  Generative models have recently undergone significant advancement due to the
diffusion models. The success of these models can be often attributed to their
use of guidance techniques, such as classifier or classifier-free guidance,
which provide effective mechanisms to trade-off between fidelity and diversity.
However, these methods are not capable of guiding a generated image to be aware
of its geometric configuration, e.g., depth, which hinders their application to
areas that require a certain level of depth awareness. To address this
limitation, we propose a novel guidance method for diffusion models that uses
estimated depth information derived from the rich intermediate representations
of diffusion models. We first present label-efficient depth estimation
framework using internal representations of diffusion models. Subsequently, we
propose the incorporation of two guidance techniques based on pseudo-labeling
and depth-domain diffusion prior during the sampling phase to self-condition
the generated image using the estimated depth map. Experiments and
comprehensive ablation studies demonstrate the effectiveness of our method in
guiding the diffusion models towards the generation of geometrically plausible
images.
