Move batchsize check
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@ -456,7 +456,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
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pin_memory = shared.opts.pin_memory
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)
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if shared.opts.save_training_settings_to_txt:
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saved_params = dict(
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@ -46,6 +46,8 @@ class PersonalizedBase(Dataset):
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assert data_root, 'dataset directory not specified'
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assert os.path.isdir(data_root), "Dataset directory doesn't exist"
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assert os.listdir(data_root), "Dataset directory is empty"
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if varsize:
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assert batch_size == 1, 'variable img size must have batch size 1'
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self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
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@ -61,8 +63,6 @@ class PersonalizedBase(Dataset):
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image = Image.open(path).convert('RGB')
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if not varsize:
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image = image.resize((width, height), PIL.Image.BICUBIC)
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else:
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assert batch_size == 1, 'variable img size must have batch size 1'
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except Exception:
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continue
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