7bc93821a0
- Caption dropout is supported in ``train_db.py``, ``fine_tune.py`` and ``train_network.py``. Thanks to forestsource!
- ``--caption_dropout_rate`` option specifies the dropout rate for captions (0~1.0, 0.1 means 10% chance for dropout). If dropout occurs, the image is trained with the empty caption. Default is 0 (no dropout).
- ``--caption_dropout_every_n_epochs`` option specifies how many epochs to drop captions. If ``3`` is specified, in epoch 3, 6, 9 ..., images are trained with all captions empty. Default is None (no dropout).
- ``--caption_tag_dropout_rate`` option specified the dropout rate for tags (comma separated tokens) (0~1.0, 0.1 means 10% chance for dropout). If dropout occurs, the tag is removed from the caption. If ``--keep_tokens`` option is set, these tokens (tags) are not dropped. Default is 0 (no droupout).
- The bulk image downsampling script is added. Documentation is [here](https://github.com/kohya-ss/sd-scripts/blob/main/train_network_README-ja.md#%E7%94%BB%E5%83%8F%E3%83%AA%E3%82%B5%E3%82%A4%E3%82%BA%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%97%E3%83%88) (in Jpanaese). Thanks to bmaltais!
- Typo check is added. Thanks to shirayu!
- Add option to autolaunch the GUI in a browser and set the server_port. USe either `gui.ps1 --inbrowser --server_port 3456`or `gui.cmd -inbrowser -server_port 3456`
75 lines
2.4 KiB
Python
75 lines
2.4 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, inbrowser, server_port):
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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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kwargs = {}
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if username:
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kwargs["auth"] = (username, password)
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if server_port > 0:
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kwargs["server_port"] = server_port
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kwargs["inbrowser"] = inbrowser
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interface.launch(**kwargs)
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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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parser.add_argument(
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'--server_port', type=int, default=0, help='Port to run the server listener on'
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)
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parser.add_argument("--inbrowser", action="store_true", help="Open in browser")
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args = parser.parse_args()
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UI(username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port)
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