NYU’s new AI architecture makes high-quality image generation faster and cheaper
Researchers at New York University have developed a new architecture for diffusion models that improves the semantic representation of the images they generate. “Diffusion Transformer with Representation Autoencoders” (RAE) challenges some of the accepted norms of building diffusion models. The NYU researcher's model is more efficient and accurate than standard diffusion models, takes advantage of the latest research in representation learning and could pave the way for new applications that were previously too difficult or expensive.This breakthrough could unlock more reliable and powerful features for enterprise applications. "To edit images well, a model has to really understand what’s in them," paper co-author Saining Xie told VentureBeat. "RAE helps connect t [...]
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