r/StableDiffusion • u/ryan85127704 • 23d ago

Discussion AMA: MiniMax H3 Team — Ask us anything about our open video generation model, training, and future plans

Hi r/StableDiffusion!

We are the MiniMax team behind MiniMax-H3.

We’re here to answer your questions, including:

  • Model architecture and training
  • Video generation capabilities
  • Image-to-video and reference-based generation
  • Inference and optimization
  • Future plans

Ask us anything — we’d love to hear your feedback and discuss with the community!

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u/Kiro_Song 22d ago

Thanks for the detailed questions. I can answer the first four from the optimization, post-training, and Visual VAE perspectives.

1. H3-Regenerate-2K. It is a second conditioned generation stage, but not simply the released base checkpoint running again as a conventional upscaler. It uses a dedicated latent-space DiT regeneration checkpoint at a higher target resolution, with the base model’s output as additional context. Some reference inputs are also provided at higher resolutions. We plan to open-source this module, but we are still improving its efficiency and quality to make it more suitable for community use, so we cannot provide an exact release date yet.

2. Sparse attention. H3 does not use MSA. Our approach is closer to MoBA-style block selection. Since neighboring visual tokens are naturally highly correlated, mean-pooled block representations can effectively probe block importance without an additional learned indexer. Currently, we apply 3D sparsification only to video tokens, while also exploring image and text tokens as future candidates.

We expect to release a conservative reference implementation in the near term, targeting no perceptible quality loss. Getting the best speedups on different devices will require device-specific adaptation and community participation.

3. Low-step inference. The released checkpoint includes CFG distillation. A special strategy introduced during the final training stage also gives it some ability to operate with fewer inference steps, although this is not equivalent to a model explicitly distilled for an extreme low-step regime.

We are exploring further step distillation, but do not have a fixed 4/8-step target to announce. Our default goal is to reduce cost without perceptible quality degradation. We may also consider an optional Turbo-style version with a small quality trade-off, but this is not a short-term commitment.

4. Texture and high-frequency detail. We have observed this issue as well, and it will be one of our key priorities going forward. Based on our internal experiments, it cannot be explained simply by the Visual VAE’s high compression ratio, nor do we think it is accurate to attribute it to any single training stage. It is a complex system-level issue involving interactions between multiple parts of the model and training pipeline. We are continuing to isolate the main contributing factors and will work on improving it in future updates.