Update README.md

README.md

@@ -6,7 +6,7 @@ language:
 - en
 pipeline_tag: text-to-image
 ---
-# Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step
+# Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference
 
 <a href="https://arxiv.org/abs/2406.04314"><img src="https://img.shields.io/badge/Paper-arXiv-red?style=for-the-badge" height=22.5></a>
 <a href="https://github.com/RockeyCoss/SPO"><img src="https://img.shields.io/badge/Gihub-Code-succees?style=for-the-badge&logo=GitHub" height=22.5></a>
@@ -23,15 +23,28 @@ pipeline_tag: text-to-image
 
 ## Abstract
 <p>
-Recently, Direct Preference Optimization (DPO) has extended its success from aligning large language models (LLMs) to aligning text-to-image diffusion models with human preferences.
-Unlike most existing DPO methods that assume all diffusion steps share a consistent preference order with the final generated images, we argue that this assumption neglects step-specific denoising performance and that preference labels should be tailored to each step's contribution.
+    Generating visually appealing images is fundamental to modern text-to-image generation models. 
+    A potential solution to better aesthetics is direct preference optimization (DPO), 
+    which has been applied to diffusion models to improve general image quality including prompt alignment and aesthetics. 
+    Popular DPO methods propagate preference labels from clean image pairs to all the intermediate steps along the two generation trajectories. 
+    However, preference labels provided in existing datasets are blended with layout and aesthetic opinions, which would disagree with aesthetic preference. 
+    Even if aesthetic labels were provided (at substantial cost), it would be hard for the two-trajectory methods to capture nuanced visual differences at different steps.
 </p>
-<p> 
-To address this limitation, we propose Step-aware Preference Optimization (SPO), a novel post-training approach that independently evaluates and adjusts the denoising performance at each step, using a <em>step-aware preference model</em> and a <em>step-wise resampler</em> to ensure accurate step-aware supervision.
-Specifically, at each denoising step, we sample a pool of images, find a suitable win-lose pair, and, most importantly, randomly select a single image from the pool to initialize the next denoising step. This step-wise resampler process ensures the next win-lose image pair comes from the same image, making the win-lose comparison independent of the previous step. To assess the preferences at each step, we train a separate step-aware preference model that can be applied to both noisy and clean images.
-</p> 
 <p>
-Our experiments with Stable Diffusion v1.5 and SDXL demonstrate that SPO significantly outperforms the latest Diffusion-DPO in aligning generated images with complex, detailed prompts and enhancing aesthetics, while also achieving more than 20&times; times faster in training efficiency. Code and model: <a ref="https://rockeycoss.github.io/spo.github.io/">https://rockeycoss.github.io/spo.github.io/</a>
+    To improve aesthetics economically, this paper uses existing generic preference data and introduces step-by-step preference optimization 
+    (SPO) that discards the propagation strategy and allows fine-grained image details to be assessed. Specifically, 
+    at each denoising step, we 1) sample a pool of candidates by denoising from a shared noise latent, 
+    2) use a step-aware preference model to find a suitable win-lose pair to supervise the diffusion model, and 
+    3) randomly select one from the pool to initialize the next denoising step. 
+    This strategy ensures that diffusion models focus on the subtle, fine-grained visual differences 
+    instead of layout aspect. We find that aesthetic can be significantly enhanced by accumulating these 
+    improved minor differences.
+</p>
+<p>
+    When fine-tuning Stable Diffusion v1.5 and SDXL, SPO yields significant 
+    improvements in aesthetics compared with existing DPO methods while not sacrificing image-text alignment 
+    compared with vanilla models. Moreover, SPO converges much faster than DPO methods due to the step-by-step 
+    alignment of fine-grained visual details.
 </p>
 
 ## Model Description
@@ -75,8 +88,8 @@ image.save('moon.png')
 If you find our work or codebase useful, please consider giving us a star and citing our work.
 ```
 @article{liang2024step,
-  title={Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step},
-  author={Liang, Zhanhao and Yuan, Yuhui and Gu, Shuyang and Chen, Bohan and Hang, Tiankai and Li, Ji and Zheng, Liang},
+  title={Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization},
+  author={Liang, Zhanhao and Yuan, Yuhui and Gu, Shuyang and Chen, Bohan and Hang, Tiankai and Cheng, Mingxi and Li, Ji and Zheng, Liang},
   journal={arXiv preprint arXiv:2406.04314},
   year={2024}
 }