This paper presents $\textbf{R}$epresentation-$\textbf{C}$onditioned image$\textbf{G}$eneration (RCG), a simple yet effective image generation frameworkwhich sets a new benchmark in class-unconditional image generation. RCG doesnot condition on any human annotations. Instead, it conditions on aself-supervised representation distribution which is mapped from the imagedistribution using a pre-trained encoder. During generation, RCG samples fromsuch representation distribution using a representation diffusion model (RDM),and employs a pixel generator to craft image pixels conditioned on the sampledrepresentation. Such a design provides substantial guidance during thegenerative process, resulting in high-quality image generation. Tested onImageNet 256$\times$256, RCG achieves a Frechet Inception Distance (FID) of3.31 and an Inception Score (IS) of 253.4. These results not only significantlyimprove the state-of-the-art of class-unconditional image generation but alsorival the current leading methods in class-conditional image generation,bridging the long-standing performance gap between these two tasks. Code isavailable at https://github.com/LTH14/rcg.