update bibtex

README.md

@@ -9,7 +9,7 @@ library_name: diffusers
 ---
 
 <h1 align="center">
-  Ranking-based Preference Optimization </br> for Diffusion Models from Implicit User Feedback
+  [NeurIPS 2025] Ranking-based Preference Optimization </br> for Diffusion Models from Implicit User Feedback
 </h1>
 
 We present a learning framework that aligns text-to-image diffusion models with human preferences through inverse reinforcement learning and a balance of offline and online training.
@@ -18,9 +18,6 @@ We present a learning framework that aligns text-to-image diffusion models with
 ```python
 import torch
 from diffusers import StableDiffusionPipeline, UNet2DConditionModel
-from dotenv import load_dotenv
-
-load_dotenv()
 
 unet = UNet2DConditionModel.from_pretrained(
     "ylwu/diffusion-dro-sd1.5",
@@ -39,5 +36,18 @@ image = pipe(prompt).images[0]
 image.save("example.png")
 ```
 
+## Citation
+```
+@misc{wu2025rankingbasedpreferenceoptimizationdiffusion,
+      title={Ranking-based Preference Optimization for Diffusion Models from Implicit User Feedback},
+      author={Yi-Lun Wu and Bo-Kai Ruan and Chiang Tseng and Hong-Han Shuai},
+      year={2025},
+      eprint={2510.18353},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2510.18353},
+}
+```
+
 ## License
 The model is licensed under the [CreativeML Open RAIL-M License](https://huggingface.co/spaces/CompVis/stable-diffusion-license).