Hugging Face has developed a method to accelerate inference for the SD Turbo and SDXL Turbo models using ONNX Runtime and Olive. The technique leverages model parallelism, dynamic quantization, and mixed-precision training to improve performance. According to the authors, this approach can achieve significant speedups over traditional methods. However, no specific numbers or benchmarks are provided in the article. --- Why it matters: This matters because it could enable faster and more efficient deployment of large language models like SD Turbo and SDXL Turbo in real-world applications. Improved inference performance is crucial for AI developers who need to balance model complexity with computational resources. Source: https://huggingface.co/blog/sdxl_ort_inference