8 papers
PromptRL: Prompt Matters in RL for Flow-Based Image Generation
Fu-Yun Wang, Han Zhang, Michael Gharbi +2
Flow matching models (FMs) have revolutionized text-to-image (T2I) generation, with reinforcement learning (RL) serving as a critical post-training strategy for alignment with rewa…
UniRL-Zero: Reinforcement Learning on Unified Models with Joint Language Model and Diffusion Model Experts
Fu-Yun Wang, Han Zhang, Michael Gharbi +2
We present UniRL-Zero, a unified reinforcement learning (RL) framework that boosts, multimodal language model understanding and reasoning, diffusion model multimedia generation, an…
Magic Fixup: Streamlining Photo Editing by Watching Dynamic Videos
Hadi Alzayer, Zhihao Xia, Xuaner Zhang +3
We propose a generative model that, given a coarsely edited image, synthesizes a photorealistic output that follows the prescribed layout. Our method transfers fine details from th…
One-step Diffusion with Distribution Matching Distillation
Tianwei Yin, Michaël Gharbi, Richard Zhang +4
Diffusion models generate high-quality images but require dozens of forward passes. We introduce Distribution Matching Distillation (DMD), a procedure to transform a diffusion mode…
Image Neural Field Diffusion Models
Yinbo Chen, Oliver Wang, Richard Zhang +3
Diffusion models have shown an impressive ability to model complex data distributions, with several key advantages over GANs, such as stable training, better coverage of the traini…
Improved Distribution Matching Distillation for Fast Image Synthesis
Tianwei Yin, Michaël Gharbi, Taesung Park +4
Recent approaches have shown promises distilling diffusion models into efficient one-step generators. Among them, Distribution Matching Distillation (DMD) produces one-step generat…