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This study examines the impact of optimizing the Stable Diffusion (SD) guidedinference pipeline. We propose optimizing certain denoising steps by limitingthe noise computation to conditional noise and eliminating unconditional noisecomputation, thereby reducing the complexity of the target iterations by 50%.Additionally, we demonstrate that later iterations of the SD are less sensitiveto optimization, making them ideal candidates for applying the suggestedoptimization. Our experiments show that optimizing the last 20% of thedenoising loop iterations results in an 8.2% reduction in inference time withalmost no perceivable changes to the human eye. Furthermore, we found that byextending the optimization to 50% of the last iterations, we can reduceinference time by approximately 20.3%, while still generating visually pleasingimages.

Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important?