The University of Southern California, the University of Washington, Bar-Ilan University, and the Google Research team have introduced DreamSync, a novel AI framework that enhances text-to-image synthesis by generating candidate images and utilizing a visual question answering model for evaluation. This framework does not require manual annotations, modifications to model architectures, or reinforcement learning. DreamSync achieves significant improvements in alignment and visual appeal on T2I models through a model-agnostic framework and feedback from visual language models. Additionally, DreamSync has successfully enhanced the performance of the SDXL and SD v1.4T2I models.
New AI Framework DreamSync: Improving Text-to-Image Synthesis with Feedback from Image Understanding Models

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