AI BriefingSDXL Inference Speed and Memory Optimization Guide
·2023.10.24 09:00
Key point
It introduces how to apply fp16 and use memory-efficient attention to speed up inference and reduce memory usage for the SDXL model.
Details
Stable Diffusion XL (SDXL) excels at high-quality image generation, but as a massive model with 3.5 billion (3.5B) parameters, it suffers from high memory usage and slow inference speed. When run with default settings (fp32), it requires 28GB of memory and takes 72.2 seconds of latency.
The main methods to optimize this are as follows:
- Using Lower Precision: Applying
torch.float16(fp16) can reduce memory usage to 21.7GB and shorten inference time to as little as 14.8 seconds. - Memory-efficient attention: To reduce the memory load of attention operations, PyTorch 2.0's SDPA (Scaled Dot Product Attention) is utilized. This provides optimization features similar to Flash Attention and xFormers, and is automatically enabled when using the
diffuserslibrary.
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