  Synthesizing high-quality photorealistic images with textual descriptions as
a condition is very challenging. Generative Adversarial Networks (GANs), the
classical model for this task, frequently suffer from low consistency between
image and text descriptions and insufficient richness in synthesized images.
Recently, conditional affine transformations (CAT), such as conditional batch
normalization and instance normalization, have been applied to different layers
of GAN to control content synthesis in images. CAT is a multi-layer perceptron
that independently predicts data based on batch statistics between neighboring
layers, with global textual information unavailable to other layers. To address
this issue, we first model CAT and a recurrent neural network (RAT) to ensure
that different layers can access global information. We then introduce shuffle
attention between RAT to mitigate the characteristic of information forgetting
in recurrent neural networks. Moreover, both our generator and discriminator
utilize the powerful pre-trained model, Clip, which has been extensively
employed for establishing associations between text and images through the
learning of multimodal representations in latent space. The discriminator
utilizes CLIP's ability to comprehend complex scenes to accurately assess the
quality of the generated images. Extensive experiments have been conducted on
the CUB, Oxford, and CelebA-tiny datasets to demonstrate the superiority of the
proposed model over current state-of-the-art models. The code is
https://github.com/OxygenLu/RATLIP.
