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MiniMax H3 Full-Speed Cache

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05_minimax-h3-full-speed-cache.json

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Workflows
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Published

Aug 10, 2026

Base Model

MiniMax H3

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AutoV2
A8D596DC13
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05_minimax-h3-full-speed-cache.png
https://youtu.be/SUJkkT6XLHI

This workflow is the full-speed MiniMax H3 acceleration version built around Larry Turbo, attention optimization, and cache-assisted execution. It is the more aggressive performance-oriented option in this batch.

The connected graph keeps MiniMax H3 video generation as the core and adds the acceleration structure needed for the full-speed route. It is intended for users who already understand the normal MiniMax H3 workflow and now want to push iteration speed further.

This package is meant for practical ComfyUI and RunningHub users who want a ready-made MiniMax H3 graph instead of rebuilding loader, encoder, VAE, sampler, and video export connections by hand. The workflow is especially useful for speed comparison, hardware planning, and repeatable prompt tests. Keep the same source image and prompt when comparing versions so the difference you see comes from the workflow route, not from changing creative variables.

Main features:

- Full-speed MiniMax H3 acceleration workflow
- Larry Turbo, attention optimization, and cache-oriented execution design
- Built for faster repeated image-to-video testing
- Keeps the MiniMax H3 model, encoder, VAE, sampler, and video output structure together
- Useful for comparing maximum-speed output against safer baseline routes
- Good match for hardware testing and practical production timing checks
- Cover and workflow are matched to the full-speed cache version

Suggested workflow:

Use this version after you have already validated the prompt on a normal or lower-risk acceleration path. Keep a known good source image and prompt, then test how much quality you are willing to trade for speed. For production work, save a baseline output first so you can compare temporal stability and subject identity objectively.

For better comparisons, make one small test first, review identity stability and temporal motion, then raise prompt complexity only after the base route behaves correctly. When using speaking-character prompts, keep the face visible, avoid too many fast cuts, and separate action, expression, and camera instructions clearly.

RunningHub Workflow

Try the workflow online right now - no installation required.
Workflow: https://www.runninghub.ai/post/2086705735132983297?inviteCode=rh-v1111

If the results meet your expectations, you can later deploy it locally for customization.

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Bilibili Updates (Mainland China & Asia-Pacific)

If you're in the Asia-Pacific region, you can watch the video below to see the workflow demonstration and creative breakdown.
Bilibili Video: https://www.bilibili.com/video/BV17nuU6xE3g/

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打开下方链接即可在线体验,无需安装。
工作流:https://www.runninghub.ai/post/2086705735132983297?inviteCode=rh-v1111

如果你觉得效果理想,也可以在本地进行自定义部署。

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B站视频(中国大陆及亚太地区)

如果你在中国大陆或亚太地区,可以通过下面的视频查看工作流的实测效果与创作思路。
B站视频:https://www.bilibili.com/video/BV17nuU6xE3g/

我会在夸克网盘持续更新模型资源:
https://pan.quark.cn/s/07bdc81784ce