kakaobrain/rq-vae-transformer

Health v2 · maintenance only

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1030 stars · 114 forks observed · 2026-08-28

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

The official PyTorch implementation of 'Autoregressive Image Generation using Residual Quantization' (CVPR 2022), implementing RQ-VAE and RQ-Transformer for high-resolution image generation. It includes training and evaluation pipelines plus pretrained checkpoints for FFHQ, LSUN, and ImageNet datasets.

Use cases

• generate high-resolution images autoregressively
• reproduce CVPR 2022 RQ-VAE results
• train a residual quantized VAE on custom image datasets
• run text-to-image generation with RQ-Transformer
• evaluate image generation quality with FID
• experiment with two-stage discrete code image generation

When to choose

• you need the reference implementation of RQ-VAE/RQ-Transformer
• you want pretrained checkpoints for FFHQ, LSUN, or ImageNet image generation
• you are researching autoregressive discrete-code image generation
• you want to build on residual quantization for generative vision models

When to avoid

• you need a production-ready image generation service
• you want modern diffusion-based image generation
• you need active community support or frequent updates
• you cannot run large GPU workloads (models are 355M-612M parameters)

Facets

library · maturity maintenance

machine-learning deep-learning image-processing llm-training deep-learning computer-vision image-processing artificial-intelligence python rq-vae rq-transformer autoregressive-image-generation residual-quantization pytorch text-to-image cvpr-2022 pretrained-checkpoints linux gpu

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