Fine-tuning and Optimization of Large-scale Diffusion Models via Distributed Training: Stable Diffusion XL Cluster Building Strategy
Fine-tuning large-scale Diffusion models like Stable Diffusion XL (SDXL) to specific domains or styles demands immense computing resources. This article presents a strategy to overcome the limitations of a single GPU by effectively building and optimizing a distributed training cluster, thereby significantly reducing training time and leveraging larger batch sizes and resolutions to achieve high-quality results.
1. The Challenge / Context: Overcoming the Barriers of SDXL Fine-tuning
Stable Diffusion XL boasts unparalleled image quality and generation capabilities compared to its predecessors. This is due to the increase in text encoders (OpenCLIP ViT/G and Google T5-XXL), a much larger UNet model, and a complex architecture for high-resolution image generation. Consequently, the GPU memory and computing power required to fine-tune SDXL have increased exponentially.
- Memory Requirements: At 1024x1024 resolution, the SDXL UNet alone occupies tens of GBs of GPU memory. When Optimizer States, gradients, and Activations are added, it becomes difficult to secure a sufficient batch size even with a single A100 80GB GPU. Notably, the AdamW optimizer requires memory equivalent to 12 times the number of model parameters based on FP32.
- Computational Complexity: Larger models and higher resolutions significantly
