From 73aaaca85888d139f9906eda6b519e4acd0b4013 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 21 Jan 2025 12:41:32 -0500 Subject: [PATCH] add photomaker v1 and v2 Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 8 +- modules/face/__init__.py | 9 +- modules/face/photomaker.py | 40 +- modules/face/photomaker_model.py | 555 ----------------- modules/face/photomaker_model_v1.py | 107 ++++ modules/face/photomaker_model_v2.py | 337 +++++++++++ modules/face/photomaker_pipeline.py | 885 ++++++++++++++++++++++++++++ modules/sd_checkpoint.py | 1 - 8 files changed, 1373 insertions(+), 569 deletions(-) delete mode 100644 modules/face/photomaker_model.py create mode 100644 modules/face/photomaker_model_v1.py create mode 100644 modules/face/photomaker_model_v2.py create mode 100644 modules/face/photomaker_pipeline.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 3c7e63102..ac2f26a50 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # Change Log for SD.Next -## Update for 2025-01-19 +## Update for 2025-01-21 - **Model Merge** - replace model components and merge LoRAs @@ -13,6 +13,12 @@ - **Detailer**: - in addition as standard behavior of detect & run-generate, it can now also run face-restore models - included models are: *CodeFormer, RestoreFormer, GFPGan, GPEN-BFR* +- **Face**: + - new [photomaker v2](https://huggingface.co/TencentARC/PhotoMaker-V2) and reimplemented [photomaker v1](https://huggingface.co/TencentARC/PhotoMaker) + compatible with sdxl models, generates pretty good results and its faster than most other methods + select under *scripts -> face -> photomaker* + - new [reswapper](https://github.com/somanchiu/ReSwapper) + todo: experimental-only and unfinished, only noting in changelog for future reference - **Other**: - **upscale**: new [asymmetric vae](Heasterian/AsymmetricAutoencoderKLUpscaler) upscaling method - **ipex**: update supported torch versions diff --git a/modules/face/__init__.py b/modules/face/__init__.py index c851b4ee4..6835bda17 100644 --- a/modules/face/__init__.py +++ b/modules/face/__init__.py @@ -88,6 +88,7 @@ class Script(scripts.Script): with gr.Row(): gr.HTML('  Tenecent ARC Lab PhotoMaker
') with gr.Row(): + pm_model = gr.Dropdown(label='PhotoMaker Model', choices=['PhotoMaker v1', 'PhotoMaker v2'], value='PhotoMaker v2') pm_trigger = gr.Text(label='Trigger word', value="person") pm_strength = gr.Slider(label='Strength', minimum=0.0, maximum=2.0, step=0.01, value=1.0) pm_start = gr.Slider(label='Start', minimum=0.0, maximum=1.0, step=0.01, value=0.5) @@ -98,9 +99,9 @@ class Script(scripts.Script): files.change(fn=self.load_images, inputs=[files], outputs=[gallery]) mode.change(fn=self.mode_change, inputs=[mode], outputs=[cfg_reswapper, cfg_faceid, cfg_faceswap, cfg_instantid, cfg_photomaker]) - return [mode, gallery, reswapper_model, reswapper_original, ip_model, ip_override, ip_cache, ip_strength, ip_structure, id_strength, id_conditioning, id_cache, pm_trigger, pm_strength, pm_start, fs_cache] + return [mode, gallery, reswapper_model, reswapper_original, ip_model, ip_override, ip_cache, ip_strength, ip_structure, id_strength, id_conditioning, id_cache, pm_model, pm_trigger, pm_strength, pm_start, fs_cache] - def run(self, p: processing.StableDiffusionProcessing, mode, input_images, reswapper_model, reswapper_original, ip_model, ip_override, ip_cache, ip_strength, ip_structure, id_strength, id_conditioning, id_cache, pm_trigger, pm_strength, pm_start, fs_cache): # pylint: disable=arguments-differ, unused-argument + def run(self, p: processing.StableDiffusionProcessing, mode, input_images, reswapper_model, reswapper_original, ip_model, ip_override, ip_cache, ip_strength, ip_structure, id_strength, id_conditioning, id_cache, pm_model, pm_trigger, pm_strength, pm_start, fs_cache): # pylint: disable=arguments-differ, unused-argument if not shared.native: return None if mode == 'None': @@ -130,8 +131,10 @@ class Script(scripts.Script): processed_images = face_id(p, app=app, source_images=input_images, model=ip_model, override=ip_override, cache=ip_cache, scale=ip_strength, structure=ip_structure) # run faceid pipeline processed = processing.Processed(p, images_list=processed_images, seed=p.seed, subseed=p.subseed, index_of_first_image=0) # manually created processed object elif mode == 'PhotoMaker': # photomaker creates pipeline and triggers original process_images + from modules.face.insightface import get_app + app = get_app('buffalo_l') from modules.face.photomaker import photo_maker - processed = photo_maker(p, input_images=input_images, trigger=pm_trigger, strength=pm_strength, start=pm_start) + processed = photo_maker(p, app=app, input_images=input_images, model=pm_model, trigger=pm_trigger, strength=pm_strength, start=pm_start) elif mode == 'InstantID': from modules.face.insightface import get_app app=get_app('antelopev2') diff --git a/modules/face/photomaker.py b/modules/face/photomaker.py index 2b950d572..4ad31660d 100644 --- a/modules/face/photomaker.py +++ b/modules/face/photomaker.py @@ -1,10 +1,12 @@ -import os +import cv2 +import numpy as np +import torch import huggingface_hub as hf from modules import shared, processing, sd_models, devices -def photo_maker(p: processing.StableDiffusionProcessing, input_images, trigger, strength, start): # pylint: disable=arguments-differ - from modules.face.photomaker_model import PhotoMakerStableDiffusionXLPipeline +def photo_maker(p: processing.StableDiffusionProcessing, app, model: str, input_images, trigger, strength, start): # pylint: disable=arguments-differ + from modules.face.photomaker_pipeline import PhotoMakerStableDiffusionXLPipeline # prepare pipeline if len(input_images) == 0: @@ -54,22 +56,42 @@ def photo_maker(p: processing.StableDiffusionProcessing, input_images, trigger, p.task_args['start_merge_step'] = int(start * p.steps) p.task_args['prompt'] = p.all_prompts[0] if p.all_prompts is not None else p.prompt - photomaker_path = hf.hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model", cache_dir=shared.opts.hfcache_dir) - shared.log.debug(f'PhotoMaker: model={photomaker_path} images={len(input_images)} trigger={trigger} args={p.task_args}') + is_v2 = 'v2' in model + if is_v2: + repo_id, fn = 'TencentARC/PhotoMaker-V2', 'photomaker-v2.bin' + else: + repo_id, fn = 'TencentARC/PhotoMaker', 'photomaker-v1.bin' + + photomaker_path = hf.hf_hub_download(repo_id=repo_id, filename=fn, repo_type="model", cache_dir=shared.opts.hfcache_dir) + shared.log.debug(f'PhotoMaker: model="{model}" uri="{repo_id}/{fn}" images={len(input_images)} trigger={trigger} args={p.task_args}') # load photomaker adapter shared.sd_model.load_photomaker_adapter( - os.path.dirname(photomaker_path), - subfolder="", - weight_name=os.path.basename(photomaker_path), - trigger_word=trigger + photomaker_path, + trigger_word=trigger, + weight_name='photomaker-v2.bin' if is_v2 else 'photomaker-v1.bin', + pm_version='v2' if is_v2 else 'v1', + cache_dir=shared.opts.hfcache_dir, ) shared.sd_model.set_adapters(["photomaker"], adapter_weights=[strength]) + # analyze faces + if is_v2: + id_embed_list = [] + for i, source_image in enumerate(input_images): + faces = app.get(cv2.cvtColor(np.array(source_image), cv2.COLOR_RGB2BGR)) + face = sorted(faces, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face + id_embed_list.append(torch.from_numpy(face['embedding'])) + shared.log.debug(f'PhotoMaker: face={i+1} score={face.det_score:.2f} gender={"female" if face.gender==0 else "male"} age={face.age} bbox={face.bbox}') + p.task_args['id_embeds'] = torch.stack(id_embed_list) + # run processing processed: processing.Processed = processing.process_images(p) p.extra_generation_params['PhotoMaker'] = f'{strength}' + # unload photomaker adapter + shared.sd_model.unload_lora_weights() + # restore original pipeline shared.opts.data['prompt_attention'] = orig_prompt_attention shared.sd_model = orig_pipeline diff --git a/modules/face/photomaker_model.py b/modules/face/photomaker_model.py deleted file mode 100644 index 3595c6a36..000000000 --- a/modules/face/photomaker_model.py +++ /dev/null @@ -1,555 +0,0 @@ -from typing import Any, Callable, Dict, List, Optional, Union, Tuple -import PIL -import torch -import torch.nn as nn -from safetensors import safe_open -from huggingface_hub.utils import validate_hf_hub_args -from diffusers import StableDiffusionXLPipeline -from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput -from diffusers.utils import _get_model_file -from transformers import CLIPImageProcessor -from transformers.models.clip.modeling_clip import CLIPVisionModelWithProjection -from transformers.models.clip.configuration_clip import CLIPVisionConfig - - -PipelineImageInput = Union[ - PIL.Image.Image, - torch.FloatTensor, - List[PIL.Image.Image], - List[torch.FloatTensor], -] - - -VISION_CONFIG_DICT = { - "hidden_size": 1024, - "intermediate_size": 4096, - "num_attention_heads": 16, - "num_hidden_layers": 24, - "patch_size": 14, - "projection_dim": 768 -} - - -# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg -def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): - """ - Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and - Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 - """ - std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) - std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) - # rescale the results from guidance (fixes overexposure) - noise_pred_rescaled = noise_cfg * (std_text / std_cfg) - # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images - noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg - return noise_cfg - - -class MLP(nn.Module): - def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True): - super().__init__() - if use_residual: - assert in_dim == out_dim - self.layernorm = nn.LayerNorm(in_dim) - self.fc1 = nn.Linear(in_dim, hidden_dim) - self.fc2 = nn.Linear(hidden_dim, out_dim) - self.use_residual = use_residual - self.act_fn = nn.GELU() - - def forward(self, x): - residual = x - x = self.layernorm(x) - x = self.fc1(x) - x = self.act_fn(x) - x = self.fc2(x) - if self.use_residual: - x = x + residual - return x - - -class FuseModule(nn.Module): - def __init__(self, embed_dim): - super().__init__() - self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False) - self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True) - self.layer_norm = nn.LayerNorm(embed_dim) - - def fuse_fn(self, prompt_embeds, id_embeds): - stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1) - stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds - stacked_id_embeds = self.mlp2(stacked_id_embeds) - stacked_id_embeds = self.layer_norm(stacked_id_embeds) - return stacked_id_embeds - - def forward( - self, - prompt_embeds, - id_embeds, - class_tokens_mask, - ) -> torch.Tensor: - # id_embeds shape: [b, max_num_inputs, 1, 2048] - id_embeds = id_embeds.to(prompt_embeds.dtype) - num_inputs = class_tokens_mask.sum().unsqueeze(0) - batch_size, max_num_inputs = id_embeds.shape[:2] - # seq_length: 77 - seq_length = prompt_embeds.shape[1] - # flat_id_embeds shape: [b*max_num_inputs, 1, 2048] - flat_id_embeds = id_embeds.view( - -1, id_embeds.shape[-2], id_embeds.shape[-1] - ) - # valid_id_mask [b*max_num_inputs] - valid_id_mask = ( - torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :] - < num_inputs[:, None] - ) - valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()] - - prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1]) - class_tokens_mask = class_tokens_mask.view(-1) - valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1]) - # slice out the image token embeddings - image_token_embeds = prompt_embeds[class_tokens_mask] - stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds) - assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}" - prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype)) - updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1) - return updated_prompt_embeds - -class PhotoMakerIDEncoder(CLIPVisionModelWithProjection): - def __init__(self): - super().__init__(CLIPVisionConfig(**VISION_CONFIG_DICT)) - self.visual_projection_2 = nn.Linear(1024, 1280, bias=False) - self.fuse_module = FuseModule(2048) - - def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask): - b, num_inputs, c, h, w = id_pixel_values.shape - id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w) - - shared_id_embeds = self.vision_model(id_pixel_values)[1] - id_embeds = self.visual_projection(shared_id_embeds) - id_embeds_2 = self.visual_projection_2(shared_id_embeds) - - id_embeds = id_embeds.view(b, num_inputs, 1, -1) - id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1) - - id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1) - updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask) - - return updated_prompt_embeds - - -class PhotoMakerStableDiffusionXLPipeline(StableDiffusionXLPipeline): - @validate_hf_hub_args - 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, - ): - # 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 - 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 - 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) - 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) - - return prompt_embeds, pooled_prompt_embeds, class_tokens_mask - - - @torch.no_grad() - 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, - start_merge_step: int = 0, - class_tokens_mask: Optional[torch.LongTensor] = None, - prompt_embeds_text_only: Optional[torch.FloatTensor] = None, - pooled_prompt_embeds_text_only: Optional[torch.FloatTensor] = None, - ): - # 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, - 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) - - # 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. - 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 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) diff --git a/modules/face/photomaker_model_v1.py b/modules/face/photomaker_model_v1.py new file mode 100644 index 000000000..ea7f87ca2 --- /dev/null +++ b/modules/face/photomaker_model_v1.py @@ -0,0 +1,107 @@ +### original + +import torch +import torch.nn as nn +from transformers.models.clip.modeling_clip import CLIPVisionModelWithProjection +from transformers.models.clip.configuration_clip import CLIPVisionConfig + +VISION_CONFIG_DICT = { + "hidden_size": 1024, + "intermediate_size": 4096, + "num_attention_heads": 16, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768 +} + +class MLP(nn.Module): + def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True): + super().__init__() + if use_residual: + assert in_dim == out_dim + self.layernorm = nn.LayerNorm(in_dim) + self.fc1 = nn.Linear(in_dim, hidden_dim) + self.fc2 = nn.Linear(hidden_dim, out_dim) + self.use_residual = use_residual + self.act_fn = nn.GELU() + + def forward(self, x): + residual = x + x = self.layernorm(x) + x = self.fc1(x) + x = self.act_fn(x) + x = self.fc2(x) + if self.use_residual: + x = x + residual + return x + + +class FuseModule(nn.Module): + def __init__(self, embed_dim): + super().__init__() + self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False) + self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True) + self.layer_norm = nn.LayerNorm(embed_dim) + + def fuse_fn(self, prompt_embeds, id_embeds): + unstacked_prompt_embeds = prompt_embeds.unbind(0) + stacked_id_embeds = torch.cat([unstacked_prompt_embeds[0].unsqueeze(0), id_embeds], dim=-1) # monkey patch + stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds + stacked_id_embeds = self.mlp2(stacked_id_embeds) + stacked_id_embeds = self.layer_norm(stacked_id_embeds) + return stacked_id_embeds + + def forward( + self, + prompt_embeds, + id_embeds, + class_tokens_mask, + ) -> torch.Tensor: + # id_embeds shape: [b, max_num_inputs, 1, 2048] + id_embeds = id_embeds.to(prompt_embeds.dtype) + num_inputs = class_tokens_mask.sum().unsqueeze(0) + batch_size, max_num_inputs = id_embeds.shape[:2] + # seq_length: 77 + seq_length = prompt_embeds.shape[1] + # flat_id_embeds shape: [b*max_num_inputs, 1, 2048] + flat_id_embeds = id_embeds.view( + -1, id_embeds.shape[-2], id_embeds.shape[-1] + ) + # valid_id_mask [b*max_num_inputs] + valid_id_mask = ( + torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :] + < num_inputs[:, None] + ) + valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()] + prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1]) + class_tokens_mask = class_tokens_mask.view(-1) + valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1]) + # slice out the image token embeddings + image_token_embeds = prompt_embeds[class_tokens_mask] + stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds) + assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}" + prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype)) + updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1) + return updated_prompt_embeds + +class PhotoMakerIDEncoder(CLIPVisionModelWithProjection): + def __init__(self): + super().__init__(CLIPVisionConfig(**VISION_CONFIG_DICT)) + self.visual_projection_2 = nn.Linear(1024, 1280, bias=False) + self.fuse_module = FuseModule(2048) + + def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask): # pylint: disable=arguments-differ + b, num_inputs, c, h, w = id_pixel_values.shape + id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w) + + shared_id_embeds = self.vision_model(id_pixel_values)[1] + id_embeds = self.visual_projection(shared_id_embeds) + id_embeds_2 = self.visual_projection_2(shared_id_embeds) + + id_embeds = id_embeds.view(b, num_inputs, 1, -1) + id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1) + + id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1) + updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask) + + return updated_prompt_embeds diff --git a/modules/face/photomaker_model_v2.py b/modules/face/photomaker_model_v2.py new file mode 100644 index 000000000..34704376f --- /dev/null +++ b/modules/face/photomaker_model_v2.py @@ -0,0 +1,337 @@ +### original + +import math +import torch +import torch.nn as nn +from transformers.models.clip.modeling_clip import CLIPVisionModelWithProjection +from transformers.models.clip.configuration_clip import CLIPVisionConfig +from einops import rearrange +from einops.layers.torch import Rearrange + + +class FacePerceiverResampler(torch.nn.Module): + def __init__( + self, + *, + dim=768, + depth=4, + dim_head=64, + heads=16, + embedding_dim=1280, + output_dim=768, + ff_mult=4, + ): + super().__init__() + self.proj_in = torch.nn.Linear(embedding_dim, dim) + self.proj_out = torch.nn.Linear(dim, output_dim) + self.norm_out = torch.nn.LayerNorm(output_dim) + self.layers = torch.nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + torch.nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + def forward(self, latents, x): + x = self.proj_in(x) + for attn, ff in self.layers: + latents = attn(x, latents) + latents + latents = ff(latents) + latents + latents = self.proj_out(latents) + return self.norm_out(latents) + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, _width = x.shape + # (bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8): + super().__init__() + self.scale = dim_head**-0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, l, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, l, -1) + + return self.to_out(out) + + +class Resampler(nn.Module): + def __init__( + self, + dim=1024, + depth=8, + dim_head=64, + heads=16, + num_queries=8, + embedding_dim=768, + output_dim=1024, + ff_mult=4, + max_seq_len: int = 257, # CLIP tokens + CLS token + apply_pos_emb: bool = False, + num_latents_mean_pooled: int = 0, # number of latents derived from mean pooled representation of the sequence + ): + super().__init__() + self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None + + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) + + self.proj_in = nn.Linear(embedding_dim, dim) + + self.proj_out = nn.Linear(dim, output_dim) + self.norm_out = nn.LayerNorm(output_dim) + + self.to_latents_from_mean_pooled_seq = ( + nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, dim * num_latents_mean_pooled), + Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled), + ) + if num_latents_mean_pooled > 0 + else None + ) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + def forward(self, x): + if self.pos_emb is not None: + n, device = x.shape[1], x.device + pos_emb = self.pos_emb(torch.arange(n, device=device)) + x = x + pos_emb + + latents = self.latents.repeat(x.size(0), 1, 1) + + x = self.proj_in(x) + + if self.to_latents_from_mean_pooled_seq: + meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool)) + meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq) + latents = torch.cat((meanpooled_latents, latents), dim=-2) + + for attn, ff in self.layers: + latents = attn(x, latents) + latents + latents = ff(latents) + latents + + latents = self.proj_out(latents) + return self.norm_out(latents) + + +def masked_mean(t, *, dim, mask=None): + if mask is None: + return t.mean(dim=dim) + + denom = mask.sum(dim=dim, keepdim=True) + mask = rearrange(mask, "b n -> b n 1") + masked_t = t.masked_fill(~mask, 0.0) + + return masked_t.sum(dim=dim) / denom.clamp(min=1e-5) + + +VISION_CONFIG_DICT = { + "hidden_size": 1024, + "intermediate_size": 4096, + "num_attention_heads": 16, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768 +} + + +class MLP(nn.Module): + def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True): + super().__init__() + if use_residual: + assert in_dim == out_dim + self.layernorm = nn.LayerNorm(in_dim) + self.fc1 = nn.Linear(in_dim, hidden_dim) + self.fc2 = nn.Linear(hidden_dim, out_dim) + self.use_residual = use_residual + self.act_fn = nn.GELU() + + def forward(self, x): + residual = x + x = self.layernorm(x) + x = self.fc1(x) + x = self.act_fn(x) + x = self.fc2(x) + if self.use_residual: + x = x + residual + return x + + +class QFormerPerceiver(nn.Module): + def __init__(self, id_embeddings_dim, cross_attention_dim, num_tokens, embedding_dim=1024, use_residual=True, ratio=4): + super().__init__() + + self.num_tokens = num_tokens + self.cross_attention_dim = cross_attention_dim + self.use_residual = use_residual + self.token_proj = nn.Sequential( + nn.Linear(id_embeddings_dim, id_embeddings_dim*ratio), + nn.GELU(), + nn.Linear(id_embeddings_dim*ratio, cross_attention_dim*num_tokens), + ) + self.token_norm = nn.LayerNorm(cross_attention_dim) + self.perceiver_resampler = FacePerceiverResampler( + dim=cross_attention_dim, + depth=4, + dim_head=128, + heads=cross_attention_dim // 128, + embedding_dim=embedding_dim, + output_dim=cross_attention_dim, + ff_mult=4, + ) + + def forward(self, x, last_hidden_state): + x = self.token_proj(x) + x = x.reshape(-1, self.num_tokens, self.cross_attention_dim) + x = self.token_norm(x) # cls token + out = self.perceiver_resampler(x, last_hidden_state) # retrieve from patch tokens + if self.use_residual: + out = x + 1.0 * out + return out + + +class FuseModule(nn.Module): + def __init__(self, embed_dim): + super().__init__() + self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False) + self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True) + self.layer_norm = nn.LayerNorm(embed_dim) + + def fuse_fn(self, prompt_embeds, id_embeds): + stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1) + stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds + stacked_id_embeds = self.mlp2(stacked_id_embeds) + stacked_id_embeds = self.layer_norm(stacked_id_embeds) + return stacked_id_embeds + + def forward( + self, + prompt_embeds, + id_embeds, + class_tokens_mask, + ) -> torch.Tensor: + # id_embeds shape: [b, max_num_inputs, 1, 2048] + id_embeds = id_embeds.to(prompt_embeds.dtype) + num_inputs = class_tokens_mask.sum().unsqueeze(0) + batch_size, max_num_inputs = id_embeds.shape[:2] + # seq_length: 77 + seq_length = prompt_embeds.shape[1] + # flat_id_embeds shape: [b*max_num_inputs, 1, 2048] + flat_id_embeds = id_embeds.view( + -1, id_embeds.shape[-2], id_embeds.shape[-1] + ) + # valid_id_mask [b*max_num_inputs] + valid_id_mask = ( + torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :] + < num_inputs[:, None] + ) + valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()] + + prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1]) + class_tokens_mask = class_tokens_mask.view(-1) + valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1]) + # slice out the image token embeddings + image_token_embeds = prompt_embeds[class_tokens_mask] + stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds) + assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}" + prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype)) + updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1) + return updated_prompt_embeds + + +class PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken(CLIPVisionModelWithProjection): + def __init__(self, id_embeddings_dim=512): + super().__init__(CLIPVisionConfig(**VISION_CONFIG_DICT)) + self.fuse_module = FuseModule(2048) + self.visual_projection_2 = nn.Linear(1024, 1280, bias=False) + + cross_attention_dim = 2048 + # projection + self.num_tokens = 2 + self.cross_attention_dim = cross_attention_dim + self.qformer_perceiver = QFormerPerceiver( + id_embeddings_dim, + cross_attention_dim, + self.num_tokens, + ) + + def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask, id_embeds): # pylint: disable=arguments-differ + b, num_inputs, c, h, w = id_pixel_values.shape + id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w) + + last_hidden_state = self.vision_model(id_pixel_values)[0] + id_embeds = id_embeds.view(b * num_inputs, -1) + + id_embeds = self.qformer_perceiver(id_embeds, last_hidden_state) + id_embeds = id_embeds.view(b, num_inputs, self.num_tokens, -1) + updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask) + + return updated_prompt_embeds diff --git a/modules/face/photomaker_pipeline.py b/modules/face/photomaker_pipeline.py new file mode 100644 index 000000000..ec5728961 --- /dev/null +++ b/modules/face/photomaker_pipeline.py @@ -0,0 +1,885 @@ +### original + +import inspect +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +import PIL +import torch +from transformers import CLIPImageProcessor +from safetensors import safe_open +from huggingface_hub.utils import validate_hf_hub_args +from diffusers import StableDiffusionXLPipeline +from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput +from diffusers.loaders import StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin +from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback +from diffusers.models.lora import adjust_lora_scale_text_encoder +from diffusers.utils import _get_model_file, USE_PEFT_BACKEND, deprecate, is_torch_xla_available, scale_lora_layers, unscale_lora_layers + +if is_torch_xla_available(): + import torch_xla.core.xla_model as xm + XLA_AVAILABLE = True +else: + XLA_AVAILABLE = False + +from modules.face.photomaker_model_v1 import PhotoMakerIDEncoder +from modules.face.photomaker_model_v2 import PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken + +PipelineImageInput = Union[ + PIL.Image.Image, + torch.FloatTensor, + List[PIL.Image.Image], + List[torch.FloatTensor], +] + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg +def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): + """ + Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 + """ + std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) + std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) + # rescale the results from guidance (fixes overexposure) + noise_pred_rescaled = noise_cfg * (std_text / std_cfg) + # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images + noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg + return noise_cfg + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +): + """ + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") + if timesteps is not None: + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +class PhotoMakerStableDiffusionXLPipeline(StableDiffusionXLPipeline): + @validate_hf_hub_args + 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', + pm_version: str = 'v2', + **kwargs, + ): + """ + 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 weight name NOT the path to the weight. + + 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 trigger word is used to identify the position of class word in the text prompt, + and it is recommended not to set it as a common word. + This trigger word must be placed after the class word when used, otherwise, it will affect the performance of the personalized generation. + """ + + # Load the main state dict first. + cache_dir = kwargs.pop("cache_dir", None) + force_download = kwargs.pop("force_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, + 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.num_tokens =2 # pylint: disable=attribute-defined-outside-init + self.pm_version = pm_version # pylint: disable=attribute-defined-outside-init + self.trigger_word = trigger_word # pylint: disable=attribute-defined-outside-init + # load finetuned CLIP image encoder and fuse module here if it has not been registered to the pipeline yet + self.id_image_processor = CLIPImageProcessor() # pylint: disable=attribute-defined-outside-init + if pm_version == "v1": # PhotoMaker v1 + id_encoder = PhotoMakerIDEncoder() + elif pm_version == "v2": # PhotoMaker v2 + id_encoder = PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken() + else: + raise NotImplementedError(f"The PhotoMaker version [{pm_version}] does not support") + + 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 # pylint: disable=attribute-defined-outside-init + + # load lora into models + 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, + device: Optional[torch.device] = None, + num_images_per_prompt: int = 1, + do_classifier_free_guidance: bool = True, + negative_prompt: Optional[str] = None, + negative_prompt_2: Optional[str] = None, + prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + pooled_prompt_embeds: Optional[torch.Tensor] = None, + negative_pooled_prompt_embeds: Optional[torch.Tensor] = None, + lora_scale: Optional[float] = None, + clip_skip: Optional[int] = None, + ### Added args + num_id_images: int = 1, + class_tokens_mask: Optional[torch.LongTensor] = None, + ): + device = device or self._execution_device + + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin): + self._lora_scale = lora_scale # pylint: disable=attribute-defined-outside-init + + # dynamically adjust the LoRA scale + if self.text_encoder is not None: + if not USE_PEFT_BACKEND: + adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) + else: + scale_lora_layers(self.text_encoder, lora_scale) + + if self.text_encoder_2 is not None: + if not USE_PEFT_BACKEND: + adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale) + else: + scale_lora_layers(self.text_encoder_2, lora_scale) + + prompt = [prompt] if isinstance(prompt, str) else prompt + + if prompt is not None: + 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_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 + + # textual inversion: process multi-vector tokens if necessary + prompt_embeds_list = [] + prompts = [prompt, prompt_2] + for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, tokenizer) + + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + + text_input_ids = text_inputs.input_ids + untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + _removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) + + clean_index = 0 + clean_input_ids = [] + class_token_index = [] + # Find out the corresponding class word token based on the newly added trigger word token + for _i, token_id in enumerate(text_input_ids.tolist()[0]): + 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 * self.num_tokens + \ + 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 * self.num_tokens) 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] + if clip_skip is None: + prompt_embeds = prompt_embeds.hidden_states[-2] + else: + # "2" because SDXL always indexes from the penultimate layer. + prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 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) + # get unconditional embeddings for classifier free guidance + zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt # pylint: disable=no-member + if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: + negative_prompt_embeds = torch.zeros_like(prompt_embeds) + negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) + elif do_classifier_free_guidance and negative_prompt_embeds is None: + negative_prompt = negative_prompt or "" + negative_prompt_2 = negative_prompt_2 or negative_prompt + + # normalize str to list + negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt + negative_prompt_2 = ( + batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2 + ) + + uncond_tokens: List[str] + if prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = [negative_prompt, negative_prompt_2] + + negative_prompt_embeds_list = [] + for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) + + max_length = prompt_embeds.shape[1] + uncond_input = tokenizer( + negative_prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + negative_prompt_embeds = text_encoder( + uncond_input.input_ids.to(device), + output_hidden_states=True, + ) + # We are only ALWAYS interested in the pooled output of the final text encoder + negative_pooled_prompt_embeds = negative_prompt_embeds[0] + negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2] + + negative_prompt_embeds_list.append(negative_prompt_embeds) + + negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) + + if self.text_encoder_2 is not None: + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) + else: + prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + if self.text_encoder_2 is not None: + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) + else: + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device) + + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * 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 + ) + if do_classifier_free_guidance: + negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( + bs_embed * num_images_per_prompt, -1 + ) + + if self.text_encoder is not None: + if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: + # Retrieve the original scale by scaling back the LoRA layers + unscale_lora_layers(self.text_encoder, lora_scale) + + if self.text_encoder_2 is not None: + if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: + # Retrieve the original scale by scaling back the LoRA layers + unscale_lora_layers(self.text_encoder_2, lora_scale) + + return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, class_tokens_mask + + @torch.no_grad() + 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, + timesteps: List[int] = None, + sigmas: List[float] = None, + 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.Tensor] = None, + prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + pooled_prompt_embeds: Optional[torch.Tensor] = None, + negative_pooled_prompt_embeds: Optional[torch.Tensor] = None, + ip_adapter_image: Optional[PipelineImageInput] = None, + ip_adapter_image_embeds: Optional[List[torch.Tensor]] = 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, + negative_original_size: Optional[Tuple[int, int]] = None, + negative_crops_coords_top_left: Tuple[int, int] = (0, 0), + negative_target_size: Optional[Tuple[int, int]] = None, + clip_skip: Optional[int] = None, + callback_on_step_end: Optional[ + Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] + ] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + # Added parameters (for PhotoMaker) + input_id_images: PipelineImageInput = None, + start_merge_step: int = 10, + class_tokens_mask: Optional[torch.LongTensor] = None, + id_embeds: Optional[torch.FloatTensor] = None, + prompt_embeds_text_only: Optional[torch.FloatTensor] = None, + pooled_prompt_embeds_text_only: Optional[torch.FloatTensor] = None, + **kwargs, + ): + r""" + Function invoked when calling the pipeline for generation. + Only the parameters introduced by PhotoMaker are discussed here. + For explanations of the previous parameters in StableDiffusionXLPipeline, please refer to https://github.com/huggingface/diffusers/blob/v0.25.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py + + Args: + input_id_images (`PipelineImageInput`, *optional*): + Input ID Image to work with PhotoMaker. + class_tokens_mask (`torch.LongTensor`, *optional*): + Pre-generated class token. When the `prompt_embeds` parameter is provided in advance, it is necessary to prepare the `class_tokens_mask` beforehand for marking out the position of class word. + prompt_embeds_text_only (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + pooled_prompt_embeds_text_only (`torch.FloatTensor`, *optional*): + Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. + If not provided, pooled text embeddings will be generated from `prompt` input argument. + + Returns: + [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a + `tuple`. When returning a tuple, the first element is a list with the generated images. + """ + + callback = kwargs.pop("callback", None) + callback_steps = kwargs.pop("callback_steps", None) + + if callback is not None: + deprecate( + "callback", + "1.0.0", + "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", + ) + if callback_steps is not None: + deprecate( + "callback_steps", + "1.0.0", + "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", + ) + + if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + + # 0. Default height and width to unet + height = height or self.default_sample_size * self.vae_scale_factor + width = width or self.default_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, + ip_adapter_image, + ip_adapter_image_embeds, + callback_on_step_end_tensor_inputs, + ) + + self._guidance_scale = guidance_scale # pylint: disable=attribute-defined-outside-init + self._guidance_rescale = guidance_rescale # pylint: disable=attribute-defined-outside-init + self._clip_skip = clip_skip # pylint: disable=attribute-defined-outside-init + self._cross_attention_kwargs = cross_attention_kwargs # pylint: disable=attribute-defined-outside-init + self._denoising_end = denoising_end # pylint: disable=attribute-defined-outside-init + self._interrupt = False # pylint: disable=attribute-defined-outside-init + + 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 + + # 3. Encode input prompt + lora_scale = ( + self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None + ) + + 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, + class_tokens_mask=class_tokens_mask, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=self.do_classifier_free_guidance, + negative_prompt=negative_prompt, + negative_prompt_2=negative_prompt_2, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, + lora_scale=lora_scale, + clip_skip=self.clip_skip, + ) + + # 4. Encode input prompt without the trigger word for delayed conditioning + # encode, remove trigger word token, then decode + tokens_text_only = self.tokenizer.encode(prompt, add_special_tokens=False) + trigger_word_token = self.tokenizer.convert_tokens_to_ids(self.trigger_word) + tokens_text_only.remove(trigger_word_token) + prompt_text_only = self.tokenizer.decode(tokens_text_only, add_special_tokens=False) + ( + prompt_embeds_text_only, + negative_prompt_embeds, + pooled_prompt_embeds_text_only, + 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=self.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, + lora_scale=lora_scale, + clip_skip=self.clip_skip, + ) + + # 5. Prepare timesteps + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, num_inference_steps, device, timesteps, sigmas + ) + + # 6. 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 # pylint: disable=used-before-assignment + + id_pixel_values = id_pixel_values.unsqueeze(0).to(device=device, dtype=dtype) # pylint: disable=used-before-assignment + + # 7. Get the update text embedding with the stacked ID embedding + if id_embeds is not None: + id_embeds = id_embeds.unsqueeze(0).to(device=device, dtype=dtype) + prompt_embeds = self.id_encoder(id_pixel_values, prompt_embeds, class_tokens_mask, id_embeds) + else: + 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) + + # 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 + add_text_embeds = pooled_prompt_embeds + 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, + ) + if negative_original_size is not None and negative_target_size is not None: + negative_add_time_ids = self._get_add_time_ids( + negative_original_size, + negative_crops_coords_top_left, + negative_target_size, + dtype=prompt_embeds.dtype, + text_encoder_projection_dim=text_encoder_projection_dim, + ) + else: + negative_add_time_ids = add_time_ids + + if self.do_classifier_free_guidance: + add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) + + prompt_embeds = prompt_embeds.to(device) + add_text_embeds = add_text_embeds.to(device) + add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) + + if ip_adapter_image is not None or ip_adapter_image_embeds is not None: + image_embeds = self.prepare_ip_adapter_image_embeds( + ip_adapter_image, + ip_adapter_image_embeds, + device, + batch_size * num_images_per_prompt, + self.do_classifier_free_guidance, + ) + + # 11. Denoising loop + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + + # 11.1 Apply denoising_end + if ( + self.denoising_end is not None + and isinstance(self.denoising_end, float) + and self.denoising_end > 0 + and self.denoising_end < 1 + ): + discrete_timestep_cutoff = int( + round( + self.scheduler.config.num_train_timesteps + - (self.denoising_end * self.scheduler.config.num_train_timesteps) + ) + ) + num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) + timesteps = timesteps[:num_inference_steps] + + # 12. Optionally get Guidance Scale Embedding + timestep_cond = None + if self.unet.config.time_cond_proj_dim is not None: + guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) + timestep_cond = self.get_guidance_scale_embedding( + guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim + ).to(device=device, dtype=latents.dtype) + + self._num_timesteps = len(timesteps) # pylint: disable=attribute-defined-outside-init + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + if self.interrupt: + continue + + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if self.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 + ) if self.do_classifier_free_guidance else prompt_embeds_text_only + add_text_embeds = torch.cat( + [negative_pooled_prompt_embeds, pooled_prompt_embeds_text_only], dim=0 + ) if self.do_classifier_free_guidance else pooled_prompt_embeds_text_only + else: + current_prompt_embeds = torch.cat( + [negative_prompt_embeds, prompt_embeds], dim=0 + ) if self.do_classifier_free_guidance else prompt_embeds + add_text_embeds = torch.cat( + [negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0 + ) if self.do_classifier_free_guidance else pooled_prompt_embeds + + added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} + if ip_adapter_image is not None or ip_adapter_image_embeds is not None: + added_cond_kwargs["image_embeds"] = image_embeds + + # predict the noise residual + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=current_prompt_embeds, + timestep_cond=timestep_cond, + cross_attention_kwargs=self.cross_attention_kwargs, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, + )[0] + + # perform guidance + if self.do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) + + if self.do_classifier_free_guidance and self.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=self.guidance_rescale) + + # compute the previous noisy sample x_t -> x_t-1 + latents_dtype = latents.dtype + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + if latents.dtype != latents_dtype: + if torch.backends.mps.is_available(): + # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 + latents = latents.to(latents_dtype) + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) + add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds) + negative_pooled_prompt_embeds = callback_outputs.pop( + "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds + ) + add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids) + negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids) + + # 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: + step_idx = i // getattr(self.scheduler, "order", 1) + callback(step_idx, t, latents) + + if XLA_AVAILABLE: + xm.mark_step() # pylint: disable=possibly-used-before-assignment + + if output_type != "latent": + # make sure the VAE is in float32 mode, as it overflows in float16 + needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast + + if needs_upcasting: + self.upcast_vae() + latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) + elif latents.dtype != self.vae.dtype: + if torch.backends.mps.is_available(): + # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 + self.vae = self.vae.to(latents.dtype) # pylint: disable=attribute-defined-outside-init + + # unscale/denormalize the latents + # denormalize with the mean and std if available and not None + has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None + has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None + if has_latents_mean and has_latents_std: + latents_mean = ( + torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype) + ) + latents_std = ( + torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype) + ) + latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean + else: + latents = latents / self.vae.config.scaling_factor + + image = self.vae.decode(latents, return_dict=False)[0] + + # cast back to fp16 if needed + if needs_upcasting: + self.vae.to(dtype=torch.float16) + 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 all models + self.maybe_free_model_hooks() + + if not return_dict: + return (image,) + + return StableDiffusionXLPipelineOutput(images=image) diff --git a/modules/sd_checkpoint.py b/modules/sd_checkpoint.py index c20fa00f4..92c83e953 100644 --- a/modules/sd_checkpoint.py +++ b/modules/sd_checkpoint.py @@ -306,7 +306,6 @@ def extract_thumbnail(filename, data): thumbnail = thumbnail.convert("RGB") thumbnail = thumbnail.resize((512, 512), Image.Resampling.HAMMING) fn = os.path.splitext(filename)[0] - print('HERE', thumbnail, fn) thumbnail = thumbnail.save(f"{fn}.thumb.jpg", quality=50) except Exception as e: shared.log.error(f"Error extracting thumbnail: {filename} {e}")