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On October 30, 2026, the Efficient-Large-Model team, composed of researchers from NVIDIA and MIT, released the model weights for the video enhancement project Open-H3-Regenerate-2K on Hugging Face, and the accompanying inference code has been made available in the NVlabs/Sana repository. The solution is based on MiniMax H3 and supports 2560×1440 2K output, and can achieve native 4K regeneration at up to 3840×2160; during training, the H3 Transformer backbone and the video VAE are frozen, and only the newly added refinement LoRA is trained. Team tests show that on a single NVIDIA B200 GPU, the combined time for upscaling, three-step refinement, and 2K decoding of an approximately 5-second video dropped from 87.26 seconds to 22.45 seconds; on RefinerBench-150, the solution's UniPercept average score is 60.0, compared with 53.4 for the official H3-Regenerate-2K API. There are currently no further developments.
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NVIDIA and MIT teams' Open-H3-Regenerate-2K, an open-source video enhancement solution based on MiniMax H3…Developments
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NVIDIA and MIT team release Open-H3-Regenerate-2K model weights
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Oct 30
QbitAI
NVIDIA and MIT teams' Open-H3-Regenerate-2K, an open-source video enhancement solution based on MiniMax H3…
The Efficient-Large-Model team, composed of researchers from NVIDIA and MIT, has released the model weights for the video enhancement project Open-H3-Regenerate-2K on Hugging Face, with the accompanying inference code now available in the NVlabs/Sana repository. Based on MiniMax H3, the solution regenerates low-resolution video drafts into high-resolution video, supports 2K output at 2560×1440, and can achieve native 4K regeneration at up to 3840×2160. The team kept the H3 Transformer backbone and video VAE parameters unchanged, training only the newly added refinement LoRA; at inference time it is merged with community adapters such as LightX2V FL2VA Turbo and LMS Sharp, regenerating image details through three-step denoising. On a single NVIDIA B200 GPU, the combined time for latent-space upscaling, three-step refinement, and 2K decoding of an approximately 5-second video dropped from 87.26 seconds to 22.45 seconds, about 3.9 times faster than before; on RefinerBench-150, UniPercept averaged 60.0, compared with 53.4 for the official H3-Regenerate-2K API.
Leiphone
NVIDIA and MIT Team Open-Source a 4K Video Enhancement Solution Based on MiniMax H3
The Efficient-Large-Model team, composed of researchers from NVIDIA and the Massachusetts Institute of Technology, has released the model weights for the video enhancement project Open-H3-Regenerate-2K on Hugging Face, and the inference code has been made available in the NVlabs/Sana repository. The solution is based on MiniMax H3 and supports 2560×1440 2K output, and can achieve native 4K regeneration at up to 3840×2160. During training, the H3 Transformer backbone and the video VAE are frozen, and only the newly added refinement LoRA is trained. Team tests show that on a single NVIDIA B200 GPU, the combined time for upscaling, three-step refinement, and 2K decoding of an approximately 5-second video dropped from 87.26 seconds to 22.45 seconds; on RefinerBench-150, the solution's UniPercept average score is 60.0, compared with 53.4 for the official H3-Regenerate-2K API.
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