TencentARC/SEED-Voken

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1021 stars · 46 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SEED-Voken is a collection of visual tokenizers (Open-MAGVIT2 and IBQ) that convert images and videos into discrete tokens for autoregressive visual generation. It provides pretrained tokenizers with large codebooks and high codebook utilization for image and video generation research.

Use cases

• tokenize images into discrete codes for autoregressive image generation
• train a visual tokenizer with a large codebook for image synthesis
• convert video frames into tokens for video generation models
• reproduce Open-MAGVIT2 or IBQ research results
• use pretrained tokenizers for text-conditional image generation

When to choose

• you need state-of-the-art visual tokenization for autoregressive image or video generation
• you want large codebook sizes with high codebook utilization
• you are doing research on image tokenization or visual generation

When to avoid

• you need a production-ready image generation service rather than a tokenizer
• you work outside PyTorch/GPU research environments
• you need diffusion-based generation rather than token-based autoregressive generation

Facets

library · maturity active

machine-learning deep-learning image-processing serialization deep-learning computer-vision image-processing large-language-models python visual-tokenizer image-tokenization autoregressive-generation vq-quantization video-tokenizer research gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="TencentARC/SEED-Voken")