14 papers
BAG: Budget-Aware Gating for Diffusion Caching
Tong Zhao, Mingkun Lei, Yucheng Han +1
Diffusion caching is a lightweight strategy that accelerates Diffusion Transformers (DiTs) by reusing intermediate features across denoising steps, but existing paradigms face a fu…
Momentum Guidance: Plug-and-Play Guidance for Flow Models
Runlong Liao, Jian Yu, Baiyu Su +3
Flow-based generative methods offer a simple and effective framework for high-fidelity generation, yet pretrained flow models are rarely used in their vanilla conditional form: in…
Detail++: Training-Free Detail Enhancer for T2I Diffusion Models
Lifeng Chen, Jiner Wang, Zihao Pan +3
Recent advances in text-to-image (T2I) generation have led to impressive visual results. However, these models still face significant challenges when handling complex prompt, parti…
Budget-Constrained Step-Level Diffusion Caching
Mingkun Lei, Tong Zhao, Liangyu Yuan +1
Step-level caching accelerates diffusion models by exploiting temporal redundancy across denoising steps. Existing methods make per-step cache decisions using threshold-based heuri…
Improving Diffusion Generalization with Weak-to-Strong Segmented Guidance
Liangyu Yuan, Yufei Huang, Mingkun Lei +5
Diffusion models generate synthetic images through an iterative refinement process. However, the misalignment between the simulation-free objective and the iterative process often…
Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation
Jiawei Zhou, Chi Zhang, Xiang Feng +6
We present Omni-I2C, a comprehensive benchmark designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executa…