045750b46a
- Increase max LoRA rank (dim) size to 1024.
- Update finetune preprocessing scripts.
- ``.bmp`` and ``.jpeg`` are supported. Thanks to breakcore2 and p1atdev!
- The default weights of ``tag_images_by_wd14_tagger.py`` is now ``SmilingWolf/wd-v1-4-convnext-tagger-v2``. You can specify another model id from ``SmilingWolf`` by ``--repo_id`` option. Thanks to SmilingWolf for the great work.
- To change the weight, remove ``wd14_tagger_model`` folder, and run the script again.
- ``--max_data_loader_n_workers`` option is added to each script. This option uses the DataLoader for data loading to speed up loading, 20%~30% faster.
- Please specify 2 or 4, depends on the number of CPU cores.
- ``--recursive`` option is added to ``merge_dd_tags_to_metadata.py`` and ``merge_captions_to_metadata.py``, only works with ``--full_path``.
- ``make_captions_by_git.py`` is added. It uses [GIT microsoft/git-large-textcaps](https://huggingface.co/microsoft/git-large-textcaps) for captioning.
- ``requirements.txt`` is updated. If you use this script, [please update the libraries](https://github.com/kohya-ss/sd-scripts#upgrade).
- Usage is almost the same as ``make_captions.py``, but batch size should be smaller.
- ``--remove_words`` option removes as much text as possible (such as ``the word "XXXX" on it``).
- ``--skip_existing`` option is added to ``prepare_buckets_latents.py``. Images with existing npz files are ignored by this option.
- ``clean_captions_and_tags.py`` is updated to remove duplicated or conflicting tags, e.g. ``shirt`` is removed when ``white shirt`` exists. if ``black hair`` is with ``red hair``, both are removed.
- Tag frequency is added to the metadata in ``train_network.py``. Thanks to space-nuko!
- __All tags and number of occurrences of the tag are recorded.__ If you do not want it, disable metadata storing with ``--no_metadata`` option.
69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
import gradio as gr
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import os
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import argparse
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from dreambooth_gui import dreambooth_tab
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from finetune_gui import finetune_tab
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from textual_inversion_gui import ti_tab
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from library.utilities import utilities_tab
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from library.extract_lora_gui import gradio_extract_lora_tab
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from library.merge_lora_gui import gradio_merge_lora_tab
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from lora_gui import lora_tab
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def UI(username, password):
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css = ''
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if os.path.exists('./style.css'):
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with open(os.path.join('./style.css'), 'r', encoding='utf8') as file:
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print('Load CSS...')
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css += file.read() + '\n'
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interface = gr.Blocks(css=css, title="Kohya_ss GUI")
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with interface:
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with gr.Tab('Dreambooth'):
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(
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train_data_dir_input,
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reg_data_dir_input,
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output_dir_input,
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logging_dir_input,
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) = dreambooth_tab()
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with gr.Tab('Dreambooth LoRA'):
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lora_tab()
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with gr.Tab('Dreambooth TI'):
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ti_tab()
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with gr.Tab('Finetune'):
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finetune_tab()
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with gr.Tab('Utilities'):
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utilities_tab(
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train_data_dir_input=train_data_dir_input,
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reg_data_dir_input=reg_data_dir_input,
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output_dir_input=output_dir_input,
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logging_dir_input=logging_dir_input,
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enable_copy_info_button=True,
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)
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gradio_extract_lora_tab()
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gradio_merge_lora_tab()
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# Show the interface
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if not username == '':
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interface.launch(auth=(username, password))
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else:
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interface.launch()
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if __name__ == '__main__':
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# torch.cuda.set_per_process_memory_fraction(0.48)
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--username', type=str, default='', help='Username for authentication'
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)
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parser.add_argument(
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'--password', type=str, default='', help='Password for authentication'
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)
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args = parser.parse_args()
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UI(username=args.username, password=args.password)
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