File size: 3,434 Bytes
069b516 4009728 069b516 4009728 069b516 4009728 069b516 4009728 069b516 4009728 069b516 4009728 069b516 4009728 069b516 12b7da3 069b516 4009728 069b516 4009728 069b516 4009728 069b516 4009728 069b516 de76e7d 8e31b8d de76e7d 8e31b8d de76e7d 4009728 069b516 4009728 069b516 4009728 069b516 4009728 069b516 4009728 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
---
library_name: diffusers
license: apache-2.0
language:
- ja
pipeline_tag: text-to-image
tags:
- stable-diffusion
---
# π EvoSDXL-JP-v1
π€ [Models](https://huggingface.co/SakanaAI) | π [Paper](https://arxiv.org/abs/2403.13187) | π [Blog](https://sakana.ai/TODO) | π¦ [Twitter](https://twitter.com/SakanaAILabs)
**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](https://arxiv.org/abs/2403.13187) and [blog](https://sakana.ai/TODO) for more details.
This model was produced by merging the following models.
We are grateful to the developers of the source models.
- [SDXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
- [Juggernaut-XL-v9](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9)
- [SDXL-DPO](https://huggingface.co/mhdang/dpo-sdxl-text2image-v1)
- [JSDXL](https://huggingface.co/stabilityai/japanese-stable-diffusion-xl)
- [SDXL-Lightning](https://huggingface.co/ByteDance/SDXL-Lightning)
## Usage
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
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
```python
from evosdxl_jp_v1 import load_evosdxl_jp
prompt = "ζ΄η¬"
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
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Sakana AI](https://sakana.ai/)
- **Model type:** Diffusion-based text-to-image generative model
- **Language(s):** Japanese
- **Repository:** [SakanaAI/evolutionary-model-merge](https://github.com/SakanaAI/evolutionary-model-merge)
- **Paper:** https://arxiv.org/abs/2403.13187
- **Blog:** https://sakana.ai/TODO
## License
The Python script included in this repository is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
Please note that the model/pipeline generated by this script has its own license, which 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
```bibtex
@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}
}
```
|