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MiniMax H3 SageAttention Workflow

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04_minimax-h3-sageattention-workflow.json

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Type
Workflows
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99

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Published

Aug 10, 2026

Base Model

MiniMax H3

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AutoV2
C01E661CE0
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04_minimax-h3-sageattention-workflow.png
https://youtu.be/SUJkkT6XLHI

This workflow combines a Larry Turbo MiniMax H3 route with a SageAttention acceleration environment. It is designed for users testing whether attention optimization can help MiniMax H3 video generation run more smoothly while keeping the graph easy to operate.

The workflow keeps the familiar MiniMax H3 model, Qwen3-VL encoder, video VAE, audio VAE, scheduler, and SaveVideo structure, while the acceleration focus is the Larry Turbo plus SageAttention setup around the generation path.

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:

- MiniMax H3 acceleration workflow prepared for a SageAttention environment
- Larry Turbo style route for faster video generation tests
- Connected MiniMax H3 model, encoder, and VAE chain
- 16:9 video output orientation for common tutorial and demo formats
- Short-step sampling path for practical iteration
- Clean graph layout for testing speed, stability, and render consistency
- Useful when comparing attention optimization against plain turbo routes

Suggested workflow:

Run the same source image and prompt that you use in the Larry Turbo workflow, then compare speed, output stability, and temporal consistency. Keep the first test strict and repeatable: same resolution, same duration, same prompt, and the same audio or motion request.

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/2086424118061641730?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/2086424118061641730?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