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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:
# backward compability for webui < 1.5.0
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: # Has alpha channel.
mask = 255 - input_image[:, :, 3:4] # Invert mask
input_image = input_image[:, :, :3]
feat = self.processor(images=input_image, return_tensors="pt")
feat['pixel_values'] = feat['pixel_values'].to(self.device)
# Apply CLIP mask.
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
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