3 papers
cs.AI2026
JustQuant: You Don't Need Smoothing, SVD, or Rotation for 4-Bit Activation Quantization
Kaicheng Yang, Kaisen Yang, Chunyu Liu +8
Recent generative models have become increasingly powerful, but their inference cost continues to grow. Model quantization offers a promising way to compress these models and accel…
cs.CV2025
TreeQ: Pushing the Quantization Boundary of Diffusion Transformer via Tree-Structured Mixed-Precision Search
Kaicheng Yang, Kaisen Yang, Baiting Wu +5
Diffusion Transformers (DiTs) have emerged as a highly scalable and effective backbone for image generation, outperforming U-Net architectures in both scalability and performance.…
cs.CV2025
RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
Kaicheng Yang, Xun Zhang, Haotong Qin +4
Diffusion Transformers (DiTs) have recently emerged as a powerful backbone for image generation, demonstrating superior scalability and performance over U-Net architectures. Howeve…