Fine-tune video and image models at scale with NVIDIA NeMo Automodel and 馃 Diffusers
NeMo Automodel enhances the efficiency of training and inference for diffusion models by enabling parallelism and checkpointing, supporting both full fine-tuning and LoRA-style parameter-efficient fine-tuning. It improves performance metrics such as step times and memory usage, demonstrating enhanced speed and efficiency for models like FLUX. The system offers flexibility and reproducibility, supporting YAML configuration files and a Pythonic API for programmatic use, with examples showing its effectiveness in domain-specific tasks.
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