ComfyUI 支持 MiniMax H3 Day-0:开放权重、原生音频及 2K 视频
MiniMax H3 Day-0 Support in ComfyUI: Open Weights, Native Audio, and 2K Video

原始链接: https://blog.comfy.org/p/minimax-h3-day-0-support-in-comfyui

MiniMax 发布了其第三代视频模型 **H3**,现已开放权重并支持在 ComfyUI 中原生运行。 H3 是一款强大的多模态模型,能够生成长达 15 秒、分辨率为 2K 并自带立体声的视频片段。它在跨模态任务方面表现出色,允许用户结合文本、图像、视频和音频来控制主体、动作和风格。主要特点包括: * **多功能输入:** 支持文生视频、图生视频、首尾帧控制以及基于参考的动作迁移。 * **集成音频:** 音频与视频同步生成,而非后期添加。 * **性能优化:** 通过权重剪枝、量化和自定义内核,内存占用减少了 66%。这使得模型能够在消费级硬件(如 RTX 3060)上运行,仅需 42.5 GB 显存。 如需开始使用,请将 ComfyUI 更新至 0.30.0 版本,从 Hugging Face 下载 H3 权重,并使用提供的工作流进行创作。此次发布标志着高保真 AI 视频生产在易用性方面迈出了重要一步。

MiniMax H3 在 ComfyUI 的发布是开源视频生成领域的一个重要里程碑,它使用户能够在本地硬件上实现高质量、原生音频集成的 2K 视频制作。 关键技术讨论包括: * **优化:** 用户正在探索节省内存的技术,例如用查找表(Lookup Tables)代替调制权重——这是一种针对扩散模型的无损方法——以减少内存占用。 * **性能:** 虽然生成速度因硬件而异(据报道从几秒到几分钟不等),但诸如 SageAttention 和 EasyCache 等社区优化方案已经提高了吞吐量。 * **效用与伦理:** 社区对其影响看法不一。爱好者们称赞其在快速原型设计、分镜制作以及实现高端电影制作民主化方面的潜力,但批评者认为其美学风格仍然“平淡”或“同质化”,并对版权和创意劳动力被替代表示担忧。 * **未来展望:** 尽管存在提示词遵循能力和潜在监管挑战等技术障碍,但此次发布被视为一个主要的竞争对手,对闭源基础模型形成了价格压力。用户强调,随着技术的发展,在人工智能生成内容日益饱和的环境下,人类的“审美”、创意编排和原创愿景依然至关重要。
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原文


MiniMax H3 dropped today with open weights, and it’s natively supported in ComfyUI as of this morning. Day zero.

This is a next-generation open-weights video model. Feed it text, images, video, or audio and it generates video with real stereo sound, up to 2K, up to 15 seconds a clip. It is MiniMax’s third-generation video model, following Hailuo 01 and Hailuo 02, and the first the company has released with open weights.

Try on Comfy Cloud

  • Text-to-video — prompt only.

  • Image-to-video — bring an image to life.

  • First-and-last-frame — control the opening frame, the closing frame, or both, and let the model fill in the rest.

  • Reference-to-video — supply reference images, video, or audio and carry a subject, a motion, or a voice through the clip.

Output runs to 2K and up to 15 seconds. Audio is generated with the video in the same pass, in stereo, not bolted on afterward.

This is the capability MiniMax leads with, and it’s what collapses five separate tasks into one model. Real work rarely draws on one modality. H3 takes images, audio, and video together and resolves them against a prompt that explains how they relate. Describe the relationship between your inputs and the shot you want, and the model handles the cross-modal work itself.

Audio is a property of the model, not a post-process. Every audio output is native stereo.

Motion transfer is the one that matters most for graph work. A reference video can supply movement — a camera move, a performance, a cutting rhythm — while the subject and style come from elsewhere. Combined with in-place editing, that means iterating on a shot.

Getting H3 to run well on consumer hardware took significant machine learning engineering. We found that the model's modulation weights (~40% of the total parameters) could be pruned and replaced with a functionally equivalent lookup table, dramatically shrinking the memory footprint with no loss in output quality.

On top of that, the weights ship with an accurate and efficient int8 convrot quantization, and custom kernels reduce the peak VRAM use during inference.

The result gives a total memory footprint reduced by 66%, from 123.6 GB in full precision to 42.5 GB with the smallest models variants. Combining this with our dynamic VRAM offloading enables a next-generation 2K video model to run locally on a GPU like the RTX 3060.

  1. Update ComfyUI to the latest version 0.30.0 or go to Comfy Cloud

  2. Download the workflows below, or find them in the template library.

    Download MiniMax H3 I2V Workflow

    Download MiniMax H3 R2V Workflow

    Download MiniMax H3 T2V Workflow

  3. Follow the note in the workflow to download the models and save them in the correct model directory.

  4. Write your prompt, connect any frame or reference inputs, and run.

Model weights: 🤗 Comfy-Org/MiniMax-H3

As always, enjoy creating!

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