ARTIST: A Transformer-based Chinese Text-to-Image Synthesizer Digesting Linguistic and World Knowledge
Tingting Liu, Chengyu Wang, Xiangru Zhu, Lei Li, Minghui Qiu, Jun Huang, Ming Gao, Yanghua Xiao · 2022
Text-to-Image Synthesis (TIS) is a popular task to convert natural language texts into realistic images.Recently, transformer-based TIS models (such as DALL-E) have been proposed using the encoder-decoder architectures.Yet, these billion-scale TIS models are difficult to tune and deploy in resource-constrained environments.In addition, there is a lack of language-specific TIS benchmarks for Chinese, together with high-performing models with moderate sizes.In this work, we present ARTIST, A tRansformer-based Chinese Textto-Image SynThesizer for high-quality image generation.In ARTIST, the rich linguistic and relational knowledge facts are injected into the model to ensure better model performance without the usage of ultra-large models.We further establish a large-scale Chinese TIS benchmark with the re-production results of state-of-the-art transformer-based TIS models.Results show ARTIST outperforms previous approaches. 1