Cornell University Researchers Introduce Reinforcement Learning for Consistency Models for Efficient Training and Inference in Text-to-Image Generation

Computer vision often involves complex generative models and seeks to bridge the gap between textual semantics and visual representation. It offers myriad applications, from enhancing digital art creation to aiding in design processes. One of the primary challenges in this domain is the efficient generation of high-quality images that closely align with given textual prompts.  […]

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