103 citations · 278 across the 43 of their papers we have counts for
5 papers · 2 filters
Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising
Gongfan Fang, Xinyin Ma, Xinchao Wang
Transformer-based diffusion models have achieved significant advancements across a variety of generative tasks. However, producing high-quality outputs typically necessitates large…
TinyFusion: Diffusion Transformers Learned Shallow
Gongfan Fang, Kunjun Li, Xinyin Ma +1
Diffusion Transformers have demonstrated remarkable capabilities in image generation but often come with excessive parameterization, resulting in considerable inference overhead in…
Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient
Zigeng Chen, Xinyin Ma, Gongfan Fang +1
In the rapidly advancing field of image generation, Visual Auto-Regressive (VAR) modeling has garnered considerable attention for its innovative next-scale prediction approach. Thi…
Isomorphic Pruning for Vision Models
Gongfan Fang, Xinyin Ma, Michael Bi Mi +1
Structured pruning reduces the computational overhead of deep neural networks by removing redundant sub-structures. However, assessing the relative importance of different sub-stru…
AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising
Zigeng Chen, Xinyin Ma, Gongfan Fang +2
Diffusion models have garnered significant interest from the community for their great generative ability across various applications. However, their typical multi-step sequential-…