ZiyiZhang27/sdpo
[IEEE TPAMI] Code for the paper "Aligning Few-Step Diffusion Models with Dense Reward Difference Learning"
This project helps machine learning researchers and practitioners who work with diffusion models to improve the quality of images generated by these models, especially when they need results quickly. It takes an existing few-step diffusion model and fine-tunes it using a specialized reinforcement learning technique. The output is a more refined diffusion model that produces images better aligned with specific quality objectives like aesthetics or human preference.
Use this if you are a machine learning engineer or researcher developing high-performance image generation models and need to align few-step diffusion models with specific quality metrics efficiently.
Not ideal if you are looking for an out-of-the-box image generation tool without needing to fine-tune diffusion model architectures or understand reinforcement learning concepts.
Stars
19
Forks
—
Language
Python
License
MIT
Category
Last pushed
Feb 25, 2026
Commits (30d)
0
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