kakaobrain/rq-vae-transformer
Transformer-based autoregressive image generation model using Residual Quantization with a two-stage RQ-VAE and RQ-Transformer framework for high-resolution synthesis.
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This repository provides the official implementation of a two-stage framework for high-resolution image generation. The first stage uses RQ-VAE to quantize and represent an image as a stack of discrete codes, while the second stage employs an autoregressive RQ-Transformer to generate these codes conditionally. The model supports both class-conditional and text-conditional image synthesis, and includes pretrained checkpoints for reproduction of the published results.
Frequently asked
- What is kakaobrain/rq-vae-transformer?
- Transformer-based autoregressive image generation model using Residual Quantization with a two-stage RQ-VAE and RQ-Transformer framework for high-resolution synthesis.
- Is rq-vae-transformer open source?
- Yes — kakaobrain/rq-vae-transformer is an open-source project tracked on heatdrop.
- What language is rq-vae-transformer written in?
- kakaobrain/rq-vae-transformer is primarily written in Jupyter Notebook.
- How popular is rq-vae-transformer?
- kakaobrain/rq-vae-transformer has 1k stars on GitHub.
- Where can I find rq-vae-transformer?
- kakaobrain/rq-vae-transformer is on GitHub at https://github.com/kakaobrain/rq-vae-transformer.