Self-conditioned Image Generation via Generating Representations
Authors: name: Tianhong Li, name: Dina Katabi, name: Kaiming He

This paper presents Representation-Conditioned image
Generation (RCG), a simple yet effective image generation framework
which sets a new benchmark in class-unconditional image generation. RCG does
not condition on any human annotations. Instead, it conditions on a
self-supervised representation distribution which is mapped from the image
distribution using a pre-trained encoder. During generation, RCG samples from
such representation distribution using a representation diffusion model (RDM),
and employs a pixel generator to craft image pixels conditioned on the sampled
representation. Such a design provides substantial guidance during the
generative process, resulting in high-quality image generation. Tested on
ImageNet 256times256, RCG achieves a Frechet Inception Distance (FID) of
3.31 and an Inception Score (IS) of 253.4. These results not only significantly
improve the state-of-the-art of class-unconditional image generation but also
rival the current leading methods in class-conditional image generation,
bridging the long-standing performance gap between these two tasks. Code is
available at https://github.com/LTH14/rcg.