Then we introduce a semantic mapper that produces the proxy masks from various input conditions including semantic masks.

We demonstrate the effectiveness of our algorithm by generating high-quality results of different target domains, including those with extremely few examples (e.g., 10).

Our method's efficacy is demonstrated on the class-conditional ImageNet generation benchmark, where

This restricts the tokenizer's ability to effectively leverage the redundancy

For image editing, it also excels in complex editing tasks. 2 Therefore, it is nearly impossible for a well-trained model to meet all human requirements, and the

Unconditional generation--the problem of modeling data distribution without relying on human-annotated labels--is a long-standing and fundamental challenge in generative models, creating a potential of learning from large-scale unlabeled data. In the literature, the generation quality of an unconditional method has been much worse than that of its conditional counterpart. This gap can be attributed to the lack of semantic information provided by labels. In this work, we show that one can close this gap by generating semantic representations in the representation space produced by a self-supervised encoder. These representations can be used to condition the image generator.

WARNING: This paper contains offensive images generated by models.