use modelloader for #4956
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@ -1,4 +1,3 @@
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import contextlib
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import os
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import sys
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import traceback
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@ -11,12 +10,9 @@ from torchvision import transforms
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from torchvision.transforms.functional import InterpolationMode
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import modules.shared as shared
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from modules import devices, paths, lowvram
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from modules import devices, paths, lowvram, modelloader
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blip_image_eval_size = 384
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blip_local_dir = os.path.join('models', 'Interrogator')
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blip_local_file = os.path.join(blip_local_dir, 'model_base_caption_capfilt_large.pth')
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blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
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clip_model_name = 'ViT-L/14'
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Category = namedtuple("Category", ["name", "topn", "items"])
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@ -49,16 +45,14 @@ class InterrogateModels:
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def load_blip_model(self):
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import models.blip
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if not os.path.isfile(blip_local_file):
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if not os.path.isdir(blip_local_dir):
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os.mkdir(blip_local_dir)
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files = modelloader.load_models(
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model_path=os.path.join(paths.models_path, "BLIP"),
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model_url='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth',
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ext_filter=[".pth"],
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download_name='model_base_caption_capfilt_large.pth',
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)
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print("Downloading BLIP...")
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from requests import get as reqget
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open(blip_local_file, 'wb').write(reqget(blip_model_url, allow_redirects=True).content)
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print("BLIP downloaded to", blip_local_file + '.')
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blip_model = models.blip.blip_decoder(pretrained=blip_local_file, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
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blip_model = models.blip.blip_decoder(pretrained=files[0], image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
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blip_model.eval()
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return blip_model
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