Add pipeline tag, paper link, and sample usage

README.md

@@ -1,6 +1,7 @@
 ---
-license: mit
 library_name: pytorch
+license: mit
+pipeline_tag: unconditional-image-generation
 tags:
 - hdtree
 - pytorch
@@ -11,9 +12,12 @@ tags:
 
 # HDTree ICML Checkpoints
 
-This repository hosts pretrained checkpoints for the public HDTree ICML release.
+This repository hosts pretrained checkpoints for the model presented in the paper [HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference](https://huggingface.co/papers/2506.23287).
 
-Code: https://github.com/zangzelin/code_HDTree_icml
+HDTree is a generative modeling framework designed for robust lineage inference. It captures tree relationships within a hierarchical latent space using a unified hierarchical codebook and employs a quantized diffusion process to model continuous cell state transitions.
+
+- **Code:** [https://github.com/zangzelin/code_HDTree_icml](https://github.com/zangzelin/code_HDTree_icml)
+- **Project Page:** [https://zangzelin.github.io/code_HDTree_icml/](https://zangzelin.github.io/code_HDTree_icml/)
 
 ## Files
 
@@ -22,6 +26,25 @@ Code: https://github.com/zangzelin/code_HDTree_icml
 | `checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth` | MNIST | `configs/mnist.yaml` | Best MNIST checkpoint from the full run by checkpoint validation accuracy. |
 | `checkpoints/limb/hdtree_limb_i10_epoch199_acc0.53921.pth` | Limb | `configs/limb.yaml` default | Limb sweep i10/default checkpoint. |
 
+## Sample Usage
+
+To validate a trained checkpoint using the official code, you can use the provided validation script:
+
+```bash
+# Example for MNIST
+bash scripts/validate_checkpoint.sh mnist checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth
+```
+
+To compute reconstruction and log-likelihood with diffusion sampling, enable generation using the following command:
+
+```bash
+python main.py validate \
+  -c configs/mnist.yaml \
+  --model.init_args.ckpt_path=checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth \
+  --model.init_args.training_str=step2_r \
+  --model.init_args.gen_data_bool=True
+```
+
 ## Reported Metrics
 
 MNIST full run summary:
@@ -42,15 +65,9 @@ The included `logs/` files contain the original run outputs used to record these
 
 ```bash
 pip install huggingface_hub
-huggingface-cli download zangzelin/HDTree-ICML-checkpoints --local-dir .
-```
-
-Then use the public code repository's validation script, for example:
-
-```bash
-bash scripts/validate_checkpoint.sh checkpoints/limb/hdtree_limb_i10_epoch199_acc0.53921.pth
+huggingface-cli download zelinzang/HDTree-ICML-checkpoints --local-dir .
 ```
 
 ## Checksums
 
-See `SHA256SUMS`.
+See `SHA256SUMS`.