from typing import Any, Callable, Dict, List, Optional, Union, Tuple import cv2 import PIL import numpy as np from PIL import Image import torch from torchvision import transforms from insightface.app import FaceAnalysis ### insight-face installation can be found at https://github.com/deepinsight/insightface from safetensors import safe_open from huggingface_hub.utils import validate_hf_hub_args from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline from diffusers.utils import _get_model_file from functions import process_text_with_markers, masks_for_unique_values, fetch_mask_raw_image, tokenize_and_mask_noun_phrases_ends, prepare_image_token_idx from functions import ProjPlusModel, masks_for_unique_values from attention import Consistent_IPAttProcessor, Consistent_AttProcessor, FacialEncoder from huggingface_hub import hf_hub_download PipelineImageInput = Union[ PIL.Image.Image, torch.FloatTensor, List[PIL.Image.Image], List[torch.FloatTensor], ] ### Download the pretrained model from huggingface and put it locally, then place the model in a local directory and specify the directory location. class ConsistentIDStableDiffusionPipeline(StableDiffusionPipeline): @validate_hf_hub_args def load_ConsistentID_model( self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], bise_net, weight_name: str, subfolder: str = '', trigger_word_ID: str = '<|image|>', trigger_word_facial: str = '<|facial|>', image_encoder_path: str = 'laion/CLIP-ViT-H-14-laion2B-s32B-b79K', torch_dtype = torch.float16, num_tokens = 4, lora_rank= 128, **kwargs, ): self.lora_rank = lora_rank self.torch_dtype = torch_dtype self.num_tokens = num_tokens self.set_ip_adapter() self.image_encoder_path = image_encoder_path self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to( self.device, dtype=self.torch_dtype ) self.clip_image_processor = CLIPImageProcessor() self.id_image_processor = CLIPImageProcessor() self.crop_size = 512 # FaceID self.app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) self.app.prepare(ctx_id=0, det_size=(640, 640)) ### BiSeNet # self.bise_net = BiSeNet(n_classes = 19) # self.bise_net.cuda() # CUDA must not be initialized in the main process on Spaces with Stateless GPU environment # self.bise_net_cp=bise_net_cp_path # self.bise_net.load_state_dict(torch.load(self.bise_net_cp)) self.bise_net = bise_net # load from outside self.bise_net.eval() # Colors for all 20 parts self.part_colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 0, 85], [255, 0, 170], [0, 255, 0], [85, 255, 0], [170, 255, 0], [0, 255, 85], [0, 255, 170], [0, 0, 255], [85, 0, 255], [170, 0, 255], [0, 85, 255], [0, 170, 255], [255, 255, 0], [255, 255, 85], [255, 255, 170], [255, 0, 255], [255, 85, 255], [255, 170, 255], [0, 255, 255], [85, 255, 255], [170, 255, 255]] ### LLVA (Optional) self.llva_model_path = "liuhaotian/llava-v1.5-13b" # TODO # IMPORTANT! Download the openai/clip-vit-large-patch14-336 model and specify the model path in config.json ("mm_vision_tower": "openai/clip-vit-large-patch14-336"). self.llva_prompt = "Describe this person's facial features for me, including face, ears, eyes, nose, and mouth." self.llva_tokenizer, self.llva_model, self.llva_image_processor, self.llva_context_len = None,None,None,None #load_pretrained_model(self.llva_model_path) self.image_proj_model = ProjPlusModel( cross_attention_dim=self.unet.config.cross_attention_dim, id_embeddings_dim=512, clip_embeddings_dim=self.image_encoder.config.hidden_size, num_tokens=self.num_tokens, # 4 - inspirsed by IPAdapter and Midjourney ).to(self.device, dtype=self.torch_dtype) self.FacialEncoder = FacialEncoder(self.image_encoder).to(self.device, dtype=self.torch_dtype) # 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, use_auth_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: ### TODO safetensors add for key in f.keys(): if key.startswith("FacialEncoder."): state_dict["FacialEncoder"][key.replace("FacialEncoder.", "")] = f.get_tensor(key) elif key.startswith("image_proj."): state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key) else: state_dict = torch.load(model_file, map_location="cpu") else: state_dict = pretrained_model_name_or_path_or_dict self.trigger_word_ID = trigger_word_ID self.trigger_word_facial = trigger_word_facial self.FacialEncoder.load_state_dict(state_dict["FacialEncoder"], strict=True) self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=True) ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values()) ip_layers.load_state_dict(state_dict["adapter_modules"], strict=True) print(f"Successfully loaded weights from checkpoint") # Add trigger word token if self.tokenizer is not None: self.tokenizer.add_tokens([self.trigger_word_ID], special_tokens=True) self.tokenizer.add_tokens([self.trigger_word_facial], special_tokens=True) def set_ip_adapter(self): unet = self.unet attn_procs = {} for name in unet.attn_processors.keys(): cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim if name.startswith("mid_block"): hidden_size = unet.config.block_out_channels[-1] elif name.startswith("up_blocks"): block_id = int(name[len("up_blocks.")]) hidden_size = list(reversed(unet.config.block_out_channels))[block_id] elif name.startswith("down_blocks"): block_id = int(name[len("down_blocks.")]) hidden_size = unet.config.block_out_channels[block_id] if cross_attention_dim is None: attn_procs[name] = Consistent_AttProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=self.lora_rank, ).to(self.device, dtype=self.torch_dtype) else: attn_procs[name] = Consistent_IPAttProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, rank=self.lora_rank, num_tokens=self.num_tokens, ).to(self.device, dtype=self.torch_dtype) unet.set_attn_processor(attn_procs) @torch.inference_mode() def get_facial_embeds(self, prompt_embeds, negative_prompt_embeds, facial_clip_images, facial_token_masks, valid_facial_token_idx_mask): hidden_states = [] uncond_hidden_states = [] for facial_clip_image in facial_clip_images: hidden_state = self.image_encoder(facial_clip_image.to(self.device, dtype=self.torch_dtype), output_hidden_states=True).hidden_states[-2] uncond_hidden_state = self.image_encoder(torch.zeros_like(facial_clip_image, dtype=self.torch_dtype).to(self.device), output_hidden_states=True).hidden_states[-2] hidden_states.append(hidden_state) uncond_hidden_states.append(uncond_hidden_state) multi_facial_embeds = torch.stack(hidden_states) uncond_multi_facial_embeds = torch.stack(uncond_hidden_states) # condition facial_prompt_embeds = self.FacialEncoder(prompt_embeds, multi_facial_embeds, facial_token_masks, valid_facial_token_idx_mask) # uncondition uncond_facial_prompt_embeds = self.FacialEncoder(negative_prompt_embeds, uncond_multi_facial_embeds, facial_token_masks, valid_facial_token_idx_mask) return facial_prompt_embeds, uncond_facial_prompt_embeds @torch.inference_mode() def get_image_embeds(self, faceid_embeds, face_image, s_scale, shortcut=False): clip_image = self.clip_image_processor(images=face_image, return_tensors="pt").pixel_values clip_image = clip_image.to(self.device, dtype=self.torch_dtype) clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2] uncond_clip_image_embeds = self.image_encoder(torch.zeros_like(clip_image), output_hidden_states=True).hidden_states[-2] faceid_embeds = faceid_embeds.to(self.device, dtype=self.torch_dtype) image_prompt_tokens = self.image_proj_model(faceid_embeds, clip_image_embeds, shortcut=shortcut, scale=s_scale) uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds), uncond_clip_image_embeds, shortcut=shortcut, scale=s_scale) return image_prompt_tokens, uncond_image_prompt_embeds def set_scale(self, scale): for attn_processor in self.pipe.unet.attn_processors.values(): if isinstance(attn_processor, Consistent_IPAttProcessor): attn_processor.scale = scale @torch.inference_mode() def get_prepare_faceid(self, face_image): faceid_image = np.array(face_image) faces = self.app.get(faceid_image) if faces==[]: faceid_embeds = torch.zeros_like(torch.empty((1, 512))) else: faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0) return faceid_embeds @torch.inference_mode() def parsing_face_mask(self, raw_image_refer): to_tensor = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), ]) to_pil = transforms.ToPILImage() with torch.no_grad(): image = raw_image_refer.resize((512, 512), Image.BILINEAR) image_resize_PIL = image img = to_tensor(image) img = torch.unsqueeze(img, 0) img = img.float().cuda() out = self.bise_net(img)[0] parsing_anno = out.squeeze(0).cpu().numpy().argmax(0) im = np.array(image_resize_PIL) vis_im = im.copy().astype(np.uint8) stride=1 vis_parsing_anno = parsing_anno.copy().astype(np.uint8) vis_parsing_anno = cv2.resize(vis_parsing_anno, None, fx=stride, fy=stride, interpolation=cv2.INTER_NEAREST) vis_parsing_anno_color = np.zeros((vis_parsing_anno.shape[0], vis_parsing_anno.shape[1], 3)) + 255 num_of_class = np.max(vis_parsing_anno) for pi in range(1, num_of_class + 1): # num_of_class=17 pi=1~16 index = np.where(vis_parsing_anno == pi) vis_parsing_anno_color[index[0], index[1], :] = self.part_colors[pi] vis_parsing_anno_color = vis_parsing_anno_color.astype(np.uint8) vis_parsing_anno_color = cv2.addWeighted(cv2.cvtColor(vis_im, cv2.COLOR_RGB2BGR), 0.4, vis_parsing_anno_color, 0.6, 0) return vis_parsing_anno_color, vis_parsing_anno @torch.inference_mode() def get_prepare_llva_caption(self, input_image_file, model_path=None, prompt=None): ### Optional: Use the LLaVA # args = type('Args', (), { # "model_path": self.llva_model_path, # "model_base": None, # "model_name": get_model_name_from_path(self.llva_model_path), # "query": self.llva_prompt, # "conv_mode": None, # "image_file": input_image_file, # "sep": ",", # "temperature": 0, # "top_p": None, # "num_beams": 1, # "max_new_tokens": 512 # })() # face_caption = eval_model(args, self.llva_tokenizer, self.llva_model, self.llva_image_processor) ### Use built-in template face_caption = "The person has one nose, two eyes, two ears, and a mouth." return face_caption @torch.inference_mode() def get_prepare_facemask(self, input_image_file): vis_parsing_anno_color, vis_parsing_anno = self.parsing_face_mask(input_image_file) parsing_mask_list = masks_for_unique_values(vis_parsing_anno) key_parsing_mask_list = {} key_list = ["Face", "Left_Ear", "Right_Ear", "Left_Eye", "Right_Eye", "Nose", "Upper_Lip", "Lower_Lip"] processed_keys = set() for key, mask_image in parsing_mask_list.items(): if key in key_list: if "_" in key: prefix = key.split("_")[1] if prefix in processed_keys: continue else: key_parsing_mask_list[key] = mask_image processed_keys.add(prefix) key_parsing_mask_list[key] = mask_image return key_parsing_mask_list, vis_parsing_anno_color def encode_prompt_with_trigger_word( self, prompt: str, face_caption: str, key_parsing_mask_list = None, image_token = "<|image|>", facial_token = "<|facial|>", max_num_facials = 5, num_id_images: int = 1, device: Optional[torch.device] = None, ): device = device or self._execution_device face_caption_align, key_parsing_mask_list_align = process_text_with_markers(face_caption, key_parsing_mask_list) prompt_face = prompt + "Detail:" + face_caption_align max_text_length=330 if len(self.tokenizer(prompt_face, max_length=self.tokenizer.model_max_length, padding="max_length",truncation=False,return_tensors="pt").input_ids[0])!=77: prompt_face = "Detail:" + face_caption_align + " Caption:" + prompt if len(face_caption)>max_text_length: prompt_face = prompt face_caption_align = "" prompt_text_only = prompt_face.replace("<|facial|>", "").replace("<|image|>", "") tokenizer = self.tokenizer facial_token_id = tokenizer.convert_tokens_to_ids(facial_token) image_token_id = None clean_input_id, image_token_mask, facial_token_mask = tokenize_and_mask_noun_phrases_ends( prompt_face, image_token_id, facial_token_id, tokenizer) image_token_idx, image_token_idx_mask, facial_token_idx, facial_token_idx_mask = prepare_image_token_idx( image_token_mask, facial_token_mask, num_id_images, max_num_facials ) return prompt_text_only, clean_input_id, key_parsing_mask_list_align, facial_token_mask, facial_token_idx, facial_token_idx_mask @torch.inference_mode() def get_prepare_clip_image(self, input_image_file, key_parsing_mask_list, image_size=512, max_num_facials=5, change_facial=True): facial_mask = [] facial_clip_image = [] transform_mask = transforms.Compose([transforms.CenterCrop(size=image_size), transforms.ToTensor(),]) clip_image_processor = CLIPImageProcessor() num_facial_part = len(key_parsing_mask_list) for key in key_parsing_mask_list: key_mask=key_parsing_mask_list[key] facial_mask.append(transform_mask(key_mask)) key_mask_raw_image = fetch_mask_raw_image(input_image_file,key_mask) parsing_clip_image = clip_image_processor(images=key_mask_raw_image, return_tensors="pt").pixel_values facial_clip_image.append(parsing_clip_image) padding_ficial_clip_image = torch.zeros_like(torch.zeros([1, 3, 224, 224])) padding_ficial_mask = torch.zeros_like(torch.zeros([1, image_size, image_size])) if num_facial_part < max_num_facials: facial_clip_image += [torch.zeros_like(padding_ficial_clip_image) for _ in range(max_num_facials - num_facial_part) ] facial_mask += [ torch.zeros_like(padding_ficial_mask) for _ in range(max_num_facials - num_facial_part)] facial_clip_image = torch.stack(facial_clip_image, dim=1).squeeze(0) facial_mask = torch.stack(facial_mask, dim=0).squeeze(dim=1) return facial_clip_image, facial_mask @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 5.0, negative_prompt: 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, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, original_size: Optional[Tuple[int, int]] = None, target_size: Optional[Tuple[int, int]] = None, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, 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, ): # 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, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ) 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 do_classifier_free_guidance = guidance_scale >= 1.0 input_image_file = input_id_images[0] faceid_embeds = self.get_prepare_faceid(face_image=input_image_file) face_caption = self.get_prepare_llva_caption(input_image_file) key_parsing_mask_list, vis_parsing_anno_color = self.get_prepare_facemask(input_image_file) assert do_classifier_free_guidance # 3. Encode input prompt num_id_images = len(input_id_images) ( prompt_text_only, clean_input_id, key_parsing_mask_list_align, facial_token_mask, facial_token_idx, facial_token_idx_mask, ) = self.encode_prompt_with_trigger_word( prompt = prompt, face_caption = face_caption, # prompt_2=None, key_parsing_mask_list=key_parsing_mask_list, device=device, max_num_facials = 5, num_id_images= num_id_images, # prompt_embeds= None, # pooled_prompt_embeds= None, # class_tokens_mask= None, ) # 4. Encode input prompt without the trigger word for delayed conditioning encoder_hidden_states = self.text_encoder(clean_input_id.to(device))[0] prompt_embeds = self._encode_prompt( prompt_text_only, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=True, negative_prompt=negative_prompt, ) negative_encoder_hidden_states_text_only = prompt_embeds[0:num_images_per_prompt] encoder_hidden_states_text_only = prompt_embeds[num_images_per_prompt:] # 5. Prepare the input ID images prompt_tokens_faceid, uncond_prompt_tokens_faceid = self.get_image_embeds(faceid_embeds, face_image=input_image_file, s_scale=1.0, shortcut=False) facial_clip_image, facial_mask = self.get_prepare_clip_image(input_image_file, key_parsing_mask_list_align, image_size=512, max_num_facials=5) facial_clip_images = facial_clip_image.unsqueeze(0).to(device, dtype=self.torch_dtype) facial_token_mask = facial_token_mask.to(device) facial_token_idx_mask = facial_token_idx_mask.to(device) negative_encoder_hidden_states = negative_encoder_hidden_states_text_only cross_attention_kwargs = {} # 6. Get the update text embedding prompt_embeds_facial, uncond_prompt_embeds_facial = self.get_facial_embeds(encoder_hidden_states, negative_encoder_hidden_states, \ facial_clip_images, facial_token_mask, facial_token_idx_mask) prompt_embeds = torch.cat([prompt_embeds_facial, prompt_tokens_faceid], dim=1) negative_prompt_embeds = torch.cat([uncond_prompt_embeds_facial, uncond_prompt_tokens_faceid], dim=1) prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) prompt_embeds_text_only = torch.cat([encoder_hidden_states_text_only, prompt_tokens_faceid], dim=1) prompt_embeds = torch.cat([prompt_embeds, prompt_embeds_text_only], dim=0) # 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.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) ( null_prompt_embeds, augmented_prompt_embeds, text_prompt_embeds, ) = prompt_embeds.chunk(3) # 9. 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( [null_prompt_embeds, text_prompt_embeds], dim=0 ) else: current_prompt_embeds = torch.cat( [null_prompt_embeds, augmented_prompt_embeds], dim=0 ) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=current_prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, ).sample # 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 ) else: assert 0, "Not Implemented" # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step( noise_pred, t, latents, **extra_step_kwargs ).prev_sample # 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) if output_type == "latent": image = latents has_nsfw_concept = None elif output_type == "pil": # 9.1 Post-processing image = self.decode_latents(latents) # 9.2 Run safety checker image, has_nsfw_concept = self.run_safety_checker( image, device, prompt_embeds.dtype ) # 9.3 Convert to PIL image = self.numpy_to_pil(image) else: # 9.1 Post-processing image = self.decode_latents(latents) # 9.2 Run safety checker image, has_nsfw_concept = self.run_safety_checker( image, device, prompt_embeds.dtype ) # 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, has_nsfw_concept) return StableDiffusionPipelineOutput( images=image, nsfw_content_detected=has_nsfw_concept )