README.md

---
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="https://github.com/mapo-t2i/mapo/blob/main/assets/mapo_overview.png?raw=true" 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](https://arxiv.org/abs/2406.06424).


## 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 [yuvalkirstain/pickapic_v2](mhttps://huggingface.co/datasets/yuvalkirstain/pickapic_v2) dataset. 

## Training Code

Refer to our code repository [here](https://github.com/mapo-t2i/mapo). 

## Qualitative Comparison

<div align="center">
<img src="assets/comparison.png" width=750/>
</div>


## Results

Below we report some quantitative metrics and use them to compare MaPO to existing models: 

<style>
    table {
        width: 100%;
        border-collapse: collapse;
    }
    th, td {
        border: 1px solid #000;
        padding: 8px;
        text-align: center;
    }
    th {
        background-color: #808080;
    }
    .ours {
        font-style: italic;
    }
</style>

<table>
    <caption>Average score for Aesthetic, HPS v2.1, and PickScore</caption>
    <thead>
        <tr>
            <th></th>
            <th>Aesthetic</th>
            <th>HPS v2.1</th>
            <th>Pickscore</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>SDXL</td>
            <td>6.03</td>
            <td>30.0</td>
            <td>22.4</td>
        </tr>
        <tr>
            <td>SFT<sub>Chosen</sub></td>
            <td>5.95</td>
            <td>29.6</td>
            <td>22.0</td>
        </tr>
        <tr>
            <td>Diffusion-DPO</td>
            <td>6.03</td>
            <td>31.1</td>
            <td><b>22.7<b/></td>
        </tr>
        <tr>
            <td><b>MaPO (Ours)<b/></td>
            <td><b>6.17<b/></td>
            <td><b>31.2<b/></td>
            <td>22.5</td>
        </tr>
    </tbody>
</table>


We evaluated this checkpoint in the Imgsys public benchmark. MaPO was able to outperform or match 21 out of 25 state-of-the-art text-to-image diffusion models by ranking 7th on the leaderboard at the time of writing, compared to Diffusion-DPO’s 20th place, while also consuming 14.5% less wall-clock training time on adapting Pick-a-Pic v2. We appreciate the imgsys team for helping us get the human preference data.

<div align="center">
<img src="https://mapo-t2i.github.io/static/images/imgsys.png" width=750/>
</div>

The table below reports memory efficiency of MaPO, making it a better alternative for alignment fine-tuning of diffusion models:

<table>
    <caption>Computational costs of Diffusion-DPO and MaPO</caption>
    <thead>
        <tr>
            <th></th>
            <th>Diffusion-DPO</th>
            <th>MaPO <span class="ours">(Ours)</span></th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>Time (↓)</td>
            <td>63.5</td>
            <td><b>54.3 (-14.5%)</b></td>
        </tr>
        <tr>
            <td>GPU Mem. (↓)</td>
            <td>55.9</td>
            <td><b>46.1 (-17.5%)</b></td>
        </tr>
        <tr>
            <td>Max Batch (↑)</td>
            <td>4</td>
            <td><b>16 (×4)</b></td>
        </tr>
    </tbody>
</table>


## 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-beta"

vae = AutoencoderKL.from_pretrained(vae_id, torch_dtype=torch.float16)
unet = UNet2DConditionModel.from_pretrained(unet_id, torch_dtype=torch.float16)
pipeline = DiffusionPipeline.from_pretrained(sdxl_id, vae=vae, unet=unet, torch_dtype=torch.float16).to("cuda")

prompt = "An abstract portrait consisting of bold, flowing brushstrokes against a neutral background."
image = pipeline(prompt=prompt, num_inference_steps=30).images[0]
```

For qualitative results, please visit our [project website](https://mapo-t2i.github.io/).

## Citation

```bibtex
@misc{hong2024marginaware,
  title={Margin-aware Preference Optimization for Aligning Diffusion Models without Reference}, 
  author={Jiwoo Hong and Sayak Paul and Noah Lee and Kashif Rasul and James Thorne and Jongheon Jeong},
  year={2024},
  eprint={2406.06424},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}
```