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README.md

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-tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
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-
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# EHRXDiff
+Model card for our paper: [Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records](https://arxiv.org/abs/2409.07012). 
+We provide two versions of the **EHRXDiff** model:
+* **EHRXDiff** – trained without the null-based augmentation technique
+* **EHRXDiff<sub>w_null</sub>** – trained with the null-based augmentation technique.
+This card describes the **EHRXDiff** model.
+For implementation details, please refer to the [EHRXDiff repository](https://github.com/dek924/EHRXDiff).
+
+
+## Installation
+First, clone the repository and install the required packages:
+```
+git clone https://github.com/dek924/EHRXDiff.git
+
+pip install "pip<24.1"
+pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
+pip install -r requirements.txt
+```
+
+## Loading the model
+You can load the model directly in Python:
+```python
+from cheff.ldm.models.diffusion.ddpm_tab import EHRXDiff
+
+model = EHRXDiff.from_pretrained("dek924/ehrxdiff")
+model.eval()
+```
+Alternatively, you can download the weights via the Hugging Face Hub:
+
+```python
+from huggingface_hub import hf_hub_download
+
+wt_path = hf_hub_download("dek924/ehrxdiff", "pytorch_model.bin")
+```
+and then run the evaluation script included in our github repository (`scripts/eval.py`):
+```
+python scripts/eval.py \
+    --sdm_path=${CHECKPOINT_PATH}/pytorch_model.bin \
+    --save_dir=${CHECKPOINT_PATH}/images/seed${RAND_SEED} \
+    --img_meta_dir=${IMG_META_DIR} \   # Directory containing metadata for MIMIC-CXR-JPG
+    --img_root_dir=${IMG_ROOT_DIR} \   # Directory containing preprocessed images
+    --tab_root_dir=${TAB_ROOT_DIR} \   # Directory containing tabular data
+    --seed=${RAND_SEED} \
+    --batch_size=${BATCHSIZE}
+```