Open Source · Nvidia · Hugging Face
Fine-tune video and image models at scale with NVIDIA NeMo Automodel and Diffusers
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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.
Key facts
- The run produces checkpoints at steps 50, 100, 150, and 200
- This guide walks through a full-transformer fine-tune of FLUX.1-dev on the 78-card Rider–Waite tarot dataset, then generating from the resulting checkpoint
- 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 recommended way to install Automodel is the NeMo Automodel Docker container ( nvcr
Summary
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, they offer the NVIDIA NeMo Automodel open-source library. A look at the fine-tuning workflow 1. Launch training with the existing FLUX YAML 3. 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:.