Delete ToonMage

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.encoders_flux 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.toonmage_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.toonmage_ca = nn.ModuleList([
-            PerceiverAttentionCA().to(self.device, self.weight_dtype) for _ in range(num_ca)
-        ])
-
-        dit.toonmage_ca = self.toonmage_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.toonmage_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.toonmage_encoder(id_uncond, id_vit_hidden_uncond)
-
-        return id_embedding, uncond_id_embedding

ToonMage/pipeline.py

@@ -1,232 +0,0 @@
-import gc
-
-import cv2
-import insightface
-import torch
-import torch.nn as nn
-from basicsr.utils import img2tensor, tensor2img
-from diffusers import (
-    DPMSolverMultistepScheduler,
-    StableDiffusionXLPipeline,
-    UNet2DConditionModel,
-)
-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.encoders import IDEncoder
-from toonmage.utils import is_torch2_available
-
-if is_torch2_available():
-    from toonmage.attention_processor import AttnProcessor2_0 as AttnProcessor
-    from toonmage.attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
-else:
-    from toonmage.attention_processor import AttnProcessor, IDAttnProcessor
-
-
-class ToonMagePipeline:
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-        self.device = 'cuda'
-        sdxl_base_repo = 'stabilityai/stable-diffusion-xl-base-1.0'
-        sdxl_lightning_repo = 'ByteDance/SDXL-Lightning'
-        self.sdxl_base_repo = sdxl_base_repo
-
-        # load base model
-        unet = UNet2DConditionModel.from_config(sdxl_base_repo, subfolder='unet').to(self.device, torch.float16)
-        unet.load_state_dict(
-            load_file(
-                hf_hub_download(sdxl_lightning_repo, 'sdxl_lightning_4step_unet.safetensors'), device=self.device
-            )
-        )
-        unet.half()
-        self.hack_unet_attn_layers(unet)
-        self.pipe = StableDiffusionXLPipeline.from_pretrained(
-            sdxl_base_repo, unet=unet, torch_dtype=torch.float16, variant="fp16"
-        ).to(self.device)
-        self.pipe.watermark = None
-
-        # scheduler
-        self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(
-            self.pipe.scheduler.config, timestep_spacing="trailing"
-        )
-
-        # ID adapters
-        self.id_adapter = IDEncoder().to(self.device)
-
-        # 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)
-        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)
-
-        print('load done')
-
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        self.load_pretrain()
-
-        # other configs
-        self.debug_img_list = []
-
-    def hack_unet_attn_layers(self, unet):
-        id_adapter_attn_procs = {}
-        for name, _ in unet.attn_processors.items():
-            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 not None:
-                id_adapter_attn_procs[name] = IDAttnProcessor(
-                    hidden_size=hidden_size,
-                    cross_attention_dim=cross_attention_dim,
-                ).to(unet.device)
-            else:
-                id_adapter_attn_procs[name] = AttnProcessor()
-        unet.set_attn_processor(id_adapter_attn_procs)
-        self.id_adapter_attn_layers = nn.ModuleList(unet.attn_processors.values())
-
-    def load_pretrain(self):
-        hf_hub_download('SunderAli17/SAK', 'toonmage_v2.bin', local_dir='models')
-        ckpt_path = 'models/toonmage_v2.bin'
-        state_dict = torch.load(ckpt_path, map_location='cpu')
-        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)
-
-    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):
-        """
-        Args:
-            image: numpy rgb image, range [0, 255]
-        """
-        self.face_helper.clean_all()
-        image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-        # get antelopev2 embedding
-        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')
-            id_ante_embedding = self.handler_ante.get_feat(align_face)
-
-        id_ante_embedding = torch.from_numpy(id_ante_embedding).to(self.device)
-        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, 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_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]))
-
-        id_embedding = self.id_adapter(id_cond, id_vit_hidden)
-        uncond_id_embedding = self.id_adapter(id_uncond, id_vit_hidden_uncond)
-
-        # return id_embedding
-        return torch.cat((uncond_id_embedding, id_embedding), dim=0)
-
-    def inference(self, prompt, size, prompt_n='', image_embedding=None, id_scale=1.0, guidance_scale=1.2, steps=4):
-        images = self.pipe(
-            prompt=prompt,
-            negative_prompt=prompt_n,
-            num_images_per_prompt=size[0],
-            height=size[1],
-            width=size[2],
-            num_inference_steps=steps,
-            guidance_scale=guidance_scale,
-            cross_attention_kwargs={'id_embedding': image_embedding, 'id_scale': id_scale},
-        ).images
-
-        return images

ToonMage/utils.py

@@ -1,76 +0,0 @@
-import importlib
-import os
-import random
-
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-from transformers import PretrainedConfig
-
-
-def seed_everything(seed):
-    os.environ["PL_GLOBAL_SEED"] = str(seed)
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-
-def is_torch2_available():
-    return hasattr(F, "scaled_dot_product_attention")
-
-
-def instantiate_from_config(config):
-    if "target" not in config:
-        if config == '__is_first_stage__' or config == "__is_unconditional__":
-            return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", {}))
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-
-def drop_seq_token(seq, drop_rate=0.5):
-    idx = torch.randperm(seq.size(1))
-    num_keep_tokens = int(len(idx) * (1 - drop_rate))
-    idx = idx[:num_keep_tokens]
-    seq = seq[:, idx]
-    return seq
-
-
-def import_model_class_from_model_name_or_path(
-    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
-):
-    text_encoder_config = PretrainedConfig.from_pretrained(
-        pretrained_model_name_or_path, subfolder=subfolder, revision=revision
-    )
-    model_class = text_encoder_config.architectures[0]
-
-    if model_class == "CLIPTextModel":
-        from transformers import CLIPTextModel
-
-        return CLIPTextModel
-    elif model_class == "CLIPTextModelWithProjection":  # noqa RET505
-        from transformers import CLIPTextModelWithProjection
-
-        return CLIPTextModelWithProjection
-    else:
-        raise ValueError(f"{model_class} is not supported.")
-
-
-def resize_numpy_image_long(image, resize_long_edge=768):
-    h, w = image.shape[:2]
-    if max(h, w) <= resize_long_edge:
-        return image
-    k = resize_long_edge / max(h, w)
-    h = int(h * k)
-    w = int(w * k)
-    image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
-    return image