| import os |
| import cv2 |
| import torch |
| import numpy as np |
|
|
| from einops import rearrange |
| from modules import devices |
| from annotator.annotator_path import models_path |
| from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPImageProcessor |
|
|
| try: |
| from modules.modelloader import load_file_from_url |
| except ImportError: |
| |
| from scripts.utils import load_file_from_url |
|
|
| config_clip_g = { |
| "attention_dropout": 0.0, |
| "dropout": 0.0, |
| "hidden_act": "gelu", |
| "hidden_size": 1664, |
| "image_size": 224, |
| "initializer_factor": 1.0, |
| "initializer_range": 0.02, |
| "intermediate_size": 8192, |
| "layer_norm_eps": 1e-05, |
| "model_type": "clip_vision_model", |
| "num_attention_heads": 16, |
| "num_channels": 3, |
| "num_hidden_layers": 48, |
| "patch_size": 14, |
| "projection_dim": 1280, |
| "torch_dtype": "float32" |
| } |
|
|
| config_clip_h = { |
| "attention_dropout": 0.0, |
| "dropout": 0.0, |
| "hidden_act": "gelu", |
| "hidden_size": 1280, |
| "image_size": 224, |
| "initializer_factor": 1.0, |
| "initializer_range": 0.02, |
| "intermediate_size": 5120, |
| "layer_norm_eps": 1e-05, |
| "model_type": "clip_vision_model", |
| "num_attention_heads": 16, |
| "num_channels": 3, |
| "num_hidden_layers": 32, |
| "patch_size": 14, |
| "projection_dim": 1024, |
| "torch_dtype": "float32" |
| } |
|
|
| config_clip_vitl = { |
| "attention_dropout": 0.0, |
| "dropout": 0.0, |
| "hidden_act": "quick_gelu", |
| "hidden_size": 1024, |
| "image_size": 224, |
| "initializer_factor": 1.0, |
| "initializer_range": 0.02, |
| "intermediate_size": 4096, |
| "layer_norm_eps": 1e-05, |
| "model_type": "clip_vision_model", |
| "num_attention_heads": 16, |
| "num_channels": 3, |
| "num_hidden_layers": 24, |
| "patch_size": 14, |
| "projection_dim": 768, |
| "torch_dtype": "float32" |
| } |
|
|
| configs = { |
| 'clip_g': config_clip_g, |
| 'clip_h': config_clip_h, |
| 'clip_vitl': config_clip_vitl, |
| } |
|
|
| downloads = { |
| 'clip_vitl': 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin', |
| 'clip_g': 'https://huggingface.co/lllyasviel/Annotators/resolve/main/clip_g.pth', |
| 'clip_h': 'https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/pytorch_model.bin' |
| } |
|
|
|
|
| clip_vision_h_uc = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'clip_vision_h_uc.data') |
| clip_vision_h_uc = torch.load(clip_vision_h_uc, map_location=devices.get_device_for("controlnet") if torch.cuda.is_available() else torch.device('cpu'))['uc'] |
|
|
| clip_vision_vith_uc = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'clip_vision_vith_uc.data') |
| clip_vision_vith_uc = torch.load(clip_vision_vith_uc, map_location=devices.get_device_for("controlnet") if torch.cuda.is_available() else torch.device('cpu'))['uc'] |
|
|
|
|
| class ClipVisionDetector: |
| def __init__(self, config, low_vram: bool): |
| assert config in downloads |
| self.download_link = downloads[config] |
| self.model_path = os.path.join(models_path, 'clip_vision') |
| self.file_name = config + '.pth' |
| self.config = configs[config] |
| self.device = ( |
| torch.device("cpu") if low_vram else |
| devices.get_device_for("controlnet") |
| ) |
| os.makedirs(self.model_path, exist_ok=True) |
| file_path = os.path.join(self.model_path, self.file_name) |
| if not os.path.exists(file_path): |
| load_file_from_url(url=self.download_link, model_dir=self.model_path, file_name=self.file_name) |
| config = CLIPVisionConfig(**self.config) |
|
|
| self.model = CLIPVisionModelWithProjection(config) |
| self.processor = CLIPImageProcessor(crop_size=224, |
| do_center_crop=True, |
| do_convert_rgb=True, |
| do_normalize=True, |
| do_resize=True, |
| image_mean=[0.48145466, 0.4578275, 0.40821073], |
| image_std=[0.26862954, 0.26130258, 0.27577711], |
| resample=3, |
| size=224) |
| sd = torch.load(file_path, map_location=self.device) |
| self.model.load_state_dict(sd, strict=False) |
| del sd |
| self.model.to(self.device) |
| self.model.eval() |
|
|
| def unload_model(self): |
| if self.model is not None: |
| self.model.to('meta') |
|
|
| def __call__(self, input_image: np.ndarray): |
| assert isinstance(input_image, np.ndarray) |
| with torch.no_grad(): |
| mask = None |
| input_image = cv2.resize(input_image, (224, 224), interpolation=cv2.INTER_AREA) |
| if input_image.shape[2] == 4: |
| mask = 255 - input_image[:, :, 3:4] |
| input_image = input_image[:, :, :3] |
| feat = self.processor(images=input_image, return_tensors="pt") |
| feat['pixel_values'] = feat['pixel_values'].to(self.device) |
| |
| if mask is not None: |
| mask_tensor = torch.from_numpy(mask).to(self.device).float() / 255.0 |
| feat['pixel_values'] *= rearrange(mask_tensor, "h w c -> 1 c h w") |
| result = self.model(**feat, output_hidden_states=True) |
| result['hidden_states'] = [v.to(self.device) for v in result['hidden_states']] |
| result = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v for k, v in result.items()} |
| return result |
|
|
