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MiniMax H3 生态新增 11 项社区贡献:8 步参考条件视频+音频、4 步蒸馏与多硬件适配

More reference control. Fewer denoising steps. More places to run H3. The latest community contributions span 8-step reference-conditioned video + audio, 4-step distillation, sparse attention, and reference-token compression - alongside serving and workflow integrations across GPU servers, TPUs, and Apple Silicon. What matters is what this enables: more affordable experimentation, finer creative control, and more choices about where and how video generation runs. Open models become more useful when the community can improve the methods, adapt the runtime, and build workflows around them. Credit to @haoailab @yisun0618 @lanfeng491270 @guocheng_qian @Alissonerdx @ddalcu @tazmannner @HaochengXiUCB @huggingface @Jhuaplin, and the PTD, DMAD, SparkH3, TorchTPU and FlashML teams - including Zhengming Yu and zechengtang on GitHub. 11 additions to the H3 ecosystem index. Thank you to everyone building, testing, and sharing. https://t.co/F1S5IjUNi1

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MiniMax H3 生态新增 11 项社区贡献,涵盖 8 步参考条件视频+音频生成、4 步蒸馏、稀疏注意力和 reference-token 压缩。这些改进同时带来 GPU 服务器、TPU 和 Apple Silicon 上的服务与工作流集成,让视频生成实验成本更低、创作控制更精细、运行环境选择更多。

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