{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-07-23T06:40:50.084Z","headline":"NVIDIA 与 Hugging Face 合作，通过 NeMo Automodel 为 Diffusers 模型提供分布式扩散训练","description":"NVIDIA 与 Hugging Face 合作，通过 NeMo Automodel 开源库为任意 Diffusers 格式模型提供生产级分布式扩散训练，无需检查点转换或模型重写。该集成支持 Wan 2.1、FLUX.1-dev 等模型的完整微调与 LoRA 微调，可在一到数百 GPU 上灵活扩展，所有检查点可直接加载回 Diffusers 管线进行推理。","url":"https://www.aioga.com/news/cmrp6aabj0anfbitojjksn5c4/","mainEntityOfPage":"https://www.aioga.com/news/cmrp6aabj0anfbitojjksn5c4/","datePublished":"2026-07-17T15:57:54.000Z","dateModified":"2026-07-17T15:57:54.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://huggingface.co/blog/nvidia/scale-diffusers-finetuning-nemo-automodel","https://aihot.virxact.com/items/cmrp6aabj0anfbitojjksn5c4"],"canonicalUrl":"https://www.aioga.com/news/cmrp6aabj0anfbitojjksn5c4/","directAnswer":{"@type":"Answer","text":"Aioga 编辑摘要：NVIDIA 与 Hugging Face 合作，通过 NeMo Automodel 开源库为任意 Diffusers 格式模型提供生产级分布式扩散训练，无需检查点转换或模型重写。 Aioga 将其归入「AI资讯」方向，重点关注它对真实使用和行业竞争的影响。","url":"https://www.aioga.com/news/cmrp6aabj0anfbitojjksn5c4/","dateCreated":"2026-07-17T15:57:54.000Z","author":{"@type":"Organization","@id":"https://www.aioga.com/authors/aioga-editorial/#editorial-team","name":"Aioga Editorial Team","url":"https://www.aioga.com/authors/aioga-editorial/"}},"evidence":[{"@type":"CreativeWork","name":"huggingface.co source article","url":"https://huggingface.co/blog/nvidia/scale-diffusers-finetuning-nemo-automodel","datePublished":"2026-07-17T15:57:54.000Z","provider":{"@type":"Organization","name":"huggingface.co","url":"https://huggingface.co/blog/nvidia/scale-diffusers-finetuning-nemo-automodel"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.virxact.com/items/cmrp6aabj0anfbitojjksn5c4","datePublished":"2026-07-17T15:57:54.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.virxact.com/items/cmrp6aabj0anfbitojjksn5c4"}}],"aggregationSource":"Hugging Face：Blog（RSS）","originalPublisher":{"name":"huggingface.co","url":"https://huggingface.co/blog/nvidia/scale-diffusers-finetuning-nemo-automodel"},"article":{"id":"cmrp6aabj0anfbitojjksn5c4","slug":"cmrp6aabj0anfbitojjksn5c4","url":"https://www.aioga.com/news/cmrp6aabj0anfbitojjksn5c4/","title":"NVIDIA 与 Hugging Face 合作，通过 NeMo Automodel 为 Diffusers 模型提供分布式扩散训练","title_en":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and 🤗 Diffusers","summary":"NVIDIA 与 Hugging Face 合作，通过 NeMo Automodel 开源库为任意 Diffusers 格式模型提供生产级分布式扩散训练，无需检查点转换或模型重写。该集成支持 Wan 2.1、FLUX.1-dev 等模型的完整微调与 LoRA 微调，可在一到数百 GPU 上灵活扩展，所有检查点可直接加载回 Diffusers 管线进行推理。","source":"Hugging Face：Blog（RSS）","sourceUrl":"https://huggingface.co/blog/nvidia/scale-diffusers-finetuning-nemo-automodel","aiHotUrl":"https://aihot.virxact.com/items/cmrp6aabj0anfbitojjksn5c4","publishedAt":"2026-07-17T15:57:54.000Z","category":"AI资讯","score":0,"selected":false,"articleBody":["Table of contents ：#table-of-contents What is NeMo Automodel? ：#what-is-nemo-automodel Supported diffusion models ：#supported-diffusion-models What this collaboration unlocks ：#what-this-collaboration-unlocks A look at the fine-tuning workflow ：#a-look-at-the-fine-tuning-workflow 1. Pre-encode the dataset ：#1-pre-encode-the-dataset 2. Launch training with the existing FLUX YAML ：#2-launch-training-with-the-existing-flux-yaml 3. Generate from the fine-tuned checkpoint ：#3-generate-from-the-fine-tuned-checkpoint 4. Performance ：#4-performance Other Finetuned/LoRA examples ：#other-finetunedlora-examples Try it today ：#try-it-today Coming next: Pythonic recipe APIs ：#coming-next-pythonic-recipe-apis Resources ：#resources A joint post from NVIDIA and Hugging Face. Special thanks to Sayak Paul from Hugging Face for their contributions to the integration work and for co-authoring this blog.","Diffusion models power some of the most exciting open-source releases of the last two years — such as FLUX.1-dev：https://huggingface.co/black-forest-labs/FLUX.1-dev for text-to-image and Wan 2.1：https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers and HunyuanVideo：https://huggingface.co/hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v for text-to-video. The 🤗 Diffusers：https://github.com/huggingface/diffusers library has become the de facto home for these models, giving researchers and builders a single, consistent interface for inference, adaptation, and pipeline composition.","In addition, training and fine-tuning diffusion models are also on the rise, requiring utilities that offer memory-efficient sharding, latent caching, multiresolution bucketing, and configurations that scale gracefully from one GPU to hundreds.","To cater to these technical demands, we offer the NVIDIA NeMo Automodel：https://github.com/NVIDIA-NeMo/Automodel open-source library. Today, we're highlighting the collaboration between NVIDIA and Hugging Face that brings production-grade, distributed diffusion training to any Diffusers-format model on the Hugging Face Hub — with no checkpoint conversion and no model rewrites for any new model. The integration is documented in the Diffusers training guide：https://docs.nvidia.com/nemo/automodel/recipes-e2e-examples/diffusion-fine-tuning and is fully open source under Apache 2.0.","NeMo Automodel is an open-source PyTorch DTensor-native training library, part of the NVIDIA NeMo framework, built around two design principles that matter for the Diffusers ecosystem:","AutoModel currently supports flow-matching models only. Under the hood, it uses flow matching as the training objective, with latent-space training (via pre-encoded VAE outputs) and multiresolution bucketed dataloading to accelerate throughput.","NeMo Automodel integration ships with ready-to-use fine-tuning recipes for the open diffusion models below. The list reflects the recipes currently in examples/diffusion/finetune .","For Diffusers users, the practical gains break down into a few concrete capabilities.","No checkpoint conversion. Pretrained weights from the Hub work out of the box. There's no separate \"training format\" to convert to, then convert back. Your fine-tuned checkpoint loads directly into a DiffusionPipeline for inference, or back to the Hub for sharing. Downstream tools — quantization, compilation, LoRA adapters, custom samplers — all keep working.","Fast path to new model support. When a new diffusion model lands in Diffusers, enabling it in NeMo Automodel takes a small, contained code addition — a data preprocessing handler and a model adapter — rather than a full custom training script. The rest of the recipe stack (FSDP2, bucketed dataloading, checkpointing, generation) carries over unchanged, and the same YAML-driven workflow applies.","Full and parameter-efficient fine-tuning. Both full fine-tuning and LoRA-style PEFT are supported, so you can choose between maximum quality (full FT on a large cluster) or maximum efficiency (LoRA on a single node). The same recipe structure handles both.","Scalable training that goes beyond what built-in scripts offer. NeMo Automodel adds sharding schemes such as FSDP2, tensor, context, and pipeline parallelisms, multi-node orchestration (SLURM today, Kubernetes coming), and multiresolution bucketing. These capabilities make training larger models like FLUX.1-dev (12B) and HunyuanVideo (13B) possible.","In this section, we walk through the typical workflow for fine-tuning any of the supported models. The recommended way to install Automodel is the NeMo Automodel Docker container ( nvcr.io/nvidia/nemo-automodel:26.06 ), which ships with PyTorch, TransformerEngine, and other CUDA-compiled dependencies pre-built. Alternatively, install with pip3 install nemo-automodel or from source ( pip3 install git+https://github.com/NVIDIA-NeMo/Automodel.git ); see the installation guide：https://docs.nvidia.com/nemo/automodel/latest/get-started/installation for all options.","This guide walks through a full-transformer fine-tune of FLUX.1-dev on the 78-card Rider–Waite tarot dataset：https://huggingface.co/datasets/multimodalart/1920-raider-waite-tarot-public-domain, then generating from the resulting checkpoint. It reuses the checked-in YAML configs and applies run-specific settings as command-line overrides, so no new config files are required.","The diffusion recipe consumes cached VAE latents and text embeddings instead of encoding source images during every training step. Stream the 78 Rider–Waite images directly from Hugging Face and distribute preprocessing across all visible GPUs:","The captions already contain the trtcrd trigger token. With this pixel budget and the dataset's portrait aspect ratio, preprocessing assigns the samples to the 384×640 bucket used by the showcase run.","For image training, preprocessing produces .pt cache files and sharded metadata:","Use examples/diffusion/finetune/flux_t2i_flow.yaml directly. The YAML already selects FLUX.1-dev, full transformer fine-tuning, the FLUX flow-matching adapter, an effective batch size of 32, and eight-way FSDP2.","Supply the tarot-specific paths and settings as command-line overrides:","The run produces checkpoints at steps 50, 100, 150, and 200. The final checkpoint is labeled epoch_66_step_199 ; the label is zero-based even though it represents the completed 200th optimizer step.","Use the existing FLUX generation YAML and point model.checkpoint at the complete training checkpoint:","Include trtcrd to invoke the learned tarot style. For a control comparison, keep the seed and scene fixed but omit the trigger:","At step 200, the triggered astronaut prompts retain their requested content while acquiring a cream, red, and black vintage palette, heavy ink contours, flat color fields, aged-paper tones, and allegorical card composition. The untriggered astronaut remains photographic, demonstrating that the learned effect is substantially associated with trtcrd rather than replacing the base model globally.","All measurements were collected on one node with 8× NVIDIA H100 80GB GPUs. Results are means ± sample standard deviation over three steady-state 10-step windows.","Each sample is one 49-frame video clip.","The results from fine-tuning and LoRA showcase the power of NeMo Automodel for domain specialization. For instance, fine-tuning the Wan 2.1 model on a Ghibli video dataset successfully adapted the output style, demonstrated by a noticeable change in a flower's appearance compared to the baseline.","We also observed the distinct impact of using LoRA, where applying the adapter to Wan 2.1 caused the video to adopt a characteristic Ghibli style, particularly visible in the highlighting of characters' eyes.","These examples, including those for FLUX.2, confirm that users can achieve both maximum quality via full fine-tuning and maximum efficiency via LoRA-style PEFT, tailoring the output to specific stylistic domains.","Learn more about the integration and find more fine-tuning examples in the NeMo Automodel documentation：https://docs.nvidia.com/nemo/automodel/recipes-e2e-examples/diffusion-fine-tuning","YAML is a strong fit for reproducible configuration, especially for teams that want files they can check in, review, and reuse, but many teams also need a programmatic interface.","In an upcoming NeMo Automodel release, we plan to surface the diffusion recipes through a fully typed Pythonic API as well. Users will be able to compose the same model, data, optimizer, PEFT/LoRA, parallelism, checkpointing, and generation pieces directly from Python.","The Pythonic path is intended to make the recipes easier to use from existing training code, notebooks, and experiment workflows, and to offer a first-class Pythonic interface alongside the YAML quick-start path."],"articleImages":[{"sourceUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/5f5028520bcb0e71543cec2f/bLJPy5mQs79cSneARU4Rg.png","alt":"","afterParagraph":0,"url":"/media/articles/cmrp6aabj0anfbitojjksn5c4/bd7677341349dc0e.webp"},{"sourceUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/603bdba23249b99991dbcbc4/cxCnN1H-RXOhojHY3Wcxo.jpeg","alt":"","afterParagraph":0,"url":"/media/articles/cmrp6aabj0anfbitojjksn5c4/5da245c993c3cf23.webp"}],"mediaStatus":"ok","articleBodyZh":["目录：#table-of-contents 什么是 NeMo Automodel？：#what-is-nemo-automodel 支持的扩散模型：#supported-diffusion-models 此次合作解锁的内容：#what-this-collaboration-unlocks 微调工作流程概览：#a-look-at-the-fine-tuning-workflow 1. 预编码数据集：#1-pre-encode-the-dataset 2. 使用现有 FLUX YAML 启动训练：#2-launch-training-with-the-existing-flux-yaml 3. 从微调的检查点生成：#3-generate-from-the-fine-tuned-checkpoint 4. 性能：#4-performance 其他微调/LoRA 示例：#other-finetunedlora-examples 今天就试试：#try-it-today 即将推出：Python 风格的配方 API：#coming-next-pythonic-recipe-apis 资源：#resources NVIDIA 和 Hugging Face 的联合发布。特别感谢 Hugging Face 的 Sayak Paul 对集成工作的贡献，以及共同撰写此博客。","扩散模型驱动了过去两年一些最令人兴奋的开源发布，例如用于文本到图像的 FLUX.1-dev：https://huggingface.co/black-forest-labs/FLUX.1-dev 以及 Wan 2.1：https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers 和 HunyuanVideo：https://huggingface.co/hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v 用于文本到视频。🤗 Diffusers：https://github.com/huggingface/diffusers 库已经成为这些模型的事实标准归属，为研究人员和开发者提供一个单一、一致的接口用于推理、适配和流水线组合。","此外，扩散模型的训练和微调也在不断增加，这需要提供高效内存分片、潜变量缓存、多分辨率分桶以及能够从单 GPU 平滑扩展到数百 GPU 的配置的工具。","为了满足这些技术需求，我们提供了 NVIDIA NeMo Automodel：https://github.com/NVIDIA-NeMo/Automodel 开源库。今天，我们重点介绍 NVIDIA 和 Hugging Face 之间的合作，将生产级分布式扩散训练带到 Hugging Face Hub 上的任何 Diffusers 格式模型——无需检查点转换，无需为任何新模型重写代码。此集成记录在 Diffusers 训练指南中：https://docs.nvidia.com/nemo/automodel/recipes-e2e-examples/diffusion-fine-tuning，并且完全开源，遵循 Apache 2.0。","NeMo Automodel 是一个开源的 PyTorch DTensor 原生训练库，是 NVIDIA NeMo 框架的一部分，其设计围绕着对 Diffusers 生态系统重要的两个原则：","AutoModel 目前仅支持流匹配模型。在底层，它使用流匹配作为训练目标，通过潜在空间训练（使用预编码的 VAE 输出）和多分辨率桶式数据加载来加速吞吐量。","NeMo Automodel 集成提供了现成的微调方案，适用于以下开源扩散模型。该列表反映了当前位于 examples/diffusion/finetune 中的方案。","对于 Diffusers 用户来说，实际收益可以分解为几个具体能力。","无需检查点转换。来自 Hub 的预训练权重开箱即用。无需进行单独的“训练格式”转换再转换回去。你微调后的检查点可以直接加载到 DiffusionPipeline 进行推理，或者上传回 Hub 分享。下游工具——量化、编译、LoRA 适配器、自定义采样器——都可以继续使用。","快速支持新模型。当一个新的扩散模型加入 Diffusers 时，在 NeMo Automodel 中启用它只需少量、独立的代码添加——一个数据预处理处理器和一个模型适配器——而不需要完整的自定义训练脚本。其余的方案堆栈（FSDP2、桶式数据加载、检查点保存、生成）保持不变，并且同样适用 YAML 驱动的工作流程。","完整且参数高效的微调。支持完整微调和 LoRA 风格的 PEFT，因此你可以在最大质量（在大型集群上进行完整 FT）或最大效率（在单节点上进行 LoRA）之间进行选择。同样的方案结构可以处理两者。","可扩展训练，超越内置脚本提供的能力。NeMo Automodel 添加了如 FSDP2、张量、上下文和流水线并行的分片方案，多节点编排（当前为 SLURM，未来支持 Kubernetes）以及多分辨率分桶。这些能力使得训练更大模型如 FLUX.1-dev（12B）和 HunyuanVideo（13B）成为可能。","在本节中，我们将演示对任何支持的模型进行微调的典型工作流程。推荐安装 Automodel 的方式是使用 NeMo Automodel Docker 容器（nvcr.io/nvidia/nemo-automodel:26.06），该容器预装了 PyTorch、TransformerEngine 以及其他 CUDA 编译的依赖项。或者，也可以通过 pip3 安装：pip3 install nemo-automodel，或从源码安装：pip3 install git+https://github.com/NVIDIA-NeMo/Automodel.git；所有安装选项请参见安装指南：https://docs.nvidia.com/nemo/automodel/latest/get-started/installation。","本指南演示了在 78 张 Rider–Waite 塔罗牌数据集（https://huggingface.co/datasets/multimodalart/1920-raider-waite-tarot-public-domain）上对 FLUX.1-dev 进行完整 Transformer 微调，然后从生成的检查点进行生成。它重复使用已提交的 YAML 配置，并将运行特定设置作为命令行覆盖应用，因此无需新的配置文件。","扩散训练方案在每次训练步骤中使用缓存的 VAE 潜变量和文本嵌入，而不是重新编码源图像。直接从 Hugging Face 流式获取 78 张 Rider–Waite 图像，并将预处理分配到所有可见 GPU 上：","标题中已经包含 trtcrd 触发词令牌。基于此像素预算和数据集的人像纵横比，预处理将样本分配到展示运行使用的 384×640 桶中。","对于图像训练，预处理会生成 .pt 缓存文件和分片元数据：","直接使用 examples/diffusion/finetune/flux_t2i_flow.yaml。该 YAML 已选择 FLUX.1-dev、完整 Transformer 微调、FLUX 流匹配适配器，有效批量大小为 32，并使用八路 FSDP2。","将塔罗牌特定的路径和设置作为命令行覆盖提供：","运行会在步骤 50、100、150 和 200 生成检查点。最终检查点标记为 epoch_66_step_199；该标签为零起始，即使它表示已完成的第 200 个优化器步骤。","使用现有的 FLUX 生成 YAML，并将 model.checkpoint 指向完整训练检查点：","包含 trtcrd 以调用学习到的塔罗牌风格。作为对照比较，保持种子和场景固定，但省略触发词令牌：","在步骤200中，被触发的宇航员提示保持其要求的内容，同时获得奶油色、红色和黑色的复古调色板、厚重的墨线轮廓、平面色域、旧纸张色调以及寓言卡片构图。未触发的宇航员保持摄影风格，这表明所学效果主要与trtcrd相关，而不是全局替换基础模型。","所有测量均在一台配置8× NVIDIA H100 80GB GPU的节点上收集。结果为三个稳态10步窗口的平均值 ± 样本标准差。","每个样本为一个49帧的视频剪辑。","微调和LoRA的结果展示了NeMo Automodel在领域专属化方面的强大能力。例如，将Wan 2.1模型在吉卜力视频数据集上进行微调，成功地调整了输出风格，通过与基线相比花朵外观的明显变化得到验证。","我们还观察到了使用LoRA的独特影响，将适配器应用到Wan 2.1后，视频呈现出典型的吉卜力风格，尤其在角色眼睛的高光表现上非常明显。","这些案例，包括FLUX.2的示例，确认用户可以通过完全微调实现最高质量，也可以通过LoRA风格的PEFT实现最高效率，从而将输出定制到特定的风格领域。","了解更多集成信息并查看更多微调示例，请参阅NeMo Automodel文档：https://docs.nvidia.com/nemo/automodel/recipes-e2e-examples/diffusion-fine-tuning","YAML非常适合可复现的配置，尤其适合希望拥有可提交、可审阅、可重复使用文件的团队，但许多团队也需要编程接口。","在即将发布的NeMo Automodel版本中，我们计划通过完全类型化的Python API展示扩散配方。用户将能够直接通过Python组合相同的模型、数据、优化器、PEFT/LoRA、并行性、检查点和生成组件。","Python化路径旨在使配方更容易从现有训练代码、笔记本和实验工作流中使用，并提供与YAML快速入门路径并行的优质Python接口。"],"translationStatus":"translated","bodyOrigin":"source-page","editorial":{"summary":"Aioga 编辑摘要：NVIDIA 与 Hugging Face 合作，通过 NeMo Automodel 开源库为任意 Diffusers 格式模型提供生产级分布式扩散训练，无需检查点转换或模型重写。 Aioga 将其归入「AI资讯」方向，重点关注它对真实使用和行业竞争的影响。","background":"背景分析：模型与研究类动态需要结合能力边界、开放方式、成本、可用性和真实任务表现判断，单项指标领先不等于已经形成稳定采用。","viewpoint":"Aioga 判断：这条动态更适合作为行业观察信号，当前信息足以建立线索，但不足以推导长期结论。","implications":"影响分析：对相关团队而言，短期应先核对来源、可用范围和实际成本，再判断是否值得接入或跟进。","nextStep":"后续观察：继续观察官方文档、实际可用性、价格变化、开发者反馈和竞品回应。","evidenceRefs":["title","summary","articleBody"],"confidence":"medium","status":"published","aiGenerated":false,"autoApproved":true,"generatedBy":"rule-safe-fallback","generatedAt":"2026-07-23T06:49:19.177Z","sourceHash":"8ad1b8596a05b5d2","validation":{"passed":true,"mode":"rule-safe-fallback","checks":["schema","length","source-attribution","no-html"]}},"tags":["AI资讯","Hugging Face：Blog（RSS）"],"translations":{"zh-CN":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Face：Blog（RSS） 发布，Aioga 已同步发布时间、来源和原文入口，并将继续跟踪官方更新与行业反馈。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga AI资讯","description":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Face：Blog（RSS） 发布，Aioga 已同步发布时间、来源和原文入口，并将继续跟踪官方更新与行业反馈。","url":"https://www.aioga.com/news/cmrp6aabj0anfbitojjksn5c4/"},"en":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI News. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Face：Blog（RSS） 发布，Aioga 已同步发布时间、来源和原文入口，并将继续跟踪官方更新与行业反馈。","category":"AI News","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga AI News","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI News. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Face：Blog（RSS） 发","url":"https://www.aioga.com/en/news/cmrp6aabj0anfbitojjksn5c4/"},"ja":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aiogaは「AIニュース」の動きとして、Hugging Face：Blog（RSS） からの更新を追跡しています。Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Face：Blog（RSS） 发布，Aioga 已同步发布时间、来源和原文入口，并将继续跟踪官方更新与行业反馈。","category":"AIニュース","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga AIニュース","description":"Aiogaは「AIニュース」の動きとして、Hugging Face：Blog（RSS） からの更新を追跡しています。Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Face：Blog（RSS） 发布，Aioga 已同","url":"https://www.aioga.com/ja/news/cmrp6aabj0anfbitojjksn5c4/"},"ko":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga는 Hugging Face：Blog（RSS）의 업데이트를 AI 뉴스 흐름으로 추적합니다. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Face：Blog（RSS） 发布，Aioga 已同步发布时间、来源和原文入口，并将继续跟踪官方更新与行业反馈。","category":"AI 뉴스","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga AI 뉴스","description":"Aioga는 Hugging Face：Blog（RSS）의 업데이트를 AI 뉴스 흐름으로 추적합니다. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Face：Blog（RSS） 发布，Aioga 已同步发布","url":"https://www.aioga.com/ko/news/cmrp6aabj0anfbitojjksn5c4/"},"es":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga sigue esta actualización de Hugging Face：Blog（RSS） dentro de Noticias IA. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Face：Blog（RSS） 发布，Aioga 已同步发布时间、来源和原文入口，并将继续跟踪官方更新与行业反馈。","category":"Noticias IA","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga Noticias de IA","description":"Aioga sigue esta actualización de Hugging Face：Blog（RSS） dentro de Noticias IA. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Face","url":"https://www.aioga.com/es/news/cmrp6aabj0anfbitojjksn5c4/"},"fr":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga suit cette mise à jour de Hugging Face：Blog（RSS） dans la catégorie Actu IA. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Face：Blog（RSS） 发布，Aioga 已同步发布时间、来源和原文入口，并将继续跟踪官方更新与行业反馈。","category":"Actu IA","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga Actualités IA","description":"Aioga suit cette mise à jour de Hugging Face：Blog（RSS） dans la catégorie Actu IA. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该动态由 Hugging Fa","url":"https://www.aioga.com/fr/news/cmrp6aabj0anfbitojjksn5c4/"},"de":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga KI-News","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/de/news/cmrp6aabj0anfbitojjksn5c4/"},"pt-BR":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga Notícias de IA","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/pt-BR/news/cmrp6aabj0anfbitojjksn5c4/"},"ru":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga Новости ИИ","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/ru/news/cmrp6aabj0anfbitojjksn5c4/"},"ar":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga أخبار الذكاء الاصطناعي","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/ar/news/cmrp6aabj0anfbitojjksn5c4/"},"hi":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga AI समाचार","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/hi/news/cmrp6aabj0anfbitojjksn5c4/"},"it":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga Notizie IA","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/it/news/cmrp6aabj0anfbitojjksn5c4/"},"nl":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga AI-nieuws","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/nl/news/cmrp6aabj0anfbitojjksn5c4/"},"tr":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga AI Haberleri","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/tr/news/cmrp6aabj0anfbitojjksn5c4/"},"vi":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Tin tức AI Aioga","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/vi/news/cmrp6aabj0anfbitojjksn5c4/"},"id":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Berita AI Aioga","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/id/news/cmrp6aabj0anfbitojjksn5c4/"},"th":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - ข่าว AI Aioga","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/th/news/cmrp6aabj0anfbitojjksn5c4/"},"pl":{"title":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers","summary":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文入口，并将继续跟踪后续更新。来源：Hugging Face：Blog（RSS）。","category":"AI资讯","source":"Hugging Face：Blog（RSS）","aggregationSource":"Hugging Face：Blog（RSS）","pageTitle":"Fine-tune video and image models at scale with NVIDIA NeMo Automodel and  Diffusers - Aioga Wiadomości AI","description":"Aioga tracks this update from Hugging Face：Blog（RSS） under AI资讯. Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers。该来源目前仅提供简短信息，Aioga 已保留发布时间、来源和原文","url":"https://www.aioga.com/pl/news/cmrp6aabj0anfbitojjksn5c4/"}}}}