NYU의 새로운 AI 아키텍처로 고품질 이미지 생성을 더 빠르고 저렴하게 만듭니다.

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 that understanding part with the generation part." He also pointed to future applications in "RAG-based generation, where you use RAE encoder features for search and then generate new images based