Finite Scalar Quantization: VQ-VAE Made Simple (2309.15505v2)

Abstract: We propose to replace vector quantization (VQ) in the latent representation of VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), where we project the VAE representation down to a few dimensions (typically less than 10). Each dimension is quantized to a small set of fixed values, leading to an (implicit) codebook given by the product of these sets. By appropriately choosing the number of dimensions and values each dimension can take, we obtain the same codebook size as in VQ. On top of such discrete representations, we can train the same models that have been trained on VQ-VAE representations. For example, 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 depth estimation, colorization, and panoptic segmentation. Despite the much simpler design of FSQ, we obtain competitive performance in all these tasks. We emphasize that FSQ does not suffer from codebook collapse and does not need the complex machinery employed in VQ (commitment losses, codebook reseeding, code splitting, entropy penalties, etc.) to learn expressive discrete representations.

• The paper presents FSQ, a novel method that replaces vector quantization with finite scalar quantization, eliminating issues like codebook collapse.
• It employs a straight-through estimator to efficiently propagate gradients and reduce computational overhead while achieving high codebook utilization.
• Experimental results in MaskGIT and UViM demonstrate FSQ's competitive performance in image generation and dense prediction tasks.

Finite Scalar Quantization: VQ-VAE Made Simple

Introduction

The paper presents a novel approach to simplify the Vector Quantization-Variational AutoEncoder (VQ-VAE) by replacing vector quantization in the latent space with Finite Scalar Quantization (FSQ). This method projects the VAE representation down to a few dimensions, quantizing each to a small set of fixed values, leading to a codebook analogous to that in VQ. Unlike traditional VQ, FSQ does not suffer from codebook collapse and eliminates the need for strategies like commitment losses or reseeding. The paper illustrates the implementation of FSQ within architectures like MaskGIT for image generation and UViM for dense prediction tasks, offering competitive performance.

Methodology of FSQ

The FSQ approach involves converting high-dimensional vectors into low-dimensional spaces. It bounds each dimension to a predefined number of levels and performs scalar quantization via rounding. The implicit codebook size can be controlled by the number of dimensions and the quantization levels, i.e., for a $d$-dimensional space with each dimension having $L$ levels, the effective codebook size is $L^d$. FSQ leverages the straight-through estimator (STE) to propagate gradients through the non-differentiable rounding operation, maintaining high codebook utilization.

Figure 1

Figure 1: Comparison of FSQ and VQ showing codebook utilization in encoder-decoder architectures.

Experiments and Results

MaskGIT Implementation

The FSQ method was evaluated using MaskGIT for image generation on ImageNet256. The experimental configurations included FSQ and standard VQ setups for various codebook sizes. Key metrics evaluated were Sampling FID, Reconstruction FID, Precision, Recall, and Codebook Usage. FSQ demonstrated scalable improvements in Sampling FID with increasing codebook sizes, whereas VQ struggled with diminishing performance at larger sizes due to underutilization.

Figure 2

Figure 2: Comparative performance metrics of FSQ vs. VQ across varying codebook sizes.

UViM for Dense Prediction

UViM was employed to assess FSQ on tasks like depth estimation, colorization, and panoptic segmentation. FSQ was integrated into UViM models, and performance was compared against VQ-based models. In the absence of VQ's auxiliary techniques, FSQ achieved competitive metrics with high codebook utilization and reduced parameter counts. The paper affirmed FSQ's ability to maintain effectively interpretable and dense representations.

Figure 3

Figure 3: Visualization of task-driven outputs using FSQ and VQ, demonstrating comparable qualitative outputs.

Advantages of FSQ

FSQ offers numerous benefits over traditional VQ approaches:

• Simplicity: FSQ dispenses with complex codebook management, such as reseeding or splitting, and eschews auxiliary losses.
• Efficiency: By operating in a lower-dimensional space, FSQ reduces computational overhead and parameter requirements.
• Scalability: FSQ scales gracefully with increasing codebook sizes, consistently improving performance metrics like FID and maintaining near-complete codebook utilization.

Implications and Future Work

The FSQ framework presents a reduced complexity alternative to VQ, retaining the ability to generate competitive image and vision outputs without the typical burdens of codebook collapse. The streamlining of discrete representation learning could expedite developments across various AI-driven tasks. Future exploration into FSQ's integration with other generative models or expansion into new AI domains like audio synthesis and natural language processing is warranted for broader applicability.

Conclusion

The FSQ approach simplifies and optimizes the learning of discrete representations within AI models. By addressing the limitations of vector quantization, FSQ establishes a viable path forward for efficient model training in high-dimensional data spaces. The experiments, particularly within MaskGIT and UViM frameworks, demonstrate not only the method's effectiveness but also its vast potential for deployment across diverse generative and predictive model architectures.