: The model starts with high randomness (permuted order) and gradually returns to the standard raster order as training progresses.
Published in , this paper introduces a new state-of-the-art method for generating images using an autoregressive (AR) framework.
: RAR maintains full compatibility with standard language modeling frameworks, making it easier to integrate with existing AI architectures. Managing the .rar File 868_1_RP.rar
If you have downloaded this specific file and need to access its contents (which typically include code, models, or datasets), you will need specialized software:
: Use utilities like WinRAR or 7-Zip to unpack the archive. : The model starts with high randomness (permuted
: 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.
: Always scan downloaded archives with antivirus software before opening to ensure they do not contain malicious payloads. Managing the
Paper Overview: Randomized Autoregressive Visual Generation (RAR)