这是在点谁呢?真难猜
As promised, FastVideo open-sources @MiniMax_AI FastH3 Omni, training code included! 🚀
- Real-time gen @ 12x faster on NVIDIA Blackwell
- NVFP4/FP8 for Spark + RTX!
- ComfyUI day-0
Collab with @NVIDIAAI , Nuva Lab @nuvalab and Reactor @reactorworld
Open source must win! ♥️
DeepSeek 为什么这么便宜?秘密藏在一本「笔记本」
2 分钟看懂 KV Cache
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!
Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.
🔗 github.com/deepseek-ai/DeepI…
1/4
魔改了一下英伟达团队的最新成果Sol-H3-Spark,让原本需要100G显存才能跑起来的模型,在 单卡 4090成功运行,并且生成 5 秒的 720p 视频只需要推理 50 秒左右,保留大部分效果。
关键思路是MiniMax H3 生成的384p潜空间草稿,交给LTX 2.5 进行去噪和高清化,两种模型使用特殊的桥接器粘接。充分利用了MiniMax 的运动和其他优点,又同时利用了LTX生成速度快和高清的优点。并且支持了原生的音频驱动生成,使其可以实现数字人或者MV效果。
🚀 Sol-H3 on DGX Spark: 768p in Under a Minute 🤩
Monday: 8×B300, 5s 768p in 1.65s — faster than playback.
Today: the same stack on one desktop Spark — about 56s hot E2E.
Five seconds of 1344×768 video at 24 FPS with stereo audio, on a single NVIDIA DGX Spark (GB10).
Two-stage, not the datacenter profile:
384p H3 draft → latent ×2 → H3-to-LTX VAE adapter → 768p LTX refine → VAE decode
No decode/re-encode between stages. Stage 2 is conditioned on the draft latent, so Gemma stays off the box. Quantized weights stay resident; sparse attention cuts the rest.
Stage 1 takes any MiniMax-H3 few-step LoRA. Timing is hot E2E (encode → both stages → video/audio VAE); cold start and MP4 mux are separate. Apache 2.0.
Server was realtime. Edge is one box, under a minute.
🔗 nvlabs.github.io/Sana/Sol-En…
Amazing team effort—full credits in the blog.
@haopengl33 @lawrence_cjs @yitongli165665 @shanasaimoe ,Jingyu Xin, @HaochengXiUCB @songhan_mit
Yue2 音乐模型最近要开源了,看演示,cover 功能挺强的,期待一波。
就是那个第一个开源音乐生成模型YUE。
map-yue2.github.io/
















