DC-Gen is a general post-training framework that accelerates pre-trained text-to-image diffusion models.
We introduce DC-VideoGen, a post-training acceleration framework for efficient video generation with a Deep Compression Video Autoencoder and a robust adapation strategy AE-Adapt-V.
SANA-1.5 explores efficient training scaling and inference scaling for diffusion models; Deployable on laptop GPU; Top-notch GenEval & DPGBench results.
LServe accelerates long-sequence LLM serving with unified sparse attention for both prefilling and decoding, achieving up to 3.3× speedup over state-of-the-art solution without sacrificing accuracy.
QServe accelerates large-scale LLM serving on GPUs with QoQ (W4A8KV4) quantization, boosting the generation throughputs by up to 3x over the state-of-the-art solution.
A new W4A4 quantization paradigm for diffusion models.
This blog details SANA-Video transition to a Two-Stage Inference Paradigm. By leveraging a high-compression base model for structural discovery (Stage 1) and a step-distilled refiner for high-frequency detail injection (Stage 2), we achieve 2K resolution output without compromising the latency profile of the original 720p generation.
By reducing complexity from O(N^2) to O(N), Linear Attention is the key to processing ultra-long sequences. This post explores its mathematical core—"state accumulation"—and how it unlocks infinite context for LLMs and long video generation.
A sparse attention mechanism with O(nlogn) computational complexity for long video generation. It can speed up both training and inference by 2–4×. The code is available at https://github.com/mit-han-lab/radial-attention
SVDQuant supports NVFP4 on NVIDIA Blackwell GPUs with 3× speedup over BF16 and better image quality than INT4. Try our interactive demo below or at https://svdquant.mit.edu/! Our code is all available at https://github.com/mit-han-lab/nunchaku.
With the arrival of the RTX 5090, we built a high-performance workstation to maximize its AI computing potential. In this blog post, we share our experience—from overcoming setup challenges to testing its performance.
