Unlocking Pre-trained Image Backbones for Semantic Image Synthesis
Authors: Tariq Berrada Ifriqi, Jakob Verbeek, Camille Couprie, Karteek Alahari

Semantic image synthesis i.e. generating images from user-provided semantic label maps is an important conditional image generation task as it allows to control both the content as well as the spatial layout of generated images. Although diffusion models have pushed the state of the art in generative image modeling the iterative nature of their inference process makes them computationally demanding. Other approaches such as GANs are more efficient as they only need a single feed-forward pass for generation but the image quality tends to suffer when modeling large and diverse datasets. In this work we propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images by exploiting feature backbones pre-trained for tasks such as image classification. We also introduce a new generator architecture with better context modeling and using cross-attention to inject noise into latent variables leading to more diverse generated images. Our model which we dub DP-SIMS achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K COCO-Stuff and Cityscapes surpassing recent diffusion models while requiring two orders of magnitude less compute for inference.