Update README.md

README.md

@@ -1,56 +1,69 @@
-# Progressive Compression with Universally Quantized Diffusion Models
-
-Official implementation of our ICLR 2025 paper [Progressive Compression with Universally Quantized Diffusion Models](https://www.justuswill.com/uqdm/) by Yibo Yang, Justus Will, and Stephan Mandt.
-
-## TLDR
-
-Our new form of diffusion model, UQDM, enables practical progressive compression with an unconditional diffusion model - avoiding the computational intractability of Gaussian channel simulation by using universal quantization.
-
-## Setup
-
-```
-git clone https://github.com/mandt-lab/uqdm.git
-cd uqdm
-conda env create -f environment.yml
-conda activate uqdm
-```
-
-For working with ImageNet64, download from the [official website](https://image-net.org/download-images.php) the npz dataset files:
-- Train(64x64) part1, Train(64x64) part2, Val(64x64)
-
-and place them in `./data/imagenet64`. Our implementation removes the duplicate test images as saved in `./data/imagenet64/removed.npy` during loading.
-
-## Usage
-
-Load pretrained models by placing the `config.json` and `checkpoint.pt` in a shared folder and load them for example via
-```python
-from uqdm import load_checkpoint, load_data
-model = load_checkpoint('checkpoints/uqdm-tiny')
-train_iter, eval_iter = load_data('ImageNet64', model.config.data)
-```
-
-To train or evaluate call respectively via
-
-```python
-model.trainer(train_iter, eval_iter)
-model.evaluate(eval_iter)
-```
-
-To save the compressed representation of an image and to reconstruct an image/images from their compressed representations, use
-
-```python
-image = next(iter(eval_iter))
-compressed = model.compress(image)
-reconstructions = model.decompress(compressed)
-```
-
-## Citation
-
-```bibtex
-@article{yang2025universal,
-    title={Progressive Compression with Universally Quantized Diffusion Models},
-    author={Yibo Yang and Justus Will and Stephan Mandt},
-    journal = {International Conference on Learning Representations},
-    year={2025}
-}
+---
+tags:
+- compression
+- diffusion
+license: mit
+datasets:
+- uoft-cs/cifar10
+- student/ImageNet-64
+metrics:
+- bpps
+- psnr
+---
+
+# Progressive Compression with Universally Quantized Diffusion Models
+
+Official implementation of our ICLR 2025 paper [Progressive Compression with Universally Quantized Diffusion Models](https://www.justuswill.com/uqdm/) by Yibo Yang, Justus Will, and Stephan Mandt.
+
+## TLDR
+
+Our new form of diffusion model, UQDM, enables practical progressive compression with an unconditional diffusion model - avoiding the computational intractability of Gaussian channel simulation by using universal quantization.
+
+## Setup
+
+```
+git clone https://github.com/mandt-lab/uqdm.git
+cd uqdm
+conda env create -f environment.yml
+conda activate uqdm
+```
+
+For working with ImageNet64, download from the [official website](https://image-net.org/download-images.php) the npz dataset files:
+- Train(64x64) part1, Train(64x64) part2, Val(64x64)
+
+and place them in `./data/imagenet64`. Our implementation removes the duplicate test images as saved in `./data/imagenet64/removed.npy` during loading.
+
+## Usage
+
+Load pretrained models by placing the `config.json` and `checkpoint.pt` in a shared folder and load them for example via
+```python
+from uqdm import load_checkpoint, load_data
+model = load_checkpoint('checkpoints/uqdm-tiny')
+train_iter, eval_iter = load_data('ImageNet64', model.config.data)
+```
+
+To train or evaluate call respectively via
+
+```python
+model.trainer(train_iter, eval_iter)
+model.evaluate(eval_iter)
+```
+
+To save the compressed representation of an image and to reconstruct an image/images from their compressed representations, use
+
+```python
+image = next(iter(eval_iter))
+compressed = model.compress(image)
+reconstructions = model.decompress(compressed)
+```
+
+## Citation
+
+```bibtex
+@article{yang2025universal,
+    title={Progressive Compression with Universally Quantized Diffusion Models},
+    author={Yibo Yang and Justus Will and Stephan Mandt},
+    journal = {International Conference on Learning Representations},
+    year={2025}
+}
 ```