Tongcheng Fang — accepted AI-conference papers

2025

ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation

Tianchen Zhao, Tongcheng Fang, Haofeng Huang, Rui Wan, Widyadewi Soedarmadji, Enshu Liu +7

ICLR 2025poster

Diffusion transformers have demonstrated remarkable performance in visual generation tasks, such as generating realistic images or videos based on textual instructions. However, larger model sizes and multi-frame processing for video generation lead to increased computational and memory costs, posin…

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2024

MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization

Tianchen Zhao*, Xuefei Ning, Tongcheng Fang, Enshu Liu, Guyue Huang, Zinan Lin +3

ECCV 2024poster

"Few-step diffusion models, which enable high-quality text-to-image generation with only a few denoising steps, have substantially reduced inference time. However, considerable memory consumption (5-10GB) still poses limitations for practical deployment on mobile devices. Post-Training Quantization…

Cited by 21 Source ↗PDF ↗code ↗Scholar ↗