9 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…
Customizing Text-to-Image Diffusion with Object Viewpoint Control
Nupur Kumari, Grace Su, Richard Zhang +3
Model customization introduces new concepts to existing text-to-image models, enabling the generation of these new concepts/objects in novel contexts. However, such methods lack ac…
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…
Distilling Diffusion Models into Conditional GANs
Minguk Kang, Richard Zhang, Connelly Barnes +6
We propose a method to distill a complex multistep diffusion model into a single-step conditional GAN student model, dramatically accelerating inference, while preserving image qua…
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…