| | --- |
| | base_model: stabilityai/stable-diffusion-xl-base-1.0 |
| | library_name: diffusers |
| | license: apache-2.0 |
| | pipeline_tag: text-to-image |
| | tags: |
| | - stable-diffusion-xl |
| | - stable-diffusion |
| | - diffusers |
| | - inversion |
| | - dpo |
| | - fine-tuned |
| | --- |
| | |
| | # Inversion-DPO |
| |
|
| | **Original** https://huggingface.co/ezlee258258/Inversion-DPO |
| |
|
| | I have only added vae, text enconders from Stability AI, consolidated the unet and converted to a single .safetensor file FP32 and BF16. |
| |
|
| | **StabilityAI SDXL1.0** https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 |
| |
|
| | **Paper**: [Inversion-DPO: Precise and Efficient Post-Training for Diffusion Models](https://huggingface.co/papers/2507.11554) |
| |
|
| | **Code Repository**: https://github.com/MIGHTYEZ/Inversion-DPO |
| |
|
| | ## Model Description |
| |
|
| | This repository contains the fine-tuned UNet weights from the Inversion-DPO method, built upon Stable Diffusion XL. The model has been trained using Direct Preference Optimization (DPO) techniques combined with inversion methods to improve generation quality and alignment. |
| |
|
| | ## Quick Start |
| |
|
| | ```python |
| | from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel |
| | import torch |
| | |
| | # Load the fine-tuned UNet |
| | unet = UNet2DConditionModel.from_pretrained( |
| | "ezlee258258/Inversion-DPO", |
| | subfolder="unet" |
| | ) |
| | |
| | # Load the pipeline with the fine-tuned UNet |
| | pipe = StableDiffusionXLPipeline.from_pretrained( |
| | "stabilityai/stable-diffusion-xl-base-1.0", |
| | unet=unet |
| | ) |
| | pipe = pipe.to("cuda") |
| | |
| | # Generate images |
| | prompt = "A beautiful landscape with mountains and lakes" |
| | image = pipe(prompt).images[0] |
| | image.save("output.png") |
| | ``` |
| |
|
| | ## Citation |
| |
|
| | If you use this model in your research, please cite our work: |
| |
|
| | ```bibtex |
| | @misc{li2025inversiondpo, |
| | title={Inversion-DPO: Precise and Efficient Post-Training for Diffusion Models}, |
| | author={Zejian Li and Yize Li and Chenye Meng and Zhongni Liu and Yang Ling and Shengyuan Zhang and Guang Yang and Changyuan Yang and Zhiyuan Yang and Lingyun Sun}, |
| | year={2025}, |
| | eprint={2507.11554}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.CV} |
| | } |
| | ``` |
| |
|
| | ## Acknowledgments |
| |
|
| | Built upon [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) by Stability AI. |
| |
|
| | ## Contact |
| |
|
| | For questions and support, please visit our [GitHub repository](https://github.com/MIGHTYEZ/Inversion-DPO). |