input.input_ids.to(self.device))[0]) return text_embeddings def produce_latents(self, text_embeddings, height=512, width=512, num_inference_steps=50, guidance_scale=7.5, latents=None, use_grad_guidance=False, text_format_dict={}): if latents is None: latents = torch.randn( (1, self.unet.in_channels, height // 8, width // 8), device=self.device) self.scheduler.set_timesteps(num_inference_steps) n_styles = text_embeddings.shape[0]-1 assert n_styles == len(self.masks) with torch.autocast('cuda'): for i, t in enumerate(self.scheduler.timesteps): # predict the noise residual with torch.no_grad(): noise_pred_uncond = self.unet(latents, t, encoder_hidden_states=text_embeddings[:1], text_format_dict={})['sample'] noise_pred_text = None for style_i, mask in enumerate(self.masks): if style_i < len(self.masks) - 1: masked_latent = latents noise_pred_text_cur = self.unet(masked_latent, t, encoder_hidden_states=text_embeddings[style_i+1:style_i+2], text_format_dict={})['sample'] else: noise_pred_text_cur = self.unet(latents, t, encoder_hidden_states=text_embeddings[style_i+1:style_i+2], text_format_dict=text_format_dict)['sample'] if noise_pred_text is None: noise_pred_text = noise_pred_text_cur * mask else: noise_pred_text = noise_pred_text + noise_pred_text_cur*mask # perform classifier-free guidance noise_pred = noise_pred_uncond + guidance_scale * \ (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents)[ 'prev_sample'] # apply gradient guidance if use_grad_guidance and t < text_format_dict['guidance_start_step']: with torch.enable_grad(): if not latents.requires_grad: latents.requires_grad = True latents_0 = self.predict_x0(latents, noise_pred, t) latents_inp = 1 / 0.18215 * latents_0 imgs = self.vae.decode(latents_inp).sample imgs = (imgs / 2 + 0.5).clamp(0, 1) loss_total = 0. for attn_map, rgb_val in zip(text_format_dict['color_obj_atten'], text_format_dict['target_RGB']): avg_rgb = ( imgs*attn_map[:, 0]).sum(2).sum(2)/attn_map[:, 0].sum() loss = self.color_loss( avg_rgb, rgb_val[:, :, 0, 0])*100 # print(loss) loss_total += loss loss_total.backward() latents = ( latents - latents.grad * text_format_dict['color_guidance_weight']).detach().clone() return latents def predict_x0(self, x_t, eps_t, t): alpha_t = self.scheduler.alphas_cumprod[t] return (x_t - eps_t * torch.sqrt(1-alpha_t)) / torch.sqrt(alpha_t) def produce_attn_maps(self, prompts, negative_prompts='', height=512, width=512, num_inference_steps=50, guidance_scale=7.5, latents=None): if isinstance(prompts, str): prompts = [prompts] if isinstance(negative_prompts, str): negative_prompts = [negative_prompts] # Prompts -> text embeds text_embeddings = self.get_text_embeds( prompts, negative_prompts) # [2, 77, 768] if latents is None: latents = torch.randn( (text_embeddings.shape[0] // 2, self.unet.in_channels, height // 8, width // 8), device=self.device) self.scheduler.set_timesteps(num_inference_steps) with torch.autocast('cuda'): for i, t in enumerate(self.scheduler.timesteps): # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes. latent_model_input = torch.cat([latents] * 2) # predict the noise residual with torch.no_grad(): noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=text_embeddings)['sample'] # perform guidance noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * \ (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents)[ 'prev_sample'] # Img latents -> imgs imgs = self.decode_latents(latents) # [1, 3, 512, 512] # Img to Numpy imgs = imgs.detach().cpu().permute(0, 2, 3, 1).numpy() imgs = (imgs * 255).round().astype('uint8') return imgs def decode_latents(self, latents): latents = 1 / 0.18215 * latents with torch.no_grad(): imgs = self.vae.decode(latents).sample imgs = (imgs / 2 + 0.5).clamp(0, 1) return imgs def prompt_to_img(self, prompts, negative_prompts='', height=512, width=512, num_inference_steps=50, guidance_scale=7.5, latents=None, text_format_dict={}, use_grad_guidance=False): if isinstance(prompts, str): prompts = [prompts] if isinstance(negative_prompts, str): negative_prompts = [negative_prompts] # Prompts -> text embeds text_embeds = self.get_text_embeds( prompts, negative_prompts) # [2, 77, 768] if len(text_format_dict) > 0: if 'font_styles' in text_format_dict and text_format_dict['font_styles'] is not None: text_format_dict['font_styles_embs'] = self.get_text_embeds_list( text_format_dict['font_styles']) # [2, 77, 768] else: text_format_dict['font_styles_embs'] = None # else: latents = self.produce_latents(text_embeds, height=height, width=width, latents=latents, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, use_grad_guidance=use_grad_guidance, text_format_dict=text_format_dict) # [1, 4, 64, 64] # Img latents -> imgs imgs = self.decode_latents(latents) # [1, 3, 512, 512] # Img to Numpy imgs = imgs.detach().cpu().permute(0, 2, 3, 1).numpy() imgs = (imgs * 255).round().astype('uint8') return imgs def reset_attention_maps(self): r"""Function to reset attention maps. We reset attention maps because we append them while getting hooks to visualize attention maps for every step. """ for key in self.attention_maps: self.attention_maps[key] = [] def register_evaluation_hooks(self): r"""Function for registering hooks during evaluation. We mainly store activation maps averaged over queries. """ self.forward_hooks = [] def save_activations(activations, name, module, inp, out): r""" PyTorch Forward hook to save outputs at each forward pass. """ # out[0] - final output of attention layer # out[1] - attention probability matrix if 'attn2' in name: assert out[1].shape[-1] == 77 activations[name].append(out[1].detach().cpu()) else: assert out[1].shape[-1] != 77 attention_dict = collections.defaultdict(list) for name, module in self.unet.named_modules(): leaf_name = name.split('.')[-1] if 'attn' in leaf_name: # Register hook to obtain outputs at every attention layer. self.forward_hooks.append(module.register_forward_hook( partial(save_activations, attention_dict, name) )) # attention_dict is a dictionary containing attention maps for every attention layer self.attention_maps = attention_dict def remove_evaluation_hooks(self): for hook in self.forward_hooks: hook.remove() self.attention_maps = None