mirror of https://github.com/bmaltais/kohya_ss
Fix dataset preparation
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01a6f464f1
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9ac4e92254
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@ -42,6 +42,7 @@ The GUI allows you to set the training parameters and generate and run the requi
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- [SDXL training](#sdxl-training)
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- [Masked loss](#masked-loss)
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- [Change History](#change-history)
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- [2024/04/08 (v23.1.3)](#20240408-v2313)
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- [2024/04/08 (v23.1.2)](#20240408-v2312)
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- [2024/04/07 (v23.1.1)](#20240407-v2311)
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- [2024/04/07 (v23.1.0)](#20240407-v2310)
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@ -405,6 +406,10 @@ ControlNet dataset is used to specify the mask. The mask images should be the RG
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## Change History
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### 2024/04/08 (v23.1.3)
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- Fix dataset preparation bug.
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### 2024/04/08 (v23.1.2)
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- Added config.toml support for wd14_caption.
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@ -12,13 +12,13 @@ log = setup_logging()
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def copy_info_to_Folders_tab(training_folder):
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img_folder = os.path.join(training_folder, "img")
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img_folder = gr.Dropdown(value=os.path.join(training_folder, "img"))
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if os.path.exists(os.path.join(training_folder, "reg")):
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reg_folder = os.path.join(training_folder, "reg")
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reg_folder = gr.Dropdown(value=os.path.join(training_folder, "reg"))
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else:
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reg_folder = ""
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model_folder = os.path.join(training_folder, "model")
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log_folder = os.path.join(training_folder, "log")
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reg_folder = gr.Dropdown(value="")
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model_folder = gr.Dropdown(value=os.path.join(training_folder, "model"))
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log_folder = gr.Dropdown(value=os.path.join(training_folder, "log"))
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return img_folder, reg_folder, model_folder, log_folder
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@ -293,3 +293,17 @@ def gradio_dreambooth_folder_creation_tab(
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],
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show_progress=False,
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)
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button_copy_info_to_Folders_tab = gr.Button('Copy info to respective fields')
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button_copy_info_to_Folders_tab.click(
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copy_info_to_Folders_tab,
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inputs=[util_training_dir_output],
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outputs=[
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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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],
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show_progress=False,
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)
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