Delete toonmage/fluxpipeline.py

toonmage/fluxpipeline.py

@@ -1,188 +0,0 @@
-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 toonmage.fluxencoders import IDFormer, PerceiverAttentionCA
-
-
-class ToonMagePipeline(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.toonmage_double_interval = double_interval
-        dit.toonmage_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('SunderAli17/SAK', 'toonmage_flux_v2.safetensors', local_dir ='models')
-        ckpt_path = 'models/toonmage_flux_v2.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