Text to image synthesis with multi-granularity feature aware enhancement Generative Adversarial Networks

Abstract

Synthesizing complex images from text presents challenging. Compared to autoregressive and diffusion model-based methods, Generative Adversarial Network-based methods have significant advantages in terms of computational cost and generation efficiency yet remain two limitations: first, these methods often refine all features output from the previous stage indiscriminately, without considering these features are initialized gradually during the generation process; second, the sparse semantic constraints provided by the text description are typically ineffective for refining fine-grained features. These issues complicate the balance between generation quality, computational cost and inference speed. To address these issues, we propose a Multi-granularity Feature Aware Enhancement GAN (MFAE-GAN), which allows the refinement process to match the order of different granularity features being initialized. Specifically, MFAE-GAN (1) samples category-related coarse-grained features and instance-level detail-related fine-grained features at different generation stages based on different attention mechanisms in Coarse-grained Feature Enhancement (CFE) and Fine-grained Feature Enhancement (FFE) to guide the generation process spatially, (2) provides denser semantic constraints than textual semantic information through Multi-granularity Features Adaptive Batch Normalization (MFA-BN) in the process of refining fine-grained features, and (3) adopts a Global Semantics Preservation (GSP) to avoid the loss of global semantics when sampling features continuously. Extensive experimental results demonstrate that our MFAE-GAN is competitive in terms of both image generation quality and efficiency.