  Diffusion models achieved great success in image synthesis, but still face
challenges in high-resolution generation. Through the lens of discrete cosine
transformation, we find the main reason is that \emph{the same noise level on a
higher resolution results in a higher Signal-to-Noise Ratio in the frequency
domain}. In this work, we present Relay Diffusion Model (RDM), which transfers
a low-resolution image or noise into an equivalent high-resolution one for
diffusion model via blurring diffusion and block noise. Therefore, the
diffusion process can continue seamlessly in any new resolution or model
without restarting from pure noise or low-resolution conditioning. RDM achieves
state-of-the-art FID on CelebA-HQ and sFID on ImageNet 256$\times$256,
surpassing previous works such as ADM, LDM and DiT by a large margin. All the
codes and checkpoints are open-sourced at
\url{https://github.com/THUDM/RelayDiffusion}.
