EvoSDXL-JP-v1

🐟 EvoSDXL-JP-v1

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EvoSDXL-JP-v1 is an experimental education-purpose Japanese SDXL Lightning. This model was created using the Evolutionary Model Merge method. Please refer to our report and blog for more details. This model was produced by merging the following models. We are grateful to the developers of the source models.

• SDXL
• Juggernaut-XL-v9
• SDXL-DPO
• JSDXL
• SDXL-Lightning

Usage

Use the code below to get started with the model.

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1. Git clone this model card

   git clone https://huggingface.co/SakanaAI/EvoSDXL-JP-v1
   

2. Install packages

   cd EvoSDXL-JP-v1
   pip install -r requirements.txt
   

3. Run

   from evosdxl_jp_v1 import load_evosdxl_jp
    
   prompt = "柴犬"
   pipe = load_evosdxl_jp(device="cuda")
   images = pipe(prompt, num_inference_steps=4, guidance_scale=0).images
   images[0].save("image.png")
   

</details>

Model Details

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• Developed by: Sakana AI
• Model type: Diffusion-based text-to-image generative model
• Language(s): Japanese
• Repository: SakanaAI/evolutionary-model-merge
• Paper: https://arxiv.org/abs/2403.13187
• Blog: https://sakana.ai/evosdxl-jp

License

The Python script included in this repository is licensed under the Apache License 2.0. Please note that the license for the model/pipeline generated by this script is inherited from the source models.

Uses

This model is provided for research and development purposes only and should be considered as an experimental prototype. It is not intended for commercial use or deployment in mission-critical environments. Use of this model is at the user's own risk, and its performance and outcomes are not guaranteed. Sakana AI shall not be liable for any direct, indirect, special, incidental, or consequential damages, or any loss arising from the use of this model, regardless of the results obtained. Users must fully understand the risks associated with the use of this model and use it at their own discretion.

Acknowledgement

We would like to thank the developers of the source models for their contributions and for making their work available.

Citation

@misc{akiba2024evomodelmerge,
      title         = {Evolutionary Optimization of Model Merging Recipes}, 
      author.       = {Takuya Akiba and Makoto Shing and Yujin Tang and Qi Sun and David Ha},
      year          = {2024},
      eprint        = {2403.13187},
      archivePrefix = {arXiv},
      primaryClass  = {cs.NE}
}