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MiniMax H3

Open-weights video and audio generation model from MiniMax that turns text, images and references into clips with stereo sound, and can be served locally with SGLang, vLLM, diffusers or ComfyUI.

Video generationOpen models

What it does

MiniMax H3 is a general-purpose generative system that understands text, images, video and audio together and produces video with native stereo sound. Clips run 4 to 15 seconds at 24 fps in many aspect ratios, with a default 768-pixel short side and 2K available through a separate regeneration step. Dialogue is stably supported in 11 languages. The open release has two checkpoints. FL2VA covers text-to-video and generation from a first frame, a last frame or both. Ref2VA takes reference inputs, up to 9 images, 3 video clips and 3 audio clips, with at most 12 files in total. The full weights are released, including for fine-tuning, and the repository's SGLang example starts with four GPUs. Not everything is in the open release. The context-processing stage (H3-Context-IR) and the 2K regeneration stage (H3-Regenerate-2K) are reached through the MiniMax Open Platform API, so a fully local setup produces 768p output. The sparse-attention code is also not yet published. The repository bundles a prompt-writing skill for agent tools plus eight video skills built for MiniMax Hub's canvas workflow. H3 is also offered through an online API, a web app and a desktop app. The repository refers to a MiniMax H3 Community License that governs use, and submitted content is moderated automatically.

Capabilities

  • Video with native stereo audio, 4 to 15 seconds at 24 fps
  • Text-to-video, first and last frame, and multi-reference modes
  • Full weights released as two task-specific checkpoints
  • Local serving through SGLang, vLLM, diffusers and ComfyUI
  • Bundled prompt-writing skill for agent tools

When to use it

  • You want to run an open video-and-audio model on your own GPUs
  • You need to steer a video with reference images, clips or audio
  • You plan to fine-tune a video model starting from released weights
  • You would rather use the hosted API or app than deploy anything yourself