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| from typing import Any, Callable, Dict, List, Optional, Union, Tuple | |
| from collections import OrderedDict | |
| import os | |
| import PIL | |
| import numpy as np | |
| import torch | |
| from torchvision import transforms as T | |
| from safetensors import safe_open | |
| from huggingface_hub.utils import validate_hf_hub_args | |
| from transformers import CLIPImageProcessor, CLIPTokenizer | |
| from diffusers import StableDiffusionXLPipeline | |
| from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput | |
| from diffusers.utils import ( | |
| _get_model_file, | |
| is_transformers_available, | |
| logging, | |
| ) | |
| from model import PhotoMakerIDEncoder | |
| PipelineImageInput = Union[ | |
| PIL.Image.Image, | |
| torch.FloatTensor, | |
| List[PIL.Image.Image], | |
| List[torch.FloatTensor], | |
| ] | |
| class PhotoMakerStableDiffusionXLPipeline(StableDiffusionXLPipeline): | |
| def load_photomaker_adapter( | |
| self, | |
| pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], | |
| weight_name: str, | |
| subfolder: str = '', | |
| trigger_word: str = 'img', | |
| **kwargs, | |
| ): | |
| """ | |
| #TODO | |
| Parameters: | |
| pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`): | |
| Can be either: | |
| - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on | |
| the Hub. | |
| - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved | |
| with [`ModelMixin.save_pretrained`]. | |
| - A [torch state | |
| dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict). | |
| weight_name (`str`): | |
| The subfolder location of a model file within a larger model repository on the Hub or locally. | |
| subfolder (`str`, defaults to `""`): | |
| The subfolder location of a model file within a larger model repository on the Hub or locally. | |
| trigger_word (`str`, *optional*, defaults to `"img"`): | |
| The subfolder location of a model file within a larger model repository on the Hub or locally. | |
| """ | |
| # Load the main state dict first. | |
| cache_dir = kwargs.pop("cache_dir", None) | |
| force_download = kwargs.pop("force_download", False) | |
| resume_download = kwargs.pop("resume_download", False) | |
| proxies = kwargs.pop("proxies", None) | |
| local_files_only = kwargs.pop("local_files_only", None) | |
| token = kwargs.pop("token", None) | |
| revision = kwargs.pop("revision", None) | |
| user_agent = { | |
| "file_type": "attn_procs_weights", | |
| "framework": "pytorch", | |
| } | |
| if not isinstance(pretrained_model_name_or_path_or_dict, dict): | |
| model_file = _get_model_file( | |
| pretrained_model_name_or_path_or_dict, | |
| weights_name=weight_name, | |
| cache_dir=cache_dir, | |
| force_download=force_download, | |
| resume_download=resume_download, | |
| proxies=proxies, | |
| local_files_only=local_files_only, | |
| token=token, | |
| revision=revision, | |
| subfolder=subfolder, | |
| user_agent=user_agent, | |
| ) | |
| if weight_name.endswith(".safetensors"): | |
| state_dict = {"id_encoder": {}, "lora_weights": {}} | |
| with safe_open(model_file, framework="pt", device="cpu") as f: | |
| for key in f.keys(): | |
| if key.startswith("id_encoder."): | |
| state_dict["id_encoder"][key.replace("id_encoder.", "")] = f.get_tensor(key) | |
| elif key.startswith("lora_weights."): | |
| state_dict["lora_weights"][key.replace("lora_weights.", "")] = f.get_tensor(key) | |
| else: | |
| state_dict = torch.load(model_file, map_location="cpu") | |
| else: | |
| state_dict = pretrained_model_name_or_path_or_dict | |
| keys = list(state_dict.keys()) | |
| if keys != ["id_encoder", "lora_weights"]: | |
| raise ValueError("Required keys are (`id_encoder` and `lora_weights`) missing from the state dict.") | |
| self.trigger_word = trigger_word | |
| # load finetuned CLIP image encoder and fuse module here if it has not been registered to the pipeline yet | |
| print(f"Loading PhotoMaker components [1] id_encoder from [{pretrained_model_name_or_path_or_dict}]...") | |
| id_encoder = PhotoMakerIDEncoder() | |
| id_encoder.load_state_dict(state_dict["id_encoder"], strict=True) | |
| id_encoder = id_encoder.to(self.device, dtype=self.unet.dtype) | |
| self.id_encoder = id_encoder | |
| self.id_image_processor = CLIPImageProcessor() | |
| # load lora into models | |
| print(f"Loading PhotoMaker components [2] lora_weights from [{pretrained_model_name_or_path_or_dict}]") | |
| self.load_lora_weights(state_dict["lora_weights"], adapter_name="photomaker") | |
| # Add trigger word token | |
| if self.tokenizer is not None: | |
| self.tokenizer.add_tokens([self.trigger_word], special_tokens=True) | |
| self.tokenizer_2.add_tokens([self.trigger_word], special_tokens=True) | |
| def encode_prompt_with_trigger_word( | |
| self, | |
| prompt: str, | |
| prompt_2: Optional[str] = None, | |
| num_id_images: int = 1, | |
| device: Optional[torch.device] = None, | |
| prompt_embeds: Optional[torch.FloatTensor] = None, | |
| pooled_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| class_tokens_mask: Optional[torch.LongTensor] = None, | |
| ): | |
| device = device or self._execution_device | |
| if prompt is not None and isinstance(prompt, str): | |
| batch_size = 1 | |
| elif prompt is not None and isinstance(prompt, list): | |
| batch_size = len(prompt) | |
| else: | |
| batch_size = prompt_embeds.shape[0] | |
| # Find the token id of the trigger word | |
| image_token_id = self.tokenizer_2.convert_tokens_to_ids(self.trigger_word) | |
| # Define tokenizers and text encoders | |
| tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] | |
| text_encoders = ( | |
| [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] | |
| ) | |
| if prompt_embeds is None: | |
| prompt_2 = prompt_2 or prompt | |
| prompt_embeds_list = [] | |
| prompts = [prompt, prompt_2] | |
| for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): | |
| input_ids = tokenizer.encode(prompt) # TODO: batch encode | |
| clean_index = 0 | |
| clean_input_ids = [] | |
| class_token_index = [] | |
| # Find out the corrresponding class word token based on the newly added trigger word token | |
| for i, token_id in enumerate(input_ids): | |
| if token_id == image_token_id: | |
| class_token_index.append(clean_index - 1) | |
| else: | |
| clean_input_ids.append(token_id) | |
| clean_index += 1 | |
| if len(class_token_index) != 1: | |
| raise ValueError( | |
| f"PhotoMaker currently does not support multiple trigger words in a single prompt.\ | |
| Trigger word: {self.trigger_word}, Prompt: {prompt}." | |
| ) | |
| class_token_index = class_token_index[0] | |
| # Expand the class word token and corresponding mask | |
| class_token = clean_input_ids[class_token_index] | |
| clean_input_ids = clean_input_ids[:class_token_index] + [class_token] * num_id_images + \ | |
| clean_input_ids[class_token_index+1:] | |
| # Truncation or padding | |
| max_len = tokenizer.model_max_length | |
| if len(clean_input_ids) > max_len: | |
| clean_input_ids = clean_input_ids[:max_len] | |
| else: | |
| clean_input_ids = clean_input_ids + [tokenizer.pad_token_id] * ( | |
| max_len - len(clean_input_ids) | |
| ) | |
| class_tokens_mask = [True if class_token_index <= i < class_token_index+num_id_images else False \ | |
| for i in range(len(clean_input_ids))] | |
| clean_input_ids = torch.tensor(clean_input_ids, dtype=torch.long).unsqueeze(0) | |
| class_tokens_mask = torch.tensor(class_tokens_mask, dtype=torch.bool).unsqueeze(0) | |
| prompt_embeds = text_encoder( | |
| clean_input_ids.to(device), | |
| output_hidden_states=True, | |
| ) | |
| # We are only ALWAYS interested in the pooled output of the final text encoder | |
| pooled_prompt_embeds = prompt_embeds[0] | |
| prompt_embeds = prompt_embeds.hidden_states[-2] | |
| prompt_embeds_list.append(prompt_embeds) | |
| prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) | |
| prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) | |
| class_tokens_mask = class_tokens_mask.to(device=device) # TODO: ignoring two-prompt case | |
| return prompt_embeds, pooled_prompt_embeds, class_tokens_mask | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]] = None, | |
| prompt_2: Optional[Union[str, List[str]]] = None, | |
| height: Optional[int] = None, | |
| width: Optional[int] = None, | |
| num_inference_steps: int = 50, | |
| denoising_end: Optional[float] = None, | |
| guidance_scale: float = 5.0, | |
| negative_prompt: Optional[Union[str, List[str]]] = None, | |
| negative_prompt_2: Optional[Union[str, List[str]]] = None, | |
| num_images_per_prompt: Optional[int] = 1, | |
| eta: float = 0.0, | |
| generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
| latents: Optional[torch.FloatTensor] = None, | |
| prompt_embeds: Optional[torch.FloatTensor] = None, | |
| negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| pooled_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| cross_attention_kwargs: Optional[Dict[str, Any]] = None, | |
| guidance_rescale: float = 0.0, | |
| original_size: Optional[Tuple[int, int]] = None, | |
| crops_coords_top_left: Tuple[int, int] = (0, 0), | |
| target_size: Optional[Tuple[int, int]] = None, | |
| callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, | |
| callback_steps: int = 1, | |
| # Added parameters (for PhotoMaker) | |
| input_id_images: PipelineImageInput = None, | |
| class_tokens_mask: Optional[torch.LongTensor] = None, | |
| prompt_embeds_text_only: Optional[torch.FloatTensor] = None, | |
| pooled_prompt_embeds_text_only: Optional[torch.FloatTensor] = None, | |
| start_merge_step: int = 0, | |
| ): | |
| # TODO: doc | |
| # 0. Default height and width to unet | |
| height = height or self.unet.config.sample_size * self.vae_scale_factor | |
| width = width or self.unet.config.sample_size * self.vae_scale_factor | |
| original_size = original_size or (height, width) | |
| target_size = target_size or (height, width) | |
| # 1. Check inputs. Raise error if not correct | |
| self.check_inputs( | |
| prompt, | |
| prompt_2, | |
| height, | |
| width, | |
| callback_steps, | |
| negative_prompt, | |
| negative_prompt_2, | |
| prompt_embeds, | |
| negative_prompt_embeds, | |
| pooled_prompt_embeds, | |
| negative_pooled_prompt_embeds, | |
| ) | |
| # | |
| if prompt_embeds is not None and class_tokens_mask is None: | |
| raise ValueError( | |
| "If `prompt_embeds` are provided, `class_tokens_mask` also have to be passed. Make sure to generate `class_tokens_mask` from the same tokenizer that was used to generate `prompt_embeds`." | |
| ) | |
| # check the input id images | |
| if input_id_images is None: | |
| raise ValueError( | |
| "Provide `input_id_images`. Cannot leave `input_id_images` undefined for PhotoMaker pipeline." | |
| ) | |
| if not isinstance(input_id_images, list): | |
| input_id_images = [input_id_images] | |
| # 2. Define call parameters | |
| if prompt is not None and isinstance(prompt, str): | |
| batch_size = 1 | |
| elif prompt is not None and isinstance(prompt, list): | |
| batch_size = len(prompt) | |
| else: | |
| batch_size = prompt_embeds.shape[0] | |
| device = self._execution_device | |
| # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) | |
| # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` | |
| # corresponds to doing no classifier free guidance. | |
| do_classifier_free_guidance = guidance_scale > 1.0 | |
| assert do_classifier_free_guidance | |
| # 3. Encode input prompt | |
| num_id_images = len(input_id_images) | |
| ( | |
| prompt_embeds, | |
| pooled_prompt_embeds, | |
| class_tokens_mask, | |
| ) = self.encode_prompt_with_trigger_word( | |
| prompt=prompt, | |
| prompt_2=prompt_2, | |
| device=device, | |
| num_id_images=num_id_images, | |
| prompt_embeds=prompt_embeds, | |
| pooled_prompt_embeds=pooled_prompt_embeds, | |
| class_tokens_mask=class_tokens_mask, | |
| ) | |
| # 4. Encode input prompt without the trigger word for delayed conditioning | |
| prompt_text_only = prompt.replace(" "+self.trigger_word, "") # sensitive to white space | |
| ( | |
| prompt_embeds_text_only, | |
| negative_prompt_embeds, | |
| pooled_prompt_embeds_text_only, # TODO: replace the pooled_prompt_embeds with text only prompt | |
| negative_pooled_prompt_embeds, | |
| ) = self.encode_prompt( | |
| prompt=prompt_text_only, | |
| prompt_2=prompt_2, | |
| device=device, | |
| num_images_per_prompt=num_images_per_prompt, | |
| do_classifier_free_guidance=do_classifier_free_guidance, | |
| negative_prompt=negative_prompt, | |
| negative_prompt_2=negative_prompt_2, | |
| prompt_embeds=prompt_embeds_text_only, | |
| negative_prompt_embeds=negative_prompt_embeds, | |
| pooled_prompt_embeds=pooled_prompt_embeds_text_only, | |
| negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, | |
| ) | |
| # 5. Prepare the input ID images | |
| dtype = next(self.id_encoder.parameters()).dtype | |
| if not isinstance(input_id_images[0], torch.Tensor): | |
| id_pixel_values = self.id_image_processor(input_id_images, return_tensors="pt").pixel_values | |
| id_pixel_values = id_pixel_values.unsqueeze(0).to(device=device, dtype=dtype) # TODO: multiple prompts | |
| # 6. Get the update text embedding with the stacked ID embedding | |
| prompt_embeds = self.id_encoder(id_pixel_values, prompt_embeds, class_tokens_mask) | |
| bs_embed, seq_len, _ = prompt_embeds.shape | |
| # duplicate text embeddings for each generation per prompt, using mps friendly method | |
| prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) | |
| prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) | |
| pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( | |
| bs_embed * num_images_per_prompt, -1 | |
| ) | |
| # 7. Prepare timesteps | |
| self.scheduler.set_timesteps(num_inference_steps, device=device) | |
| timesteps = self.scheduler.timesteps | |
| # 8. Prepare latent variables | |
| num_channels_latents = self.unet.config.in_channels | |
| latents = self.prepare_latents( | |
| batch_size * num_images_per_prompt, | |
| num_channels_latents, | |
| height, | |
| width, | |
| prompt_embeds.dtype, | |
| device, | |
| generator, | |
| latents, | |
| ) | |
| # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline | |
| extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | |
| # 10. Prepare added time ids & embeddings | |
| if self.text_encoder_2 is None: | |
| text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) | |
| else: | |
| text_encoder_projection_dim = self.text_encoder_2.config.projection_dim | |
| add_time_ids = self._get_add_time_ids( | |
| original_size, | |
| crops_coords_top_left, | |
| target_size, | |
| dtype=prompt_embeds.dtype, | |
| text_encoder_projection_dim=text_encoder_projection_dim, | |
| ) | |
| add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0) | |
| add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) | |
| # 11. Denoising loop | |
| num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order | |
| with self.progress_bar(total=num_inference_steps) as progress_bar: | |
| for i, t in enumerate(timesteps): | |
| latent_model_input = ( | |
| torch.cat([latents] * 2) if do_classifier_free_guidance else latents | |
| ) | |
| latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
| if i <= start_merge_step: | |
| current_prompt_embeds = torch.cat( | |
| [negative_prompt_embeds, prompt_embeds_text_only], dim=0 | |
| ) | |
| add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds_text_only], dim=0) | |
| else: | |
| current_prompt_embeds = torch.cat( | |
| [negative_prompt_embeds, prompt_embeds], dim=0 | |
| ) | |
| add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0) | |
| # predict the noise residual | |
| added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} | |
| noise_pred = self.unet( | |
| latent_model_input, | |
| t, | |
| encoder_hidden_states=current_prompt_embeds, | |
| cross_attention_kwargs=cross_attention_kwargs, | |
| added_cond_kwargs=added_cond_kwargs, | |
| return_dict=False, | |
| )[0] | |
| # perform guidance | |
| if do_classifier_free_guidance: | |
| noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) | |
| noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | |
| if do_classifier_free_guidance and guidance_rescale > 0.0: | |
| # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf | |
| noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale) | |
| # compute the previous noisy sample x_t -> x_t-1 | |
| latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] | |
| # call the callback, if provided | |
| if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): | |
| progress_bar.update() | |
| if callback is not None and i % callback_steps == 0: | |
| callback(i, t, latents) | |
| # make sure the VAE is in float32 mode, as it overflows in float16 | |
| if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: | |
| self.upcast_vae() | |
| latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) | |
| if not output_type == "latent": | |
| image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] | |
| else: | |
| image = latents | |
| return StableDiffusionXLPipelineOutput(images=image) | |
| # apply watermark if available | |
| # if self.watermark is not None: | |
| # image = self.watermark.apply_watermark(image) | |
| image = self.image_processor.postprocess(image, output_type=output_type) | |
| # Offload last model to CPU | |
| if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: | |
| self.final_offload_hook.offload() | |
| if not return_dict: | |
| return (image,) | |
| return StableDiffusionXLPipelineOutput(images=image) |