We present FastComposer which enables efficient, personalized, multi-subject text-to-image generation without fine-tuning.
A training-free algorithm to harness multiple GPUs to accelerate diffusion model inference without sacrificing image quality.
A new conditional control method for diffusion models by dynamically adapting their weight.
VILA is a visual language model (VLM) pre-trained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge.
Quest is an effient long-context LLM inference framework that leverages query-aware sparsity in KV cache to reduce memory movement during attention and thus boost throughput.
LongLoRA takes advantage of shifted sparse attention to greatly reduce the finetuning cost of long context LLMs.
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.
