1 citations · 2 across the 21 of their papers we have counts for
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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…
MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models
Gongfan Fang, Hongxu Yin, Saurav Muralidharan +5
Large Language Models (LLMs) are distinguished by their massive parameter counts, which typically result in significant redundancy. This work introduces MaskLLM, a learnable prunin…