3 papers
cs.LG2026
PE-OPSD: Internalizing Prompt Enhancement into Flow-matching Models via On-Policy Self-Distillation
Mingfeng Lin, Chengfei Cai, Lin Xu +3
Text-to-image users often provide concise and underspecified prompts, whereas generative models benefit from detailed textual conditions for reliable instruction following. Existin…
cs.LG2026
DreOPD: Degraded-Reference Extrapolative On-Policy Distillation for Flow-matching Models
Mingfeng Lin, Chengfei Cai, Lin Xu +2
Flow-matching models are now a mainstream method to image generation, but its adaptation to diverse downstream scenarios typically relies on post-training, which may cause conflict…
cs.CV2026
MixDiffusion: Mixing Diffusion-based Uni-condition Text-to-Image Generation Models for Multi-condition Image Synthesis
Pengcheng Wan, Liang Han, Lin Xu +2
Recent advances in text-to-image (T2I) generation have enabled controllable image synthesis by incorporating conditions beyond text. However, most existing diffusion-based methods…