FastVideo 开源预览版 FastH3,15 秒视频生成仅需 13 秒,实现 14 倍加速,45% 用户偏好或认为其与 MiniMax H3 持平。该模型为预览检查点,仅用部分算力和数据训练。引用推文提醒,速度提升需配合质量评估,避免沦为“随机像素生成器”。
45% preferred FastVideo FastH3 or are neutral compared with @MiniMax_AI H3.
Our open-weight FastH3 achieves 15s generation in 13s and 14x speedup. 🚀
Big shoutout to the folks at @fal and @isidentical for helping us run an eval against H3. As an open source project we always appreciate all the help we can get.
The entire team is quite excited by this result for a preview checkpoint trained on a fraction of our planned total compute and partial data. Excited for what our next versions can do!!
Weights: https://huggingface.co/FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree
Blog: https://haoailab.com/blogs/fasth3-preview/
Thank you for your contribution to the open source H3 ecosystem @fal!
Speed is easy when you ignore quality. I can generate random pixels under 1 nanosecond and claim to be ♾x faster than H3 and call it H3-ultra-lightning-fast. But this is not how people should innovate on speed in open. Image and video models are notoriously hard to optimize and we @ fal spend 2-3x more time on evals and making sure optimizations work than actual kernel engineering work. But when you see people claiming they are X times faster than original while messing the quality to a degree that the outputs has nothing to do with original model, it is annoying. Numbers below are for this case, but this is not the first/only one doing it. I'd recommend the authors below, and everyone on the field to run their own evals. Without preference and quality evals in the loop, ai-slop-performance agents can just lead you into a way you generate random pixels and it is just dishonest to call it anything but a random-pixel-generator.在 X 查看被引用的帖子
来源:Hao AI Lab · x.com