@@ -97,7 +97,9 @@ For enterprises interested in adopting SVDQuant or Nunchaku, including technical
-[DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models](https://arxiv.org/abs/2402.19481), CVPR 2024
-[QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving](https://arxiv.org/abs/2405.04532), MLSys 2025
-[SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers](https://arxiv.org/abs/2410.10629), ICLR 2025
-[Radial Attention: $O(n \log n)$ Sparse Attention with Energy Decay for Long Video Generation](https://github.com/mit-han-lab/radial-attention), ArXiv 2025
-[Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity](https://arxiv.org/abs/2502.01776), ICML 2025
-[Radial Attention: $O(n \log n)$ Sparse Attention with Energy Decay for Long Video Generation](https://github.com/mit-han-lab/radial-attention), NeurIPS 2025
-[Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation](https://arxiv.org/abs/2505.18875), NeurIPS 2025
