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@@ -272,7 +272,7 @@ class CustomDiffusionTrainer(EpochBasedTrainer):
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sample_batch_size: Batch size (per device) for sampling images.
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train_batch_size: Batch size (per device) for the training dataloader.
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center_crop: execute center crop or not.
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concepts_list: Path to json containing multiple concepts, will overwrite parameters like instance_prompt etc.
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concepts_list: Path to json containing multiple concepts, will overwrite parameters.
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instance_data_name: The instance data local dir or online ID.
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"""
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@@ -315,7 +315,6 @@ class CustomDiffusionTrainer(EpochBasedTrainer):
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else:
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with open(self.concepts_list, 'r') as f:
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self.concepts_list = json.load(f)
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print('--------self.concepts_list: ', self.concepts_list)
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for concept in self.concepts_list:
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if not os.path.exists(concept['class_data_dir']):
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@@ -527,7 +526,6 @@ class CustomDiffusionTrainer(EpochBasedTrainer):
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"""
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for i, concept in enumerate(self.concepts_list):
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class_images_dir = Path(concept['class_data_dir'])
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print('-------class_images_dir: ', class_images_dir)
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if not class_images_dir.exists():
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class_images_dir.mkdir(parents=True, exist_ok=True)
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