Key takeaways
- A joint post from NVIDIA and Hugging Face.
- NeMo Automodel integration ships with ready-to-use fine-tuning recipes for the open diffusion models below.
- Your fine-tuned checkpoint loads directly into a DiffusionPipeline for inference, or back to the Hub for sharing.
What happened
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. 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.
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.
Why it matters
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. 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.
What to watch
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.
1-dev (12B) and HunyuanVideo (13B) possible. In this section, we walk through the typical workflow for fine-tuning any of the supported models. 06), which ships with PyTorch, TransformerEngine, and other CUDA-compiled dependencies pre-built. git); see the installation guide for all options. 1-dev on the 78-card Rider–Waite tarot dataset, then generating from the resulting checkpoint. It reuses the checked-in YAML configs and ap