AsyncDiff is a method for accelerating diffusion models through asynchronous denoising parallelization. It divides the noise prediction model into multiple components and distributes them across different devices, enabling parallel processing. This approach significantly reduces inference latency while having a minimal impact on generation quality. AsyncDiff supports a variety of diffusion models, including Stable Diffusion 2.1, Stable Diffusion 1.5, Stable Diffusion x4 Upscaler, Stable Diffusion XL 1.0, ControlNet, Stable Video Diffusion, and AnimateDiff.