Apache-2.0 11B text-to-video and image-to-video model from HPC-AI Tech with training and inference code.
Open-Sora 2.0 generates 256px and 768px video from text or an image.
Open-Sora is an open effort to reproduce a Sora-class video model, now at version 2.0 with 11 billion parameters. It suits research teams and well-equipped developers who want to run and study a large open video model.
Open-Sora 2.0 handles text-to-video and image-to-video in one model at 256px and 768px, in several aspect ratios. The repo includes inference scripts, a Gradio demo and weights on Hugging Face and ModelScope.
It suits teams with H100-class GPUs and Python skills. It does not suit laptops or single consumer cards, since the README reports 52 to 60GB of peak memory for one GPU.
A single 11B model covers text-to-video and image-to-video.
Two resolutions with 16:9, 9:16, 1:1 and 2.39:1 aspect ratios.
Frame counts follow a 4k+1 rule and stay under 129.
A Gradio folder and a Hugging Face Space demo the model.
Checkpoints are on Hugging Face and ModelScope for download.
The README shows multi-GPU runs that cut time per clip.
Free under Apache-2.0. The cost is H100-class GPU time or hardware; the repo lists no paid hosted plan.
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The README reports 52.5GB peak memory at 256x256 and 60.3GB at 768x768 on one GPU, measured on H100 and H800 cards. Multi-GPU runs use about 44 to 48GB per card.
The repository lists Apache-2.0 and the README adds no separate weights licence. Confirm the model card on Hugging Face before shipping a product.
You download and run the model on your own hardware, with no credits or queue, but you handle installation, GPU capacity and prompt tuning yourself.
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