RunningHubAI/rh-minimax-h3-turbo-4steps-lora
AI model by RunningHubAI
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[](READMEcn.md) [](https://www.runninghub.ai/call-api?utmsource=huggingface&utmmedium=badge&utmcampaign=apipromotion&utmcontent=rh-2092791011117453313) [](https://www.runninghub.ai?utmsource=huggingface&utmmedium=badge&utmcampaign=modelupload&utmcontent=rh-2092791011117453313) [](https://www.runninghub.cn?utmsource=huggingface&utmmedium=badge&utmcampaign=modelupload&utmcontent=rh-2092791011117453313) 核心优势显存占用更低：相比未剪枝的原版Turbo LoRA，内存占用降低约40%，8GB显存的消费级显卡即可流畅运行。生成效率更高：在保持基础画质的前提下，将采样步数压缩至4步，生成速度相比原版20步模型提升3倍以上。音画同步支持：支持直接生成32kHz高保真立体声音频，无需后期额外合成音频，避免音画错位问题。部署使用要点放置目录：需将该文件放入ComfyUI的 models/loras/ 目录下，重启ComfyUI后即可在LoRA加载节点中识别选中。配套依赖：需搭配MiniMax H3 pruned剪枝版基础扩散模型、对应视频VAE、音频VAE共同使用。参数推荐：LoRA强度建议设置在0.8-1.2区间，采样器选择resmultistep，采样步数固定为4即可发挥最佳加速效果。 This repository provides rh-minimax-h3-turbo-4steps-lora weight files. You can load them on RunningHub. Finetuned from: minimax-h3 Published by RunningHub on behalf of the author. Copyright…
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Model overview
RunningHubAI/rh-minimax-h3-turbo-4steps-lora is a task-unspecified model repository published by RunningHubAI on Hugging Face. A library was not reported.
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Model facts
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- Created
- 2026-10-15T06:41:04.000Z
- Last modified
- 2026-10-15T06:42:05.000Z
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Source: Hugging Face Hub. Metadata observed 2026-10-15T06:42:18.893Z. Daily imports are snapshots, not real-time monitoring.
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