README.md

metadata

library_name: diffusers
license: apache-2.0
language:
  - ja
pipeline_tag: text-to-image
tags:
  - stable-diffusion

🐟 Evo-Ukiyoe-v1

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Evo-Ukiyoe-v1 is an experimental education-purpose Japanese woodblock print Ukiyoe style image generation model. The model was train based on Sakana AI's Evo-SDXL-JP. All the dataset used to train Evo-Ukiyoe comes from Ukiyoe images belonged to Ritsumeikan University, Art Research Center.

Please refer to our blog for more details.

Usage

Use the code below to get started with the model.

Click to expand

1. Git clone this model card

   git clone https://huggingface.co/SakanaAI/Evo-Ukiyoe-v1
   

2. Install git-lfs if you don't have it yet.

   sudo apt install git-lfs
   git lfs install
   

3. Create conda env

   conda create -n evo-ukiyoe python=3.11
   conda activate evo-ukiyoe
   

4. Install packages

   cd Evo-Ukiyoe-v1
   pip install -r requirements.txt
   

5. Run

   from evo_ukiyoe_v1 import load_evo_ukiyoe
    
   prompt = "着物を着ている猫が庭でお茶を飲んでいる。"
   pipe = load_evo_ukiyoe(device="cuda")
   images = pipe(prompt + "輻の浮世絵。超詳細。", negative_prompt='', guidance_scale=8.0, num_inference_steps=40).images
   images[0].save("image.png")
   

Model Details

• Developed by: Sakana AI
• Model type: Diffusion-based text-to-image generative model
• Language(s): Japanese
• Blog: https://sakana.ai/evo-ukiyoe/

License

The Python script included in this repository and Lora weight are 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

Evo-Ukiyoe was trained based on Evo-SDXL-JP. We would like to thank the developers of Evo-SDXL-JP source models for their contributions and for making their work available.

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

Citation

@misc{Evo-Ukiyoe, 
url    = {[https://huggingface.co/SakanaAI/Evo-Nishikie-v1](https://huggingface.co/SakanaAI/Evo-Nishikie-v1)}, 
title  = {Evo-Ukiyoe}, 
author = {Clanuwat, Tarin and Shing, Makoto and Imajuku, Yuki and Kitamoto, Asanobu and Akama, Ryo}
}