With the advent of generative adversarial networks, synthesizing images from text
descriptions has recently become an active research area. It is a flexible and intuitive way for …

We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation.
This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent …

A rich supply of data and innovative algorithms have made data-driven modeling a popular
technique in modern industry. Among various data-driven methods, latent variable models …

In this work, we propose TediGAN, a novel framework for multi-modal image generation and
manipulation with textual descriptions. The proposed method consists of three components …

Synthesizing high-fidelity complex images from text is challenging. Based on large
pretraining, the autoregressive and diffusion models can synthesize photo-realistic images …

In this work, we investigate the task of text-to-image (T2I) synthesis under the abstract-to-
intricate setting, ie, generating intricate visual content from simple abstract text prompts …

Synthesizing high-quality realistic images from text descriptions is a challenging task.
Existing text-to-image Generative Adversarial Networks generally employ a stacked …

Human evaluation is critical for validating the performance of text-to-image generative
models, as this highly cognitive process requires deep comprehension of text and images …

Text-to-image synthesis refers to generating an image from a given text description, the key
goal of which lies in photo realism and semantic consistency. Previous methods usually …

Text-to-image synthesis (T2I) aims to generate photo-realistic images which are
semantically consistent with the text descriptions. Existing methods are usually built upon …