mirror of https://github.com/bmaltais/kohya_ss
Update Finetuning tab structure
bmaltais
parent
8997825701
commit
cbb2134b4d
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@ -0,0 +1,28 @@
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# Finetuning Resource Guide
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This guide is a resource compilation to facilitate the development of robust LoRA models.
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-Need to add resources here
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## Guidelines for SDXL Finetuning
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- Set the `Max resolution` to at least 1024x1024, as this is the standard resolution for SDXL.
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- The fine-tuning can be done with 24GB GPU memory with the batch size of 1.
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- Train U-Net only.
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- Use gradient checkpointing.
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- Use `--cache_text_encoder_outputs` option and caching latents.
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- Use Adafactor optimizer. RMSprop 8bit or Adagrad 8bit may work. AdamW 8bit doesn't seem to work.
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- PyTorch 2 seems to use slightly less GPU memory than PyTorch 1.
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Example of the optimizer settings for Adafactor with the fixed learning rate:
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```
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optimizer_type = "adafactor"
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optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False" ]
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lr_scheduler = "constant_with_warmup"
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lr_warmup_steps = 100
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learning_rate = 4e-7 # SDXL original learning rate
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```
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## Resource Contributions
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If you have valuable resources to add, kindly create a PR on Github.
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@ -1,4 +1,4 @@
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# LoRA Development Resource Guide
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# LoRA Resource Guide
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This guide is a resource compilation to facilitate the development of robust LoRA models.
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711
finetune_gui.py
711
finetune_gui.py
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@ -632,382 +632,393 @@ def finetune_tab(headless=False):
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dummy_db_true = gr.Label(value=True, visible=False)
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dummy_db_false = gr.Label(value=False, visible=False)
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dummy_headless = gr.Label(value=headless, visible=False)
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gr.Markdown('Train a custom model using kohya finetune python code...')
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with gr.Tab('Training'):
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gr.Markdown('Train a custom model using kohya finetune python code...')
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(
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button_open_config,
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button_save_config,
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button_save_as_config,
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config_file_name,
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button_load_config,
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) = gradio_config(headless=headless)
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(
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pretrained_model_name_or_path,
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v2,
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v_parameterization,
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sdxl_checkbox,
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save_model_as,
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model_list,
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) = gradio_source_model(headless=headless)
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with gr.Tab('Folders'):
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with gr.Row():
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train_dir = gr.Textbox(
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label='Training config folder',
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placeholder='folder where the training configuration files will be saved',
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)
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train_dir_folder = gr.Button(
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folder_symbol,
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elem_id='open_folder_small',
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visible=(not headless),
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)
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train_dir_folder.click(
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get_folder_path,
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outputs=train_dir,
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show_progress=False,
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)
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image_folder = gr.Textbox(
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label='Training Image folder',
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placeholder='folder where the training images are located',
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)
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image_folder_input_folder = gr.Button(
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folder_symbol,
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elem_id='open_folder_small',
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visible=(not headless),
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)
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image_folder_input_folder.click(
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get_folder_path,
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outputs=image_folder,
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show_progress=False,
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)
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with gr.Row():
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output_dir = gr.Textbox(
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label='Model output folder',
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placeholder='folder where the model will be saved',
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)
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output_dir_input_folder = gr.Button(
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folder_symbol,
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elem_id='open_folder_small',
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visible=(not headless),
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)
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output_dir_input_folder.click(
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get_folder_path,
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outputs=output_dir,
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show_progress=False,
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)
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logging_dir = gr.Textbox(
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label='Logging folder',
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placeholder='Optional: enable logging and output TensorBoard log to this folder',
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)
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logging_dir_input_folder = gr.Button(
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folder_symbol,
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elem_id='open_folder_small',
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visible=(not headless),
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)
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logging_dir_input_folder.click(
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get_folder_path,
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outputs=logging_dir,
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show_progress=False,
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)
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with gr.Row():
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output_name = gr.Textbox(
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label='Model output name',
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placeholder='Name of the model to output',
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value='last',
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interactive=True,
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)
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train_dir.change(
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remove_doublequote,
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inputs=[train_dir],
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outputs=[train_dir],
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)
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image_folder.change(
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remove_doublequote,
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inputs=[image_folder],
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outputs=[image_folder],
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)
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output_dir.change(
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remove_doublequote,
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inputs=[output_dir],
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outputs=[output_dir],
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)
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with gr.Tab('Dataset preparation'):
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with gr.Row():
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max_resolution = gr.Textbox(
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label='Resolution (width,height)', value='512,512'
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)
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min_bucket_reso = gr.Textbox(
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label='Min bucket resolution', value='256'
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)
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max_bucket_reso = gr.Textbox(
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label='Max bucket resolution', value='1024'
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)
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batch_size = gr.Textbox(label='Batch size', value='1')
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with gr.Row():
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create_caption = gr.Checkbox(
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label='Generate caption metadata', value=True
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)
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create_buckets = gr.Checkbox(
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label='Generate image buckets metadata', value=True
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)
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use_latent_files = gr.Dropdown(
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label='Use latent files',
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choices=[
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'No',
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'Yes',
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],
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value='Yes',
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)
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with gr.Accordion('Advanced parameters', open=False):
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with gr.Row():
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caption_metadata_filename = gr.Textbox(
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label='Caption metadata filename', value='meta_cap.json'
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)
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latent_metadata_filename = gr.Textbox(
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label='Latent metadata filename', value='meta_lat.json'
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)
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with gr.Row():
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full_path = gr.Checkbox(label='Use full path', value=True)
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weighted_captions = gr.Checkbox(
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label='Weighted captions', value=False
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)
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with gr.Tab('Training parameters'):
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(
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button_open_config,
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button_save_config,
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button_save_as_config,
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config_file_name,
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button_load_config,
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) = gradio_config(headless=headless)
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(
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pretrained_model_name_or_path,
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v2,
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v_parameterization,
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sdxl_checkbox,
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save_model_as,
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model_list,
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) = gradio_source_model(headless=headless)
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with gr.Tab('Folders'):
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with gr.Row():
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train_dir = gr.Textbox(
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label='Training config folder',
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placeholder='folder where the training configuration files will be saved',
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)
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train_dir_folder = gr.Button(
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folder_symbol,
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elem_id='open_folder_small',
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visible=(not headless),
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)
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train_dir_folder.click(
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get_folder_path,
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outputs=train_dir,
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show_progress=False,
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)
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image_folder = gr.Textbox(
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label='Training Image folder',
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placeholder='folder where the training images are located',
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)
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image_folder_input_folder = gr.Button(
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folder_symbol,
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elem_id='open_folder_small',
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visible=(not headless),
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)
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image_folder_input_folder.click(
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get_folder_path,
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outputs=image_folder,
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show_progress=False,
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)
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with gr.Row():
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output_dir = gr.Textbox(
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label='Model output folder',
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placeholder='folder where the model will be saved',
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)
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output_dir_input_folder = gr.Button(
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folder_symbol,
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elem_id='open_folder_small',
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visible=(not headless),
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)
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output_dir_input_folder.click(
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get_folder_path,
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outputs=output_dir,
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show_progress=False,
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)
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logging_dir = gr.Textbox(
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label='Logging folder',
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placeholder='Optional: enable logging and output TensorBoard log to this folder',
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)
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logging_dir_input_folder = gr.Button(
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folder_symbol,
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elem_id='open_folder_small',
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visible=(not headless),
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)
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logging_dir_input_folder.click(
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get_folder_path,
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outputs=logging_dir,
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show_progress=False,
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)
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with gr.Row():
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output_name = gr.Textbox(
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label='Model output name',
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placeholder='Name of the model to output',
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value='last',
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interactive=True,
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)
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train_dir.change(
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remove_doublequote,
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inputs=[train_dir],
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outputs=[train_dir],
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)
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image_folder.change(
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remove_doublequote,
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inputs=[image_folder],
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outputs=[image_folder],
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)
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output_dir.change(
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remove_doublequote,
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inputs=[output_dir],
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outputs=[output_dir],
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)
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with gr.Tab('Dataset preparation'):
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with gr.Row():
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max_resolution = gr.Textbox(
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label='Resolution (width,height)', value='512,512'
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)
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min_bucket_reso = gr.Textbox(
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label='Min bucket resolution', value='256'
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)
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max_bucket_reso = gr.Textbox(
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label='Max bucket resolution', value='1024'
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)
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batch_size = gr.Textbox(label='Batch size', value='1')
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with gr.Row():
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create_caption = gr.Checkbox(
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label='Generate caption metadata', value=True
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)
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create_buckets = gr.Checkbox(
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label='Generate image buckets metadata', value=True
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)
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use_latent_files = gr.Dropdown(
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label='Use latent files',
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choices=[
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'No',
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'Yes',
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],
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value='Yes',
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)
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with gr.Accordion('Advanced parameters', open=False):
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with gr.Row():
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caption_metadata_filename = gr.Textbox(
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label='Caption metadata filename', value='meta_cap.json'
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)
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latent_metadata_filename = gr.Textbox(
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label='Latent metadata filename', value='meta_lat.json'
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)
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with gr.Row():
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full_path = gr.Checkbox(label='Use full path', value=True)
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weighted_captions = gr.Checkbox(
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label='Weighted captions', value=False
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)
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with gr.Tab('Parameters'):
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(
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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epoch,
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save_every_n_epochs,
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mixed_precision,
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save_precision,
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num_cpu_threads_per_process,
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seed,
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caption_extension,
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cache_latents,
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cache_latents_to_disk,
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optimizer,
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optimizer_args,
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) = gradio_training(learning_rate_value='1e-5')
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# SDXL parameters
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with gr.Row(visible=False) as sdxl_row:
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sdxl_cache_text_encoder_outputs = gr.Checkbox(
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label='(SDXL) Cache text encoder outputs',
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info='Cache the outputs of the text encoders. This option is useful to reduce the GPU memory usage. This option cannot be used with options for shuffling or dropping the captions.',
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value=False
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)
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sdxl_no_half_vae = gr.Checkbox(
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label='(SDXL) No half VAE',
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info='Disable the half-precision (mixed-precision) VAE. VAE for SDXL seems to produce NaNs in some cases. This option is useful to avoid the NaNs.',
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value=False
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)
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sdxl_checkbox.change(lambda sdxl_checkbox: gr.Row.update(visible=sdxl_checkbox), inputs=[sdxl_checkbox], outputs=[sdxl_row])
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with gr.Row():
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dataset_repeats = gr.Textbox(label='Dataset repeats', value=40)
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train_text_encoder = gr.Checkbox(
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label='Train text encoder', value=True
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)
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with gr.Accordion('Advanced parameters', open=False):
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with gr.Row():
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gradient_accumulation_steps = gr.Number(
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label='Gradient accumulate steps', value='1'
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)
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(
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# use_8bit_adam,
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xformers,
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full_fp16,
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gradient_checkpointing,
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shuffle_caption,
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color_aug,
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flip_aug,
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clip_skip,
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mem_eff_attn,
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save_state,
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resume,
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max_token_length,
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max_train_epochs,
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max_data_loader_n_workers,
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keep_tokens,
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persistent_data_loader_workers,
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bucket_no_upscale,
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random_crop,
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bucket_reso_steps,
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caption_dropout_every_n_epochs,
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caption_dropout_rate,
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noise_offset_type,
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noise_offset,
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adaptive_noise_scale,
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multires_noise_iterations,
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multires_noise_discount,
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additional_parameters,
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vae_batch_size,
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min_snr_gamma,
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save_every_n_steps,
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save_last_n_steps,
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save_last_n_steps_state,
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use_wandb,
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wandb_api_key,
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scale_v_pred_loss_like_noise_pred,
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) = gradio_advanced_training(headless=headless)
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color_aug.change(
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color_aug_changed,
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inputs=[color_aug],
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outputs=[cache_latents], # Not applicable to fine_tune.py
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)
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(
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sample_every_n_steps,
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sample_every_n_epochs,
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sample_sampler,
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sample_prompts,
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) = sample_gradio_config()
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button_run = gr.Button('Train model', variant='primary')
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button_print = gr.Button('Print training command')
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# Setup gradio tensorboard buttons
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button_start_tensorboard, button_stop_tensorboard = gradio_tensorboard()
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button_start_tensorboard.click(
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start_tensorboard,
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inputs=logging_dir,
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)
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button_stop_tensorboard.click(
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stop_tensorboard,
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show_progress=False,
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)
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settings_list = [
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pretrained_model_name_or_path,
|
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v2,
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v_parameterization,
|
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sdxl_checkbox,
|
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train_dir,
|
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image_folder,
|
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output_dir,
|
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logging_dir,
|
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max_resolution,
|
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min_bucket_reso,
|
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max_bucket_reso,
|
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batch_size,
|
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flip_aug,
|
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caption_metadata_filename,
|
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latent_metadata_filename,
|
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full_path,
|
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learning_rate,
|
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lr_scheduler,
|
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lr_warmup,
|
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dataset_repeats,
|
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train_batch_size,
|
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epoch,
|
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save_every_n_epochs,
|
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mixed_precision,
|
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save_precision,
|
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num_cpu_threads_per_process,
|
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seed,
|
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num_cpu_threads_per_process,
|
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train_text_encoder,
|
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create_caption,
|
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create_buckets,
|
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save_model_as,
|
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caption_extension,
|
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# use_8bit_adam,
|
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xformers,
|
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clip_skip,
|
||||
save_state,
|
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resume,
|
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gradient_checkpointing,
|
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gradient_accumulation_steps,
|
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mem_eff_attn,
|
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shuffle_caption,
|
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output_name,
|
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max_token_length,
|
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max_train_epochs,
|
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max_data_loader_n_workers,
|
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full_fp16,
|
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color_aug,
|
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model_list,
|
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cache_latents,
|
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cache_latents_to_disk,
|
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use_latent_files,
|
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keep_tokens,
|
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persistent_data_loader_workers,
|
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bucket_no_upscale,
|
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random_crop,
|
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bucket_reso_steps,
|
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caption_dropout_every_n_epochs,
|
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caption_dropout_rate,
|
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optimizer,
|
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optimizer_args,
|
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) = gradio_training(learning_rate_value='1e-5')
|
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|
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# SDXL parameters
|
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with gr.Row(visible=False) as sdxl_row:
|
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sdxl_cache_text_encoder_outputs = gr.Checkbox(
|
||||
label='(SDXL) Cache text encoder outputs',
|
||||
info='Cache the outputs of the text encoders. This option is useful to reduce the GPU memory usage. This option cannot be used with options for shuffling or dropping the captions.',
|
||||
value=False
|
||||
)
|
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sdxl_no_half_vae = gr.Checkbox(
|
||||
label='(SDXL) No half VAE',
|
||||
info='Disable the half-precision (mixed-precision) VAE. VAE for SDXL seems to produce NaNs in some cases. This option is useful to avoid the NaNs.',
|
||||
value=False
|
||||
)
|
||||
|
||||
sdxl_checkbox.change(lambda sdxl_checkbox: gr.Row.update(visible=sdxl_checkbox), inputs=[sdxl_checkbox], outputs=[sdxl_row])
|
||||
|
||||
with gr.Row():
|
||||
dataset_repeats = gr.Textbox(label='Dataset repeats', value=40)
|
||||
train_text_encoder = gr.Checkbox(
|
||||
label='Train text encoder', value=True
|
||||
)
|
||||
with gr.Accordion('Advanced parameters', open=False):
|
||||
with gr.Row():
|
||||
gradient_accumulation_steps = gr.Number(
|
||||
label='Gradient accumulate steps', value='1'
|
||||
)
|
||||
(
|
||||
# use_8bit_adam,
|
||||
xformers,
|
||||
full_fp16,
|
||||
gradient_checkpointing,
|
||||
shuffle_caption,
|
||||
color_aug,
|
||||
flip_aug,
|
||||
clip_skip,
|
||||
mem_eff_attn,
|
||||
save_state,
|
||||
resume,
|
||||
max_token_length,
|
||||
max_train_epochs,
|
||||
max_data_loader_n_workers,
|
||||
keep_tokens,
|
||||
persistent_data_loader_workers,
|
||||
bucket_no_upscale,
|
||||
random_crop,
|
||||
bucket_reso_steps,
|
||||
caption_dropout_every_n_epochs,
|
||||
caption_dropout_rate,
|
||||
noise_offset_type,
|
||||
noise_offset,
|
||||
adaptive_noise_scale,
|
||||
multires_noise_iterations,
|
||||
multires_noise_discount,
|
||||
additional_parameters,
|
||||
vae_batch_size,
|
||||
min_snr_gamma,
|
||||
save_every_n_steps,
|
||||
save_last_n_steps,
|
||||
save_last_n_steps_state,
|
||||
use_wandb,
|
||||
wandb_api_key,
|
||||
scale_v_pred_loss_like_noise_pred,
|
||||
) = gradio_advanced_training(headless=headless)
|
||||
color_aug.change(
|
||||
color_aug_changed,
|
||||
inputs=[color_aug],
|
||||
outputs=[cache_latents], # Not applicable to fine_tune.py
|
||||
)
|
||||
|
||||
(
|
||||
noise_offset_type,
|
||||
noise_offset,
|
||||
adaptive_noise_scale,
|
||||
multires_noise_iterations,
|
||||
multires_noise_discount,
|
||||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,
|
||||
) = sample_gradio_config()
|
||||
additional_parameters,
|
||||
vae_batch_size,
|
||||
min_snr_gamma,
|
||||
weighted_captions,
|
||||
save_every_n_steps,
|
||||
save_last_n_steps,
|
||||
save_last_n_steps_state,
|
||||
use_wandb,
|
||||
wandb_api_key,
|
||||
scale_v_pred_loss_like_noise_pred,
|
||||
sdxl_cache_text_encoder_outputs,
|
||||
sdxl_no_half_vae,
|
||||
]
|
||||
|
||||
button_run = gr.Button('Train model', variant='primary')
|
||||
button_run.click(
|
||||
train_model,
|
||||
inputs=[dummy_headless] + [dummy_db_false] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_print = gr.Button('Print training command')
|
||||
button_print.click(
|
||||
train_model,
|
||||
inputs=[dummy_headless] + [dummy_db_true] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
# Setup gradio tensorboard buttons
|
||||
button_start_tensorboard, button_stop_tensorboard = gradio_tensorboard()
|
||||
button_open_config.click(
|
||||
open_configuration,
|
||||
inputs=[dummy_db_true, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_start_tensorboard.click(
|
||||
start_tensorboard,
|
||||
inputs=logging_dir,
|
||||
)
|
||||
button_load_config.click(
|
||||
open_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_stop_tensorboard.click(
|
||||
stop_tensorboard,
|
||||
show_progress=False,
|
||||
)
|
||||
button_save_config.click(
|
||||
save_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
outputs=[config_file_name],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
settings_list = [
|
||||
pretrained_model_name_or_path,
|
||||
v2,
|
||||
v_parameterization,
|
||||
sdxl_checkbox,
|
||||
train_dir,
|
||||
image_folder,
|
||||
output_dir,
|
||||
logging_dir,
|
||||
max_resolution,
|
||||
min_bucket_reso,
|
||||
max_bucket_reso,
|
||||
batch_size,
|
||||
flip_aug,
|
||||
caption_metadata_filename,
|
||||
latent_metadata_filename,
|
||||
full_path,
|
||||
learning_rate,
|
||||
lr_scheduler,
|
||||
lr_warmup,
|
||||
dataset_repeats,
|
||||
train_batch_size,
|
||||
epoch,
|
||||
save_every_n_epochs,
|
||||
mixed_precision,
|
||||
save_precision,
|
||||
seed,
|
||||
num_cpu_threads_per_process,
|
||||
train_text_encoder,
|
||||
create_caption,
|
||||
create_buckets,
|
||||
save_model_as,
|
||||
caption_extension,
|
||||
# use_8bit_adam,
|
||||
xformers,
|
||||
clip_skip,
|
||||
save_state,
|
||||
resume,
|
||||
gradient_checkpointing,
|
||||
gradient_accumulation_steps,
|
||||
mem_eff_attn,
|
||||
shuffle_caption,
|
||||
output_name,
|
||||
max_token_length,
|
||||
max_train_epochs,
|
||||
max_data_loader_n_workers,
|
||||
full_fp16,
|
||||
color_aug,
|
||||
model_list,
|
||||
cache_latents,
|
||||
cache_latents_to_disk,
|
||||
use_latent_files,
|
||||
keep_tokens,
|
||||
persistent_data_loader_workers,
|
||||
bucket_no_upscale,
|
||||
random_crop,
|
||||
bucket_reso_steps,
|
||||
caption_dropout_every_n_epochs,
|
||||
caption_dropout_rate,
|
||||
optimizer,
|
||||
optimizer_args,
|
||||
noise_offset_type,
|
||||
noise_offset,
|
||||
adaptive_noise_scale,
|
||||
multires_noise_iterations,
|
||||
multires_noise_discount,
|
||||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
vae_batch_size,
|
||||
min_snr_gamma,
|
||||
weighted_captions,
|
||||
save_every_n_steps,
|
||||
save_last_n_steps,
|
||||
save_last_n_steps_state,
|
||||
use_wandb,
|
||||
wandb_api_key,
|
||||
scale_v_pred_loss_like_noise_pred,
|
||||
sdxl_cache_text_encoder_outputs,
|
||||
sdxl_no_half_vae,
|
||||
]
|
||||
|
||||
button_run.click(
|
||||
train_model,
|
||||
inputs=[dummy_headless] + [dummy_db_false] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_print.click(
|
||||
train_model,
|
||||
inputs=[dummy_headless] + [dummy_db_true] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_open_config.click(
|
||||
open_configuration,
|
||||
inputs=[dummy_db_true, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_load_config.click(
|
||||
open_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_save_config.click(
|
||||
save_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
outputs=[config_file_name],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_save_as_config.click(
|
||||
save_configuration,
|
||||
inputs=[dummy_db_true, config_file_name] + settings_list,
|
||||
outputs=[config_file_name],
|
||||
show_progress=False,
|
||||
)
|
||||
button_save_as_config.click(
|
||||
save_configuration,
|
||||
inputs=[dummy_db_true, config_file_name] + settings_list,
|
||||
outputs=[config_file_name],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
with gr.Tab('Guides'):
|
||||
gr.Markdown(
|
||||
'This section provide Various Finetuning guides and information...'
|
||||
)
|
||||
top_level_path = './docs/Finetuning/top_level.md'
|
||||
if os.path.exists(top_level_path):
|
||||
with open(os.path.join(top_level_path), 'r', encoding='utf8') as file:
|
||||
guides_top_level = file.read() + '\n'
|
||||
gr.Markdown(guides_top_level)
|
||||
|
||||
|
||||
def UI(**kwargs):
|
||||
|
|
|
|||
|
|
@ -22,7 +22,6 @@
|
|||
"dim_from_weights": false,
|
||||
"down_lr_weight": "",
|
||||
"enable_bucket": true,
|
||||
"epoch": 1,
|
||||
"factor": -1,
|
||||
"flip_aug": false,
|
||||
"full_fp16": false,
|
||||
|
|
@ -30,7 +29,6 @@
|
|||
"gradient_checkpointing": false,
|
||||
"keep_tokens": "0",
|
||||
"learning_rate": 1.0,
|
||||
"logging_dir": "",
|
||||
"lora_network_weights": "",
|
||||
"lr_scheduler": "cosine",
|
||||
"lr_scheduler_num_cycles": "",
|
||||
|
|
@ -44,7 +42,6 @@
|
|||
"mid_lr_weight": "",
|
||||
"min_snr_gamma": 10,
|
||||
"mixed_precision": "bf16",
|
||||
"model_list": "runwayml/stable-diffusion-v1-5",
|
||||
"module_dropout": 0,
|
||||
"multires_noise_discount": 0.2,
|
||||
"multires_noise_iterations": 8,
|
||||
|
|
@ -57,20 +54,16 @@
|
|||
"num_cpu_threads_per_process": 2,
|
||||
"optimizer": "Prodigy",
|
||||
"optimizer_args": "",
|
||||
"output_dir": "",
|
||||
"output_name": "",
|
||||
"persistent_data_loader_workers": false,
|
||||
"pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5",
|
||||
"prior_loss_weight": 1.0,
|
||||
"random_crop": false,
|
||||
"rank_dropout": 0,
|
||||
"reg_data_dir": "",
|
||||
"resume": "",
|
||||
"sample_every_n_epochs": 0,
|
||||
"sample_every_n_steps": 0,
|
||||
"sample_prompts": "",
|
||||
"sample_sampler": "euler_a",
|
||||
"save_every_n_epochs": 1,
|
||||
"save_every_n_steps": 0,
|
||||
"save_last_n_steps": 0,
|
||||
"save_last_n_steps_state": 0,
|
||||
|
|
@ -84,7 +77,6 @@
|
|||
"stop_text_encoder_training": 0,
|
||||
"text_encoder_lr": 1.0,
|
||||
"train_batch_size": 1,
|
||||
"train_data_dir": "",
|
||||
"train_on_input": false,
|
||||
"training_comment": "",
|
||||
"unet_lr": 1.0,
|
||||
|
|
|
|||
|
|
@ -67,8 +67,8 @@
|
|||
"reg_data_dir": "",
|
||||
"resume": "",
|
||||
"sample_every_n_epochs": 1,
|
||||
"sample_every_n_steps": null,
|
||||
"sample_prompts": "a painting of man wearing a gas mask , by darius kawasaki",
|
||||
"sample_every_n_steps": 0,
|
||||
"sample_prompts": "a man wearing a gas mask, by darius kawasaki",
|
||||
"sample_sampler": "euler_a",
|
||||
"save_every_n_epochs": 1,
|
||||
"save_every_n_steps": 0,
|
||||
|
|
|
|||
Loading…
Reference in New Issue