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name: vector-quantization description: "Base VectorQuantize and RandomProjectionQuantizer workflows for single-stage codebook quantization, including masks and lens, image and 3D feature maps, kmeans/cosine/codebook-dim tuning, top-k/manual EMA behavior, gradient tricks, and optional FVQ bridge guidance." disable-model-invocation: true metadata: disco-role: operating license: MIT

Vector Quantization

Use this sub-skill for the single-stage, codebook-based APIs in vector_quantize_pytorch.

Covers

• VectorQuantize
• RandomProjectionQuantizer
• get_output_from_indices
• update_ema_indices
• topk
• mask and lens handling
• accept_image_fmap and accept_3d_fmap
• kmeans_init, use_cosine_sim, codebook_dim
• stale-code expiration
• orthogonal and codebook-diversity losses
• multi-headed VQ
• rotation_trick and directional_reparam
• sync_codebook
• optional FVQ bridge guidance

Route elsewhere

• Residual or grouped residual codebooks -> residual-quantizers
• FSQ or FSP -> scalar-quantizers
• LFQ, ResidualLFQ, GroupedResidualLFQ, LatentQuantize, BinaryMapper, EvoLFQ -> lookup-free-and-latent
• SimVQ, ResidualSimVQ, HierarchicalVQ -> sim-and-hierarchical

Start here

• API reference
• Workflows
• Troubleshooting
• Smoke helper

Fast read

If you only need a safe check, run the smoke helper on CPU. It covers a tiny VectorQuantize forward/reconstruction check and a tiny RandomProjectionQuantizer round trip.