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@ -401,7 +401,7 @@ class AspectDataset(torch.utils.data.Dataset):
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(item[1], item[2]),
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(item[1], item[2]),
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bleed=0.0,
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bleed=0.0,
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centering=(0.5, 0.5),
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centering=(0.5, 0.5),
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method=Image.Resampling(Image.LANCZOS)
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method=Image.Resampling.LANCZOS
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)
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)
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return_dict['pixel_values'] = self.transforms(image_file)
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return_dict['pixel_values'] = self.transforms(image_file)
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@ -548,7 +548,7 @@ def main():
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if args.resume:
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if args.resume:
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args.model = args.resume
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args.model = args.resume
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tokenizer = CLIPTokenizer.from_pretrained(args.model, subfolder='tokenizer', use_auth_token=args.hf_token)
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tokenizer = CLIPTokenizer.from_pretrained(args.model, subfolder='tokenizer', use_auth_token=args.hf_token)
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text_encoder = CLIPTextModel.from_pretrained(args.model, subfolder='text_encoder', use_auth_token=args.hf_token)
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text_encoder = CLIPTextModel.from_pretrained(args.model, subfolder='text_encoder', use_auth_token=args.hf_token)
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vae = AutoencoderKL.from_pretrained(args.model, subfolder='vae', use_auth_token=args.hf_token)
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vae = AutoencoderKL.from_pretrained(args.model, subfolder='vae', use_auth_token=args.hf_token)
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