import gc import cv2 import insightface import torch import torch.nn as nn from basicsr.utils import img2tensor, tensor2img from facexlib.parsing import init_parsing_model from facexlib.utils.face_restoration_helper import FaceRestoreHelper from huggingface_hub import hf_hub_download, snapshot_download from insightface.app import FaceAnalysis from safetensors.torch import load_file from torchvision.transforms import InterpolationMode from torchvision.transforms.functional import normalize, resize from eva_clip import create_model_and_transforms from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD from pulid.encoders_flux import IDFormer, PerceiverAttentionCA class PuLIDPipeline(nn.Module): def __init__(self, dit, device, weight_dtype=torch.bfloat16, *args, **kwargs): super().__init__() self.device = device self.weight_dtype = weight_dtype double_interval = 2 single_interval = 4 # init encoder self.pulid_encoder = IDFormer().to(self.device, self.weight_dtype) num_ca = 19 // double_interval + 38 // single_interval if 19 % double_interval != 0: num_ca += 1 if 38 % single_interval != 0: num_ca += 1 self.pulid_ca = nn.ModuleList([ PerceiverAttentionCA().to(self.device, self.weight_dtype) for _ in range(num_ca) ]) dit.pulid_ca = self.pulid_ca dit.pulid_double_interval = double_interval dit.pulid_single_interval = single_interval # preprocessors # face align and parsing self.face_helper = FaceRestoreHelper( upscale_factor=1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', device=self.device, ) self.face_helper.face_parse = None self.face_helper.face_parse = init_parsing_model(model_name='bisenet', device=self.device) # clip-vit backbone model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True) model = model.visual self.clip_vision_model = model.to(self.device, dtype=self.weight_dtype) eva_transform_mean = getattr(self.clip_vision_model, 'image_mean', OPENAI_DATASET_MEAN) eva_transform_std = getattr(self.clip_vision_model, 'image_std', OPENAI_DATASET_STD) if not isinstance(eva_transform_mean, (list, tuple)): eva_transform_mean = (eva_transform_mean,) * 3 if not isinstance(eva_transform_std, (list, tuple)): eva_transform_std = (eva_transform_std,) * 3 self.eva_transform_mean = eva_transform_mean self.eva_transform_std = eva_transform_std # antelopev2 snapshot_download('DIAMONIK7777/antelopev2', local_dir='models/antelopev2') self.app = FaceAnalysis( name='antelopev2', root='.', providers=['CPUExecutionProvider'] ) self.app.prepare(ctx_id=0, det_size=(640, 640)) self.handler_ante = insightface.model_zoo.get_model('models/antelopev2/glintr100.onnx', providers=['CPUExecutionProvider']) self.handler_ante.prepare(ctx_id=0) gc.collect() torch.cuda.empty_cache() # self.load_pretrain() # other configs self.debug_img_list = [] def load_pretrain(self, pretrain_path=None): hf_hub_download('guozinan/PuLID', 'pulid_flux_v0.9.0.safetensors', local_dir='models') ckpt_path = 'models/pulid_flux_v0.9.0.safetensors' if pretrain_path is not None: ckpt_path = pretrain_path state_dict = load_file(ckpt_path) state_dict_dict = {} for k, v in state_dict.items(): module = k.split('.')[0] state_dict_dict.setdefault(module, {}) new_k = k[len(module) + 1:] state_dict_dict[module][new_k] = v for module in state_dict_dict: print(f'loading from {module}') getattr(self, module).load_state_dict(state_dict_dict[module], strict=True) del state_dict del state_dict_dict def to_gray(self, img): x = 0.299 * img[:, 0:1] + 0.587 * img[:, 1:2] + 0.114 * img[:, 2:3] x = x.repeat(1, 3, 1, 1) return x def get_id_embedding(self, image, cal_uncond=False): """ Args: image: numpy rgb image, range [0, 255] """ self.face_helper.clean_all() self.debug_img_list = [] image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # get antelopev2 embedding # for k in self.app.models.keys(): # self.app.models[k].session.set_providers(['CUDAExecutionProvider']) face_info = self.app.get(image_bgr) if len(face_info) > 0: face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[ -1 ] # only use the maximum face id_ante_embedding = face_info['embedding'] self.debug_img_list.append( image[ int(face_info['bbox'][1]) : int(face_info['bbox'][3]), int(face_info['bbox'][0]) : int(face_info['bbox'][2]), ] ) else: id_ante_embedding = None # using facexlib to detect and align face self.face_helper.read_image(image_bgr) self.face_helper.get_face_landmarks_5(only_center_face=True) self.face_helper.align_warp_face() if len(self.face_helper.cropped_faces) == 0: raise RuntimeError('facexlib align face fail') align_face = self.face_helper.cropped_faces[0] # incase insightface didn't detect face if id_ante_embedding is None: print('fail to detect face using insightface, extract embedding on align face') # self.handler_ante.session.set_providers(['CUDAExecutionProvider']) id_ante_embedding = self.handler_ante.get_feat(align_face) id_ante_embedding = torch.from_numpy(id_ante_embedding).to(self.device, self.weight_dtype) if id_ante_embedding.ndim == 1: id_ante_embedding = id_ante_embedding.unsqueeze(0) # parsing input = img2tensor(align_face, bgr2rgb=True).unsqueeze(0) / 255.0 input = input.to(self.device) parsing_out = self.face_helper.face_parse(normalize(input, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0] parsing_out = parsing_out.argmax(dim=1, keepdim=True) bg_label = [0, 16, 18, 7, 8, 9, 14, 15] bg = sum(parsing_out == i for i in bg_label).bool() white_image = torch.ones_like(input) # only keep the face features face_features_image = torch.where(bg, white_image, self.to_gray(input)) self.debug_img_list.append(tensor2img(face_features_image, rgb2bgr=False)) # transform img before sending to eva-clip-vit face_features_image = resize(face_features_image, self.clip_vision_model.image_size, InterpolationMode.BICUBIC) face_features_image = normalize(face_features_image, self.eva_transform_mean, self.eva_transform_std) id_cond_vit, id_vit_hidden = self.clip_vision_model( face_features_image.to(self.weight_dtype), return_all_features=False, return_hidden=True, shuffle=False ) id_cond_vit_norm = torch.norm(id_cond_vit, 2, 1, True) id_cond_vit = torch.div(id_cond_vit, id_cond_vit_norm) id_cond = torch.cat([id_ante_embedding, id_cond_vit], dim=-1) id_embedding = self.pulid_encoder(id_cond, id_vit_hidden) if not cal_uncond: return id_embedding, None id_uncond = torch.zeros_like(id_cond) id_vit_hidden_uncond = [] for layer_idx in range(0, len(id_vit_hidden)): id_vit_hidden_uncond.append(torch.zeros_like(id_vit_hidden[layer_idx])) uncond_id_embedding = self.pulid_encoder(id_uncond, id_vit_hidden_uncond) return id_embedding, uncond_id_embedding