Upsample Guidance: Scale Up Diffusion Models without Training
Juno Hwang Yong-Hyun Park Junghyo Jo
Diffusion models have demonstrated superior performance across various
generative tasks including images, videos, and audio. However, they encounter
difficulties in directly generating high-resolution samples. Previously
proposed solutions to this issue involve modifying the architecture, further
training, or partitioning the sampling process into multiple stages. These
methods have the limitation of not being able to directly utilize pre-trained
models as-is, requiring additional work. In this paper, we introduce upsample
guidance, a technique that adapts pretrained diffusion model (e.g., $512^2$) to
generate higher-resolution images (e.g., $1536^2$) by adding only a single term
in the sampling process. Remarkably, this technique does not necessitate any
additional training or relying on external models. We demonstrate that upsample
guidance can be applied to various models, such as pixel-space, latent space,
and video diffusion models. We also observed that the proper selection of
guidance scale can improve image quality, fidelity, and prompt alignment.