StableVQ: Practical Guidelines for Stable Vector-Quantized Tokenizer Training

arXiv:2609.26774 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-22

Terminology

• Quantization — 25× in this paper · explained in 12 episodes
• AUC — 11× in this paper · explained in 2 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Transformer — 9× in this paper · explained in 2 episodes
• Vector Quantization — 9× in this paper
• Robustness — 8× in this paper · explained in 5 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Failure Modes — 6× in this paper · explained in 3 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Entanglement — 4× in this paper · explained in 3 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Alignment Problem — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Sparsity — 3× in this paper · explained in 4 episodes
• Tokenization — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Cooperation — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Straight-Through Estimator — 2× in this paper
• Active Regions — 1× in this paper · explained in 1 episode
• Alignment Methods — 1× in this paper · explained in 1 episode
• Decoupling — 1× in this paper · explained in 2 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Vector Quantization (VQ) — 1× in this paper · explained in 2 episodes

Sources

• Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization
• VQRAE: Representation Quantization Autoencoders for Multimodal Understanding, Generation and Reconstruction
• Enhancing Vector Quantization with Distributional Matching: A Theoretical and Empirical Study
• Categorical Reparameterization with Gumbel-Softmax
• Flow Matching for Generative Modeling
• Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation
• Finite Scalar Quantization: VQ-VAE Made Simple
• Score-Based Generative Modeling through Stochastic Differential Equations
• Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation
• Vector-quantized Image Modeling with Improved VQGAN
• Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
• Diffusion Transformers with Representation Autoencoders

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