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We propose to replace vector quantization (VQ) in the latent representationof VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), wherewe project the VAE representation down to a few dimensions (typically less than10). Each dimension is quantized to a small set of fixed values, leading to an(implicit) codebook given by the product of these sets. By appropriatelychoosing the number of dimensions and values each dimension can take, we obtainthe same codebook size as in VQ. On top of such discrete representations, wecan train the same models that have been trained on VQ-VAE representations. Forexample, autoregressive and masked transformer models for image generation,multimodal generation, and dense prediction computer vision tasks. Concretely,we employ FSQ with MaskGIT for image generation, and with UViM for depthestimation, colorization, and panoptic segmentation. Despite the much simplerdesign of FSQ, we obtain competitive performance in all these tasks. Weemphasize that FSQ does not suffer from codebook collapse and does not need thecomplex machinery employed in VQ (commitment losses, codebook reseeding, codesplitting, entropy penalties, etc.) to learn expressive discreterepresentations.

Finite Scalar Quantization: VQ-VAE Made Simple