Fine-tune video and image models at scale with NVIDIA NeMo Automodel and 🤗 Diffusers

NVIDIA and Hugging Face have collaborated to enhance the training and fine-tuning of diffusion models using the NVIDIA NeMo Automodel and 🤗 Diffusers Enterprise. This integration allows for scalable, distributed training of diffusion models without the need for checkpoint conversion or model rewrites. The NeMo Automodel library, part of NVIDIA's NeMo framework, is designed to work seamlessly with the Diffusers ecosystem, supporting a variety of parallelism configurations for efficient model training at any scale. It offers out-of-the-box fine-tuning recipes for popular models like FLUX and Wan, with capabilities like memory-efficient sharding and multiresolution bucketing. The integration is fully open-source and documented, enabling users to perform both full fine-tuning and parameter-efficient LoRA-style tuning, catering to different quality and efficiency needs. Future updates plan to introduce a Pythonic API to complement the existing YAML-based configuration system, enhancing usability for teams with programmatic needs.

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