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FastVideo open-sourced DreamVerse for REALTIME video generation > Steer clips with natural language > Edit, continue, and rewrite scenes > Frontend backend open source haoailab.com/blogs/fastvideo…

@BrianChen112900 is the co-creator of FastVideo and it is great to have him if you’re working on videogen and world model!

THIS OPEN SOURCE AI TOOL GENERATES A 30 SECOND 1080P VIDEO IN JUST 7 SECONDS Hao AI Lab just dropped FastVideo Dreamverse for real time video generation on a single GPU.

【動画生成に革命】リアルタイム操作AI「FastVideo Dreamverse」 自然言語の会話で動画を自由自在に操作できる「FastVideo Dreamverse」がオープンソースで公開されました！✨ ・「Vibe Directing」により、チャット感覚で指示を送るだけで動画を即座に修正・継続可能 ・最新GPU（Blackwell）への最適化により、1枚のGPUで圧倒的な爆速生成を実現 ・LPUやfMP4技術を活用し、生成しながら即再生できる徹底的な低遅延を追求 RTX 5090等の家庭用ハイエンドGPUへの対応も進んでおり、個人クリエイターの制作環境を劇的に進化させます！🔥 #動画生成AI #AI技術

30秒のAI動画を、7秒で生成できるとしたら、制作工程のどこが一番変わるのか？ Hao AI LabがFastVideo Dreamverseをオープン化。 LTX-2を使ったリアルタイム動画生成がNVIDIA B200 GPU上で動く。 B200前提なのでハードルはあるが、この速度はインパクト。 詳細は🧵

Hao AI Lab open-sourced FastVideo Dreamverse, a real-time video generation app that can create a 30-second 1080p video in 7 seconds on a single NVIDIA B200 GPU. Dreamverse is built on the FastVideo framework and uses the LTX-2 model for fast, interactive video generation.

Bu muhteşem 🚨 🚀 Video üretiminde yeni bir hız rekoru: **FastVideo Dreamverse** açık kaynak olarak yayınlandı! 🙂‍↔️ Hao AI Lab, tek NVIDIA B200 GPU ile 30 saniyelik 1080p videoyu sadece **7 saniyede** üretebilen gerçek zamanlı bir sistem geliştirdi. LTX-2 modeli üzerine kurulu olan Dreamverse, yaratıcılara sahne üretme, doğal dil ile yönlendirme, yeniden yazma ve devam ettirme imkanı sunuyor. En önemli farkı: Yaratıcıyı döngünün dışına değil, **içine** koyuyor. Beklemek yerine anında yönlendirme ve iterasyon yapılabiliyor. Repo: github.com/hao-ai-lab/FastVi… Blog: haoailab.com/blogs/fastvideo… Bu, gerçek zamanlı ve etkileşimli video üretiminin önünü açan önemli bir adım. Orijinal paylaşım: x.com/i/status/2059695648103… Gerçek zamanlı video üretimi yaratıcı iş akışlarını nasıl değiştirecek sizce? 👇

30-second 1080p video in 7 seconds is not a benchmark tweak. It is a different category of speed. FastVideo Dreamverse just went open-source. It runs on a single NVIDIA B200 GPU using the LTX-2 model from @ltx_model, and the core feature is what they call "vibe directing": you guide the generation in real-time through natural language as the video takes shape, rather than submitting a prompt and waiting. The demo shows a clay stop-motion clip being transformed into anime style, which makes the interactive angle concrete. You are not just prompting once; you are steering. The tradeoff worth watching: this runs on a B200, a flagship data center GPU that most people do not have on their desk. The speed numbers are real, but the hardware ceiling is high. What matters longer-term is whether this inference efficiency transfers down to more accessible hardware as the stack matures.

Here’s the AI video shift that matters more than another pretty demo: Dreamverse / FastVideo just open-sourced a self-hosted browser workspace for real-time 1080p video generation and editing. The interesting part isn’t only the clip quality. It’s the architecture: - browser editing workspace - Python runtime for sessions/workers - fMP4 streaming over websocket - prompt rewriting safety filters - Dockerized deployment That turns “generate a video” into “run a video generation app.” Still not consumer-hardware friendly yet — they point to B200 cloud GPUs — but they also say Wan2.1 1.3B is running under 2s on a single RTX 5090 and may be integrated next. That’s the real direction: AI video becoming interactive infrastructure, not just a render button.

Huge thanks to the FastVideo team and contributors who made this release possible. Special thanks to our collaborators and supporters across the ecosystem, including @NVIDIAAI , @ltx_model , @mbzuai , for helping push real-time video generation forward on modern GPU platforms.

Under the hood, Dreamverse uses a NVIDIA B200-optimized FastVideo generation path with: - FA4 FlashAttention - NVFP4 inference - torch.compile - continuation-aware generation - low-latency streaming Checkout haoailab.com/FastVideo/ to see how you can use these optimizations in your FastVideo application!

Dreamverse is our flagship application built on the FastVideo framework. It brings together the core components of a real-time generative video system: - Inference optimization - Streaming support - GPU resource management - Prompt rewriting - App session orchestration

🚀Generate a 30-second 1080p video in just 7 seconds! We’re open-sourcing FastVideo Dreamverse: real-time vibe directing for video generation on a single NVIDIA B200 GPU with LTX-2 model @ltx_model Repo: github.com/hao-ai-lab/FastVi… Blog: haoailab.com/blogs/fastvideo…

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So excited to send our @wlsaidhi to present in this Dynamo event during GTC -- we shared a lot of latest advances, knowledge, OSS development in FastVideo to the greater Nvidia / Dynamo community. Our dreamverse and fastvideo projects get so many excitement from the community. Let's keep the FastVideo Dynamo collaboration going and push for real-time videogen in 2026!!!

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We (FastVideo Team) just keep shipping! For the first time, we push FP4 QAT into attention — and it actually works very well. Let me explain why this is a bigger deal than you might think, as I think the field is underestimating how fast the FP4 era is arriving and how important it will be. Start with the hardware. On Vera Rubin, FP4 is not 2× FP8. If you check the spec, R200 delivers ~50 PFLOPS NVFP4 inference vs ~16 PFLOPS FP8 — over 3× the throughput. NVIDIA is betting VR silicon on FP4. The compute is already sitting on the die, waiting. The only question has ever been whether we can make FP4 work end-to-end. Now look at where that stack stood until this week: ✅ FP8 training — now the norm ✅ FP4 MLP inference — already usable in many production settings ❌ FP4 attention — "too sensitive, too many outliers, won't work" Attention was the missing piece. And without it, there is no such thing as an end-to-end FP4 model — you're always paying the FP8 tax on the part of the network that scales worst with context length. The one component we most want in low precision was the one component nobody had a working recipe for. This work basically fills that last gap. FP4 MLP FP4 attention is now a complete FP4 inference path. That's what I mean when I say this closes the loop on FP4. And if FP4 attention is already here in 2026 — FP2 is closer than the field thinks. 🫡🫡 Go read the thread, try the code, tell us where it breaks