| language: | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - Autoregressive | |
| - Tokenizer | |
| pipeline_tag: image-to-image | |
| library_name: transformers | |
| ## Taming Scalable Visual Tokenizer for Autoregressive Image Generation | |
| Code: https://github.com/TencentARC/SEED-Voken | |
| Paper: https://arxiv.org/abs/2412.02692 | |
| This repo is used for hosting IBQ’s checkpoints. | |
| ## Introduction | |
| We propose Index Backpropagation Quantization (IBQ), a new vector quantization method for the joint optimization of all codebook embeddings and the visual encoder, ensuring the consistent latent space. IBQ enables scalable training of visual tokenizers and, for the first time, achieves a large-scale codebook (2^18) with high dimension (256) and high utilization. |