1. Tutorial Introduction

SRPO is a text-to-image generation model jointly launched in September 2025 by Tencent Hunyuan Team, the School of Science of the Chinese University of Hong Kong, Shenzhen, and Tsinghua University Shenzhen International Graduate School. By designing the reward signal as a text-conditional signal, it enables online adjustment of the reward, reducing reliance on offline reward fine-tuning. SRPO introduces Direct-Align technology, directly recovering the original image from any time step through predefined noise priors, avoiding over-optimization in later time steps. Experiments on the FLUX.1.dev model show that SRPO significantly improves the realism and aesthetic quality of generated images as perceived by humans, and its training efficiency is extremely high, requiring only 10 minutes to complete optimization. Related research papers are available. Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference .

This tutorial uses a single A6000 GPU as computing resource. This model currently only supports English prompts.

2. Effect display

3. Operation steps

1. Start the container

If "Bad Gateway" is displayed, it means the model is initializing. Since the model is large, please wait about 2-3 minutes and refresh the page.

2. Usage steps

Specific parameters:

• Prompt: You can enter a text description here.
• Width: Image width.
• Height: The height of the image.
• Guidance Scale: Guidance scale, used to control the influence of text prompts on the final result during image generation.
• Inference Steps: The number of inference steps controls the number of iterations of the generation process, affecting the generation quality and calculation time.
• Seed: Random number seed, used to control the initial value of the randomness generation process.
• Seed Used: The seed used.

4. Discussion

🖌️ If you see a high-quality project, please leave a message in the background to recommend it! In addition, we have also established a tutorial exchange group. Welcome friends to scan the QR code and remark [SD Tutorial] to join the group to discuss various technical issues and share application effects↓

Citation Information

The citation information for this project is as follows:

@misc{shen2025directlyaligningdiffusiontrajectory,
      title={Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference}, 
      author={Xiangwei Shen and Zhimin Li and Zhantao Yang and Shiyi Zhang and Yingfang Zhang and Donghao Li and Chunyu Wang and Qinglin Lu and Yansong Tang},
      year={2025},
      eprint={2509.06942},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2509.06942}, 
}