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

1 Introduction . Unconditional image synthesis generates images from latent codes by adversarial training [ 10 , 16 , 19 , 25 , 27 , 33 , 50 ] . Recent advances have been achieved by a style-based generator architecture in terms of both the visual quality and resolution of generated images [ 26 , 27 , 28 , 37 , 52 ] . In particular , the style-based generator has been widely used in many other generative tasks , including facial editing [ 9 , 40 ] , style transfer [ 1 , 38 ] , image super-resolution [ 17 , 31 ] , and image inpainting [ 2 , 52 ] . The key to the success of the style-based generator lies in the learning of the style control based on the intermediate latent spaceW [ 27 , 28 ] . Instead of feeding the input latent code z ∈ Z through the input layer only ( Figure1-a ) , the style-based generator maps the input z to an intermediate latent space w ∈ W , which then controls the “ style ” of the image at each layer via adaptive instance normalization ( AdaIN [ 21 ] ) ( Figure 1-b ) . It has been demonstrated that such a design allows a less entangled representation learning inW , leading to better generative image modeling [ 12 , 23 , 27 , 40 ] . Despite the promising results , the style-based generator can suffer from the style control via AdaIN operation [ 28 , 52 ] . Specifically , the style control is content-independent . It “ washes away ” the original information of features by normalization and assigns new styles decided by the latent codes regardless of the image/feature content . Besides , the style code w affects the entire image by scaling and biasing complete feature maps with a single value via AdaIN operation [ 35 , 44 , 54 ] . Such an imposed single style over multiple image regions can inevitably result in entangled representation of ∗This work was done while Yanhong Zeng was a research intern at Microsoft Research Asia . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . different image variations ( e.g. , hairstyle , facial expression ) [ 28 , 44 , 52 ] . These limitations for image modeling can even lead to visible artifacts in the synthesized results ( e.g. , droplet artifacts [ 27 ] ) . To get rid of the issues caused by StyleGAN ’ s style modeling , we introduce a new perspective that views image synthesis as a visual token generation problem . The visual token is a popular representation of an image patch with a predefined size and position [ 6 , 7 , 13 ] ; and has shown an impressive superiority in various tasks with the development of Transformer models [ 6 , 13 , 43 , 46 ] . Inspired by the appealing property of the token-based representation , we propose to achieve image synthesis by visual token generation . Specifically , it takes as input a sequence of latent tokens to predict the visual tokens of an image . Such a token-based representation enables a flexible local manipulation for different image regions , which makes it possible to learn content-aware and fine-grained style control for image synthesis . Under this new paradigm , we design a token-based generator , i.e. , TokenGAN , for the visual token generation problem . Specifically , we consider two different types of input tokens in the generator , i.e. , the content tokens and the style tokens . The content tokens are learned as the constant input in the generator network and the style tokens are projected from a learned intermediate latent space ( Figure1-c ) . Given a sequence of style tokens , the TokenGAN learns to control the visual token generation by rendering each content token with related style tokens according to their semantics . In particular , since the Transformer has been verified to be effective in sequence modeling in a broad range of tasks [ 6 , 43 ] , we adopt a generator network architecture from a visual Transformer to model the relations between the content tokens and the style tokens . Through such a content-dependent style modeling by the attention mechanism in Transformer , the TokenGAN is able to achieve content-aware and fine-grained style learning for image synthesis . We conduct both quantitative comparisons and qualitative analysis on several unconditional image generation benchmarks . The results show that the token-based generator has achieved comparable results to the state-of-the-art in image synthesis . We summarize our contributions as below : • We propose a new perspective of achieving image synthesis by visual token generation . Such a token-based representation enables flexible local manipulation for different image regions , leading to a better image modeling . • We propose a token-based generator ( i.e. , TokenGAN ) for the visual token generation . Specifically , the TokenGAN introduces the style tokens and the content tokens . It adopts a Transformer-based network for content-aware style modeling . • We show extensive experiments ( quantitative and qualitative comparisons , study on style editing , image inversion and image interpolation ) to verify the effectiveness of the tokenbased generator . Specifically , the token-based generator is able to synthesize high-fidelity 1024× 1024 images without any convolutions in the generator . 2 Related works . 2.1 Style-based generator . The distinguishing feature of the style-based generator is its unconventional generator architecture [ 26 , 27 , 28 , 52 ] . Typically , the style-based generator consists of a mapping network f and a synthesis network g. The mapping network is used to transform the input latent code z to an intermediate latent code w ∈ W for learning a less entangled latent space . The synthesis network follows a progressive growing design that is able to first output low-resolution images that are not affected significantly by high-resolution layers [ 25 , 27 ] . Such an architecture design can lead to an automatic , unsupervised separation of high-level attributes in different layers . For example , when trained on human faces , the low-resolution layers control coarse styles ( e.g. , pose , identity ) of images and high-resolution layers control fine styles ( e.g. , micro-structure , color scheme ) [ 25 ] . To control the attributes of the image , the style-based generator produces styles from the intermediate latent code w by affine transforms , which then control each layer of the synthesis network via adaptive instance normalization ( AdaIN ) [ 21 , 35 ] . The AdaIN operation removes styles from previous layers by normalizing each feature map to zero mean and unit deviation , and it assigns the new styles by scaling and biasing the complete normalized feature maps . However , it has been demonstrated such an AdaIN operation can destroy the magnitude information of features and thus results in the well-known droplet artifacts [ 28 ] . Besides , the scale-specific style disentanglement in StyleGAN can be limited due to the complex semantic components in each feature map [ 28 , 52 ] . In this paper , we explore a new architecture by flattening the image as a sequence of tokens , which enables fine-grained control of the image by assigning token-wise semantic-aware styles based on the attention mechanism . 2.2 Transformer in vision . The Transformer typically takes as input a sequence of vectors , called tokens [ 43 ] . The Transformer is built for sequence modeling solely on attention mechanisms over the tokens , dispensing with recurrence and convolutions entirely [ 5 , 11 , 43 , 30 ] . Due to its great success in the field of natural language processing , an increasing number of works attempt to extend Transformer for computer vision tasks [ 6 , 7 , 8 , 13 , 18 , 36 , 55 ] . For example , iGPT shows promising results in image classification by pre-training a sequence Transformer with the task of auto-regressive next pixel prediction and masked pixel prediction [ 8 ] . Dosoyvitskiy et al . propose to split an image into patches and feed these patches into a standard Transformer ( ViT ) for image classification [ 13 ] . They show that a ViT with large-scale training can trump CNNs equipped with inductive bias . DETR is a seminal work that views object detection as a direct set prediction problem , eliminating the need for hand-crafted components and achieving impressive performance by Transformer [ 6 ] . To mitigate the issue of high computation complexity associated with long sequences caused by high-resolution images , a branch of works explores lightweight Transformer ( e.g .. , Deformable DETR ) for vision tasks [ 55 ] . Transformer is also attracting increasing attention in low-level vision tasks [ 14 , 24 , 34 , 36 , 42 ] . Parmar et al . cast image generation as an autoregressive sequence generation problem and propose Image Transformer with a local self-attention mechanism [ 36 ] . However , Image Transformer can suffer from its quadratic computation cost and its long inference time due to auto-regressive prediction . Most existing works adopt a hybrid CNN-Transformer architecture , which consists of a CNN head for feature extraction , a Transformer encoder-decoder backbone , and a CNN tail for feature decoding [ 7 , 46 , 48 ] . For example , IPT fully utilizes the Transformer architecture by a large-scale pre-training and achieves promising results in several image restoration tasks [ 7 ] . In parallel work , Hudson et al . introduce bipartite structure to maintain computation of linear efficiency for long-range interactions across the image based on a convolution backbone [ 22 ] . Our token-based generator inherits the network architecture from Transformer without any convolution layers and is able to yield surprising promising results for high-resolution image synthesis . 3 Approach . In this section , we introduce the details of the proposed token-based generator ( TokenGAN ) . As depicted in Figure 2 , the TokenGAN takes as input two kinds of tokens to generate the visual tokens of images by a Transformer . We introduce the input tokens in Section 3.1 and the token-based generator architecture in Section 3.2 , following the overall optimization in Section 3.3 . 3.1 Visual tokens . It ’ s intuitive that the visual tokens of an image contain the information of both content ( i.e. , semantics ) and related styles . In our token-based generator , we choose to separate these two kinds of information by a sequence of content tokens and a sequence of style tokens , so that the interactions between them can be modeled and controlled for the image synthesis . We introduce the details of each as below . Content tokens . As depicted in Figure 2-a , the TokenGAN starts from the sequence of learned constant content tokens c ∈ Rm× ( p2×d ) through the input layer . Specifically , d is the dimension in terms of channels , p × p is the patch size of the content token , and m is the length of the content token sequence . To maintain the position information of each token , we add position encodings p ∈ Rm× ( p2×d ) to each content token following a commonly used paradigm [ 7 , 13 ] . For simplicity , we rewrite the notation for the content tokens as { c1 , · · · , cm } , where ci ∈ Rd . Style tokens . Given a latent code z from the input latent space Z , the mapping network adopts several MLPs to map the input z to a set of different style tokens { s1 , · · · , sn } ∈ S , where si ∈ Rd . As shown in Figure 2-b , the style tokens are paired with a set of learnable semantic embedding as a key-value structure in each style modulation layer of the TokenGAN . With the Transformer modeling , all the content tokens will match with the semantic embedding and then fetch the new style from the style tokens based on the matching results . The fetched new styles are used to control the values of the content tokens , which are finally decoded to images . Such a token-wise style control enables content-aware and fine-grained style learning for image synthesis .

Summary: This paper proposes TokenGAN for unconditional image synthesis. The token-based generator takes the learned content tokens and style tokens from the latent space as input, and output a series of style-modulated content tokens, which are then concatenated and reshaped to get the output image. Cross-attention is used to assign the styles to different content tokens. The model adopts the style-based generator idea from StyleGAN and introduces the recent token-based structures and transformers to the traditional style-based generator. Experiments are conducted on FFHQ and LSUN CHURCH datasets and the quantitative results are slightly better than StyleGAN2.