InstantPainting: Expanding GANs for Efficient Text-Conditioned Image Generation Platform

Bing-Kun Bao, Yefei Sheng, Jie Wang, Yaning Li, Sisi You

Abstract: Text-conditioned image generation enables cross-modal comprehension. Recent emergence of many platforms have found applications in diverse domains like assisted designing and video gaming. However, there still exist challenges in existing platforms due to their expensive training and time-consuming generation processes. In this paper, we introduce an efficient text-conditioned image generation platform, termed InstantPainting. Unlike existing platforms based on large-scale pre-trained diffusion models, InstantPainting expands generative adversarial networks (GANs) to achieve efficient generation by using only about three percent pre-training data of other platforms. Compared to existing platforms, InstantPainting achieves the following functions at a very low deployment cost and approximately 4 to 5 times faster generation speeds: (1) Multi-category and multi-size image generation (2) Image stylization and controlled generation (3) Creative generation, including the generation of poetry pictures and counterfactual images. The proposed platform provides web application implementations for PC and mobile, users can create high-quality images directly through the user interface.