11 papers
Variable-Length Generative Protein Design via Generalized Poisson Flow
Chaoran Cheng, Zhanghan Ni, Yanru Qu +4
The ability to generate variable-length proteins is crucial in protein design, where the optimal length is often unknown and tightly coupled to designability. Current diffusion- an…
LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling
Yuxin Chen, Chumeng Liang, Hangke Sui +4
Continuous diffusion has been the foundation of high-fidelity, controllable, and few-step generation of many data modalities such as images. However, in language modeling, prior co…
M3: High-fidelity Text-to-Image Generation via Multi-Modal, Multi-Agent and Multi-Round Visual Reasoning
Bangji Yang, Ruihan Guo, Jiajun Fan +2
Generative models have achieved impressive fidelity in text-to-image synthesis, yet struggle with complex compositional prompts involving multiple constraints. We introduce \textbf…
Categorical Flow Matching on Statistical Manifolds
Chaoran Cheng, Jiahan Li, Jian Peng +1
We introduce Statistical Flow Matching (SFM), a novel and mathematically rigorous flow-matching framework on the manifold of parameterized probability measures inspired by the resu…
Fine-tuning Flow Matching Generative Models with Intermediate Feedback
Jiajun Fan, Chaoran Cheng, Shuaike Shen +2
Flow-based generative models have shown remarkable success in text-to-image generation, yet fine-tuning them with intermediate feedback remains challenging, especially for continuo…
Adaptive Divergence Regularized Policy Optimization for Fine-tuning Generative Models
Jiajun Fan, Tong Wei, Chaoran Cheng +2
Balancing exploration and exploitation during reinforcement learning fine-tuning of generative models presents a critical challenge, as existing approaches rely on fixed divergence…