Text-to-Image Synthesis is the process of generating images from textual descriptions using machine learning models, often powered by deep learning techniques like GANs or transformers. The model interprets the given text and produces a visual representation that aligns with the described content. This technology has applications in creative fields, design, and artificial intelligence research.

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Attribute-Centric Compositional Text-to-Image Generation

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Despite the recent impressive breakthroughs in text-to-image generation, generative models have difficulty in capturing thedata distribution of underrepresented attribute compositions while over-memorizing overrepresented attribute compositions,which raises public concerns about their robustness and fairness. To tackle this challenge, we propose ACTIG, an attributecentriccompositional text-to-image generation framework. We present an attribute-centric feature augmentation and a novelimage-free training scheme, which greatly improves model’s ability to generate images with underrepresented attributes.Wefurther propose an attribute-centric contrastive loss to avoid overfitting to overrepresented attribute compositions.We validateour framework on the CelebA-HQ and CUB datasets. Extensive experiments show that the compositional generalization ofACTIG is outstanding, and our framework outperforms previous works in terms of image quality and text-image consistency

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