Back to Articles
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.
To cater to these technical demands, we offer the 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 and is fully open source under Apache 2.0.
