Hugging Face / NVIDIA | July 17, 2026
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 Hugging Face 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. 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, NVIDIA has released NeMo Automodel, now integrated with Hugging Face's Diffusers. The joint post from NVIDIA and Hugging Face details how the integration enables fine-tuning of video and image models at scale, offering utilities like memory-efficient sharding and multi-GPU support. This represents a significant step forward in making diffusion model customization more accessible to researchers and developers working with open-source generative AI.