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We show how bidirectional transformers trained for masked token predictioncan be applied to neural image compression to achieve state-of-the-art results.Such models were previously used for image generation by progressivly samplinggroups of masked tokens according to uncertainty-adaptive schedules. Unlikethese works, we demonstrate that predefined, deterministic schedules perform aswell or better for image compression. This insight allows us to use maskedattention during training in addition to masked inputs, and activation cachingduring inference, to significantly speed up our models (~4 higher inferencespeed) at a small increase in bitrate.

M2T: Masking Transformers Twice for Faster Decoding