[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-75d2f385-00ef-4074-80bf-ee47ba05a4a4":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"75d2f385-00ef-4074-80bf-ee47ba05a4a4","NVIDIA × Hugging Face 把 Diffusers 微调搬上 H100 集群:Wan 2.2 MoE、FLUX.2、Hunyuan 一次性打通","NVIDIA 与 Hugging Face 7 月 17 日联合发布 NeMo Automodel 与 Diffusers 的深度集成,把扩散模型的训练\u002F微调从「每换一个模型就要写一套脚本」拉进「写一次、跑遍整个 Hub」的状态。\n\n核心改动是把 NeMo Automodel 的训练栈从原本的 LLM\u002FMoE 场景扩展到 flow-matching 扩散模型。底座是 DTensor + PyTorch 原生,所有并行策略——FSDP2、Tensor Parallel、Expert Parallel、Context Parallel、Pipeline Parallel——都通过 YAML 切换,不再需要改模型代码。模型类直接复用 Diffusers 的 WanTransformer3DModel、FLUXPipeline,训练完的 checkpoint 能立刻跑回 DiffusionPipeline 推理,完全没有格式转换的中间步骤。\n\n首批官方 recipe 覆盖了开源扩散圈的主力:FLUX.1-dev(12B)与 FLUX.2-dev(32B)的文生图、Wan 2.1 1.3B\u002F14B 与 Wan 2.2 A14B(MoE)的文生视频、HunyuanVideo 1.5(13B)、Qwen-Image(20B MMDiT),全部同时支持 Full FT 与 LoRA。配合 latent 缓存 + 多分辨率 bucketing,真正做到了「数据集预编码一次,后面全是模型与并行策略的旋钮」。\n\n实测数据来自 8×H100 80GB 集群:FLUX.1-dev 全量微调 35.51 imgs\u002Fs、LoRA r64 53.73 imgs\u002Fs;Wan 2.1 14B 全量 2.107 clips\u002Fs;Wan 2.2 A14B 高噪分支 1.73 clips\u002Fs;FLUX.2-dev 32B 也已被列入路线图。单卡显存峰值多压在 60GiB 以内,意味着大部分条目单机 8 卡就能开训。整个栈 Apache 2.0 开源,Pythonic recipe API 也已在路上,下一步是把 YAML 配置和编程式接口并列起来。\n\n这套工具的真正价值不在性能数字,而是把「微调」从一项需要为每个模型写一堆胶水代码的工程活,变成可配置的研究基础设施。对 LoRA 创作者和企业定制模型都是直接利好——新模型一上 Hub,几行 YAML 就能开训。NVIDIA 在 LLM 与 Diffusion 两端同时拿下「训练框架」位置,等于把生态护城河往上游推了一格。","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fnvidia\u002Fscale-diffusers-finetuning-nemo-automodel","474eef8c-e0c3-46cf-adee-c089558220f9",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"8dac812d-3839-4abe-a855-5f56ec9515fd","nvidia",{"id":18,"name":19,"slug":19,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",{"id":21,"name":22,"slug":22,"description":13,"color":13},"ebe5dcd1-46b1-4298-b8c2-8e0e2f456e56","video-generation","2026-07-17T14:00:00Z","2026-07-18T22:11:34.902167Z","2026-07-18T22:11:34.902179Z",true,"agent",7]