🚀 Causal realtime streaming SANA-WM open-sourced! Thanks to @reactorworld for serving the model — try demo: sana-wm.reactor.inc/ ~0.93x realtime on single H100, watch 60s 720p live 6-DoF camera control. Code: nvlabs.github.io/Sana/docs/s…

We‘ll be presenting ForeAct (🌟Highlight) at CVPR 2026: 📍 Poster: Sunday, 3:30PM, ExHall A #95 📍 NVIDIA Tech Talk: Friday, 12:40PM, Booth #211 @NVIDIAAI Feel free to stop by and chat! Also find our coffee making demo empowered by ForeAct! (youtube.com/watch?v=kBKJxWUV…) I’ll be at #CVPR2026 from Jun 4-7. Open to chat about VLA, world models and efficient visual generation!

Expert traces can guide RLVR, but full traces may leak key values, executable code, or answer entities — creating an unintended reward-hacking channel. We propose SMEPO: Semantic Masked Expert Policy Optimization. Paper: arxiv.org/abs/2605.25198 Code: github.com/mit-han-lab/SMEPO

🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: github.com/NVlabs/LongLive 📰 Paper: huggingface.co/papers/2605.1… 🎥 Demo: nvlabs.github.io/LongLive/Lo… #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA

One image text camera trajectory = controllable worlds. All on a single GPU. Our research team just released SANA-WM, a 2.6B open source world model natively trained for 60-second video generation with precise camera control.

Sparse attention mechanisms are finally moving beyond academic benchmarks into production systems, including DeepSeek Sparse Attention, and recently @NousResearch 's Lighthouse Attention. BLASST by NVIDIA, from paper Dynamic Blocked Attention Sparsity via Softmax Thresholding, attempts to sparsify attention in a different way, leveraging a similar rescale factor threshold idea from Flash Attention 4. We expect to see more interesting sparse attention techniques in the future. arxiv.org/abs/2512.12087 (2/4)

Sparse attention mechanisms are finally moving beyond academic benchmarks into production systems, including DeepSeek Sparse Attention, and recently @NousResearch 's Lighthouse Attention. BLASST by NVIDIA, from paper Dynamic Blocked Attention Sparsity via Softmax Thresholding, attempts to sparsify attention in a different way, leveraging a similar rescale factor threshold idea from Flash Attention 4. We expect to see more interesting sparse attention techniques in the future. arxiv.org/abs/2512.12087 (2/4)

🤩Excited to share SANA-WM: a 2.6B open-source world model for minute-scale 720p video generation. Given one image text a 6-DoF camera trajectory, it synthesizes action-controllable 60s worlds on a single GPU. Project: nvlabs.github.io/Sana/WM/ Paper: huggingface.co/papers/2605.1…

🚀 We are excited to announce the release of AnyFlow, the first any-step video diffusion on-policy distillation (OPD) framework. By leveraging Flow Map distillation, AnyFlow significantly enhances model inference efficiency by reducing sample steps. (Code, models, and demos are now open-source!) Key Highlights: ⚡ Any-Step Generation: Unlike traditional distilled models tied to fixed step budgets, AnyFlow enables a single model to adapt to arbitrary inference budgets. It achieves high-quality few-step generation while providing stable improvements as more sampling steps are added. 🔀 Multiple Architectures: AnyFlow supports any-step distillation for both causal and bidirectional video diffusion models. 🎬 Multiple Tasks: AnyFlow supports Text-to-Video, Image-to-Video, and Video-to-Video generation within one causal video diffusion model. 📈 Scalable Performance: AnyFlow is validated from 1.3B up to 14B parameters. 📄 Paper: huggingface.co/papers/2605.1… 💻 Code: github.com/NVlabs/AnyFlow 🎨 Pre-trained Models: huggingface.co/collections/n… 🎬 Demo: nvlabs.github.io/AnyFlow/dem…