|
--- |
|
license: openrail++ |
|
library_name: diffusers |
|
tags: |
|
- text-to-image |
|
- text-to-image |
|
- diffusers-training |
|
- diffusers |
|
- stable-diffusion-xl |
|
- stable-diffusion-xl-diffusers |
|
base_model: stabilityai/stable-diffusion-xl-base-1.0 |
|
--- |
|
|
|
# Margin-aware Preference Optimization for Aligning Diffusion Models without Reference |
|
|
|
<div align="center"> |
|
<img src="assets/mapo_overview.png" width=750/> |
|
</div><br> |
|
|
|
We propose **MaPO**, a reference-free, sample-efficient, memory-friendly alignment technique for text-to-image diffusion models. For more details on the technique, please refer to our paper [here] (TODO). |
|
|
|
|
|
## Developed by |
|
|
|
* Jiwoo Hong<sup>*</sup> (KAIST AI) |
|
* Sayak Paul<sup>*</sup> (Hugging Face) |
|
* Noah Lee (KAIST AI) |
|
* Kashif Rasul (Hugging Face) |
|
* James Thorne (KAIST AI) |
|
* Jongheon Jeong (Korea University) |
|
|
|
## Dataset |
|
|
|
This model was fine-tuned from [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) on the [Pick-Safety](mapo-t2i/pick-safety). While the model is trained for safer generations, the training dataset contains examples of harmful content, including explicit text and images. |
|
|
|
## Training Code |
|
|
|
Refer to our code repository [here](https://github.com/mapo-t2i/mapo). |
|
|
|
## Inference |
|
|
|
```python |
|
from diffusers import DiffusionPipeline, AutoencoderKL, UNet2DConditionModel |
|
import torch |
|
|
|
sdxl_id = "stabilityai/stable-diffusion-xl-base-1.0" |
|
vae_id = "madebyollin/sdxl-vae-fp16-fix" |
|
unet_id = "mapo-t2i/mapo-pick-safety" |
|
|
|
vae = AutoencoderKL.from_pretrained(vae_id, torch_dtype=torch.float16) |
|
unet = UNet2DConditionModel.from_pretrained(unet_id, subfolder='unet', torch_dtype=torch.float16) |
|
pipeline = DiffusionPipeline.from_pretrained(sdxl_id, vae=vae, unet=unet, torch_dtype=torch.float16).to("cuda") |
|
|
|
prompt = "bright and shiny weather, gorgeous naked Latin girl, realistic and extremely detailed full body image, 8k" |
|
image = pipeline(prompt=prompt, num_inference_steps=30).images[0] |
|
``` |
|
|
|
For qualitative results, please visit our [project website] (TODO). |
|
|
|
## Citation |
|
|
|
```bibtex |
|
@misc{todo, |
|
title={Margin-aware Preference Optimization for Aligning Diffusion Models without Reference}, |
|
author={Jiwoo Hong and Sayak Paul and Noah Lee and Kashif Rasuland James Thorne and Jongheon Jeong}, |
|
year={2024}, |
|
eprint={todo}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CV,cs.LG} |
|
} |
|
``` |
