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Co-authored-by: Patryk Bartkowiak <patryk.bartkowiak@tcl.com> Co-authored-by: Linoy Tsaban <57615435+linoytsaban@users.noreply.github.com>
57 lines
2.7 KiB
Python
57 lines
2.7 KiB
Python
# Script for converting a Hugging Face Diffusers trained SDXL LoRAs to Kohya format
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# This means that you can input your diffusers-trained LoRAs and
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# Get the output to work with WebUIs such as AUTOMATIC1111, ComfyUI, SD.Next and others.
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# To get started you can find some cool `diffusers` trained LoRAs such as this cute Corgy
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# https://huggingface.co/ignasbud/corgy_dog_LoRA/, download its `pytorch_lora_weights.safetensors` file
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# and run the script:
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# python convert_diffusers_sdxl_lora_to_webui.py --input_lora pytorch_lora_weights.safetensors --output_lora corgy.safetensors
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# now you can use corgy.safetensors in your WebUI of choice!
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# To train your own, here are some diffusers training scripts and utils that you can use and then convert:
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# LoRA Ease - no code SDXL Dreambooth LoRA trainer: https://huggingface.co/spaces/multimodalart/lora-ease
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# Dreambooth Advanced Training Script - state of the art techniques such as pivotal tuning and prodigy optimizer:
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# - Script: https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
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# - Colab (only on Pro): https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/SDXL_Dreambooth_LoRA_advanced_example.ipynb
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# Canonical diffusers training scripts:
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# - Script: https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sdxl.py
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# - Colab (runs on free tier): https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/SDXL_DreamBooth_LoRA_.ipynb
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import argparse
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import os
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from safetensors.torch import load_file, save_file
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from diffusers.utils import convert_all_state_dict_to_peft, convert_state_dict_to_kohya
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def convert_and_save(input_lora, output_lora=None):
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if output_lora is None:
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base_name = os.path.splitext(input_lora)[0]
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output_lora = f"{base_name}_webui.safetensors"
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diffusers_state_dict = load_file(input_lora)
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peft_state_dict = convert_all_state_dict_to_peft(diffusers_state_dict)
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kohya_state_dict = convert_state_dict_to_kohya(peft_state_dict)
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save_file(kohya_state_dict, output_lora)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Convert LoRA model to PEFT and then to Kohya format.")
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parser.add_argument(
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"--input_lora",
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type=str,
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required=True,
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help="Path to the input LoRA model file in the diffusers format.",
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)
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parser.add_argument(
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"--output_lora",
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type=str,
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required=False,
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help="Path for the converted LoRA (safetensors format for AUTOMATIC1111, ComfyUI, etc.). Optional, defaults to input name with a _webui suffix.",
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)
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args = parser.parse_args()
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convert_and_save(args.input_lora, args.output_lora)
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