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README.md

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+---
+license: mit
+tags:
+  - vector-quantization
+  - image-tokenizer
+  - codebook-regularization
+  - icml2026
+datasets:
+  - imagenet-1k
+---
+
+# DimVQ: Unveiling And Addressing Dimensional Collapse In Vector Quantization Models Via Codebook Regularization
+
+Official pre-trained checkpoints for the **ICML 2026** paper.
+
+## Model Description
+
+DimVQ identifies **dimensional collapse** in vector quantization models and proposes a simple **codebook regularization** to restore suppressed low-variance components. This regularization bridges the spectral gap between discrete codebook spaces and continuous representations.
+
+## Available Checkpoints
+
+| File | Model | Resolution | Codebook Size (K) | Embedding Dim (D) |
+|------|-------|-----------|-------------------|-------------------|
+| `simvq_K65536/65536.ckpt` | SimVQ + Codebook Reg. | 128x128 | 65,536 | 128 |
+| `simvq_K65536/65536.yaml` | Config for above | - | - | - |
+| `simvq_K262144/262144.ckpt` | SimVQ + Codebook Reg. | 128x128 | 262,144 | 128 |
+| `simvq_K262144/262144.yaml` | Config for above | - | - | - |
+
+## Usage
+
+```python
+# Load checkpoint
+import torch
+checkpoint = torch.load("262144.ckpt", map_location="cpu")
+model.load_state_dict(checkpoint["state_dict"])
+```
+
+## TODO
+
+- [ ] IBQ checkpoints (K=16384, K=262144, 256x256)
+- [ ] Downstream autoregressive generation models (IBQ-B, IBQ-L, IBQ-XXL)
+
+## Citation
+
+```bibtex
+@inproceedings{zhang2026dimvq,
+  title={Unveiling And Addressing Dimensional Collapse In Vector Quantization Models Via Codebook Regularization},
+  author={Zhang, Fang and Zhu, Yongxin and Liu, Yihao and Fu, Bin and Xu, Linli},
+  booktitle={International Conference on Machine Learning (ICML)},
+  year={2026}
+}
+```
+
+## Links
+
+- [Paper (arXiv)](https://arxiv.org/abs/TODO)
+- [Code (GitHub)](https://github.com/ksblk2116/dimvq)