CompVis/taming-transformers

Health v2 · maintenance only

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6521 stars · 1219 forks observed · 2026-08-28

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

The official implementation of 'Taming Transformers for High-Resolution Image Synthesis' (CVPR 2021), combining a convolutional VQGAN codebook with an autoregressive transformer to synthesize high-resolution images. It ships pretrained models for class-conditional ImageNet, FFHQ, CelebA-HQ, COCO, and ADE20k, plus training scripts and Colab notebooks.

Use cases

• generate high-resolution images with transformers
• train a VQGAN on my own dataset
• run class-conditional ImageNet image synthesis
• semantically-guided image synthesis from segmentation maps
• reconstruct images with pretrained VQGAN models
• compare VQGAN with DALL-E

When to choose

• you need the original VQGAN/transformer image synthesis models or their pretrained checkpoints
• you want to train a VQGAN tokenizer on custom data
• you're reproducing the CVPR 2021 paper or building on its codebook approach

When to avoid

• you want modern latent diffusion image generation - use CompVis/latent-diffusion or Stable Diffusion instead
• you need a maintained production library - this is research code with infrequent updates
• you need text-to-image generation out of the box

Facets

library · maturity maintenance

machine-learning deep-learning image-processing transformers computer-vision deep-learning image-processing artificial-intelligence python cross-platform vqgan image-synthesis autoregressive-transformer generative-models research-code cvpr-2021 gpu linux

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