: RAR maintains full compatibility with standard language modeling frameworks, making it easier to integrate with existing AI architectures. Managing the .rar File
Published in , this paper introduces a new state-of-the-art method for generating images using an autoregressive (AR) framework.
: The model starts with high randomness (permuted order) and gradually returns to the standard raster order as training progresses. 868_1_RP.rar
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Paper Overview: Randomized Autoregressive Visual Generation (RAR) : RAR maintains full compatibility with standard language
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: It achieved a Frechet Inception Distance (FID) score of 1.48 on the ImageNet-256 benchmark, outperforming many leading diffusion-based and masked transformer models.