Summary

In this chapter, we have introduced six techniques to enhance the performance of Stable Diffusion and minimize VRAM usage. The amount of VRAM is often the most significant hurdle in running a Stable Diffusion model, with CUDA Out of memory being a common issue. The techniques we have discussed can drastically reduce VRAM usage while maintaining the same inference speed.

Enabling the float16 data type can halve VRAM usage and nearly double the inference speed. VAE tiling allows the generation of large images without excessive VRAM usage. Xformers can further decrease VRAM usage and increase inference speed by implementing an intelligent two-layer attention mechanism. PyTorch 2.0 provides native features such as Xformers and automatically enables them.

Sequential CPU offload can significantly reduce VRAM usage by offloading a sub-model and its sub-modules to CPU RAM, albeit at the cost of slower inference speed. However, we can use the same concept to implement our sequential...