lucidrains/vector-quantize-pytorch

Health v2 · maintenance only

Adoption not part of the score

3996 stars · 337 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch library implementing vector and scalar quantization, including Residual VQ and techniques like DiVeQ codebook updates. It originated from DeepMind's TensorFlow implementation and is used in models like VQ-VAE and RQ-VAE for compressing data into discrete codes.

Use cases

• quantize neural network embeddings into discrete codes
• build a VQ-VAE for image generation
• implement residual vector quantization for audio codecs
• train codebooks with exponential moving averages in PyTorch
• compress high-resolution images with RQ-VAE
• apply scalar quantization to model latents

When to choose

• you need ready-made vector quantization layers in PyTorch
• you are building VQ-VAE, RQ-VAE, or audio/image tokenizers
• you want EMA or gradient-based codebook updates without writing them yourself

When to avoid

• you need quantization for model compression/inference speedup (that's a different kind of quantization)
• you work outside PyTorch
• you need a full VQ-VAE model rather than quantization building blocks

Facets

library · maturity active

machine-learning deep-learning serialization deep-learning machine-learning artificial-intelligence python pytorch vector-quantization scalar-quantization vq-vae residual-vq codebook-learning

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