Spaces:
Runtime error
Runtime error
File size: 4,779 Bytes
c49a0ab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
import os
import cv2
import torch
from modules import devices
from modules.modelloader import load_file_from_url
from annotator.annotator_path import models_path
from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPImageProcessor
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=torch.device('cuda' if torch.cuda.is_available() else '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=torch.device('cuda' if torch.cuda.is_available() else 'cpu'))['uc']
class ClipVisionDetector:
def __init__(self, config):
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 = 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=torch.device('cpu'))
self.model.load_state_dict(sd, strict=False)
del sd
self.model.eval()
self.model.cpu()
def unload_model(self):
if self.model is not None:
self.model.to('meta')
def __call__(self, input_image):
with torch.no_grad():
input_image = cv2.resize(input_image, (224, 224), interpolation=cv2.INTER_AREA)
clip_vision_model = self.model.cpu()
feat = self.processor(images=input_image, return_tensors="pt")
feat['pixel_values'] = feat['pixel_values'].cpu()
result = clip_vision_model(**feat, output_hidden_states=True)
result['hidden_states'] = [v.to(devices.get_device_for("controlnet")) for v in result['hidden_states']]
result = {k: v.to(devices.get_device_for("controlnet")) if isinstance(v, torch.Tensor) else v for k, v in result.items()}
return result
|