Optimize SDXL Inference: FP16, SDPA, and torch.compile

AI Impact Summary

Stable Diffusion XL (SDXL) optimization techniques can significantly improve inference speed and reduce memory usage, particularly through lower precision weights (fp16), memory-efficient attention (SDPA), and PyTorch compilation (torch.compile). By utilizing fp16, the model’s memory footprint is reduced by nearly half, and inference time is slashed by over 60%. These optimizations are crucial for deploying SDXL on consumer GPUs with limited resources, enabling faster image generation and reducing the risk of out-of-memory errors.