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We present a new perspective of achieving image synthesis by viewing thistask as a visual token generation problem. Different from existing paradigmsthat directly synthesize a full image from a single input (e.g., a latentcode), the new formulation enables a flexible local manipulation for differentimage regions, which makes it possible to learn content-aware and fine-grainedstyle control for image synthesis. Specifically, it takes as input a sequenceof latent tokens to predict the visual tokens for synthesizing an image. Underthis perspective, we propose a token-based generator (i.e.,TokenGAN).Particularly, the TokenGAN inputs two semantically different visual tokens,i.e., the learned constant content tokens and the style tokens from the latentspace. Given a sequence of style tokens, the TokenGAN is able to control theimage synthesis by assigning the styles to the content tokens by attentionmechanism with a Transformer. We conduct extensive experiments and show thatthe proposed TokenGAN has achieved state-of-the-art results on severalwidely-used image synthesis benchmarks, including FFHQ and LSUN CHURCH withdifferent resolutions. In particular, the generator is able to synthesizehigh-fidelity images with 1024x1024 size, dispensing with convolutionsentirely.

Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers