Create pipeline.py

toonmage/pipeline.py

@@ -0,0 +1,232 @@
+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