mapo-t2i
/

mapo-beta

@@ -1,198 +1,139 @@
 ---
 library_name: diffusers
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+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] (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 [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).
+## 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>22.7</td>
+        </tr>
+        <tr class="ours">
+            <td>MaPO (Ours)</td>
+            <td>6.17</td>
+            <td>31.2</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>
+## 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{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}
+}
+```