Fine-tune video and image models at scale with NVIDIA NeMo Automodel and 🤗 Diffusers Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Back to Articles a]:hidden"> Fine-tune video and image models at scale with NVIDIA NeMo Automodel and 🤗 Diffusers Enterprise + Article Published July 17, 2026 Upvote 6 Pranav Prashant Thombre pthombre Follow nvidia linnan wang linnanwang Follow nvidia Alexandros Koumparoulis akoumpa Follow nvidia Wenwen Gao wgao2021 Follow nvidia Sylendran Arunagiri Sylendran95 Follow nvidia Bernard Nguyen bernardwin Follow nvidia Table of contents What is NeMo Automodel? Supported diffusion models What this collaboration unlocks A look at the fine-tuning workflow 1. Pre-encode the dataset 2. Launch training with the existing FLUX YAML 3. Generate from the fine-tuned checkpoint 4. Performance Other Finetuned/LoRA examples Try it today Coming next: Pythonic recipe APIs 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 for text-to-image and Wan 2.1 and HunyuanVideo for text-to-video. The 🤗 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.
Source: https://huggingface.co/blog/nvidia/scale-diffusers-finetuning-nemo-automodel
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 for text-to-image and Wan 2.1 and HunyuanVideo for text-to-video. The 🤗 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.
Source: https://huggingface.co/blog/nvidia/scale-diffusers-finetuning-nemo-automodel