Enhancing Text-to-Image Generation Using Ensemble Vision Transformers

The generation of images from text aims to create images that are both realistic and contextually accurate based on an input description. Although GANs have succeeded greatly in this domain, but they struggle with comprehension, visual nuances, representation of speech, and even classification of new unseen objects. Several techniques has been revolutionized recently with the emergence of new architectures such as Vision Transformers (ViTs) and hierarchical language models. In conjunction, the ViewDiff, ArtCrafter, and ACE frameworks have markedly improved the domain with 3D-consistent models. Several strategies such as SceneBooth maintain accuracy for the subjects and smooth backgrounds through the use of multi-modal layout generation with ControlNet and Gated Self-Attention adapters. Newly, PromptGuard enables the moderation of unsafe content by the use of soft prompts without compromising the performance of the model. The cyclic evaluation metric CAMScore facilitates Gap Between the modality in a traditional evaluation approach through consistency across modalities and examining the details. Prompting guards assist in retaining model performance while moderating unsafe content through soft prompts. This comprehensive framework achieves the highest scores on various benchmarks and surpasses other techniques.