parent
fcfdd95f0b
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@ -472,7 +472,7 @@ def main(args):
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eps=args.adam_epsilon,
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)
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noise_scheduler = DDPMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
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noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
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train_dataset = DreamBoothDataset(
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instance_data_root=args.instance_data_dir,
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@ -372,7 +372,7 @@ def main():
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weight_decay=args.adam_weight_decay,
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eps=args.adam_epsilon,
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)
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noise_scheduler = DDPMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
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noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
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# Get the datasets: you can either provide your own training and evaluation files (see below)
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# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
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@ -605,7 +605,7 @@ def main():
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vae=vae,
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unet=unet,
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tokenizer=tokenizer,
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scheduler=PNDMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler"),
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scheduler=PNDMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler"),
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safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"),
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feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
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)
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@ -441,7 +441,7 @@ def main():
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eps=args.adam_epsilon,
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)
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noise_scheduler = DDPMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
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noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
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train_dataset = TextualInversionDataset(
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data_root=args.train_data_dir,
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@ -574,7 +574,7 @@ def main():
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vae=vae,
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unet=unet,
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tokenizer=tokenizer,
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scheduler=PNDMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler"),
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scheduler=PNDMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler"),
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safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"),
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feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
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)
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