Update README

README.md

@@ -9,7 +9,7 @@ language:
 ## Model Summary
 
 
-**HistAug** is a lightweight transformer-based generator for **controllable latent-space augmentations** in the feature space of the [H-optimus-1 foundation model](https://huggingface.co/bioptimus/H-optimus-1). Instead of applying costly image-space augmentations on millions of WSI patches, HistAug operates **directly on patch embeddings** extracted from a given foundation model(here H-optimus-1). By conditioning on explicit transformation parameters (e.g., hue shift, erosion, HED color transform), HistAug generates realistic augmented embeddings while preserving semantic content. In practice, the H-optimus-1 variant of HistAug can reconstruct the corresponding ground-truth augmented embeddings with an average cosine similarity of **about 90%** at **10X, 20X, and 40X magnification**.
+**HistAug** is a lightweight transformer-based generator for **controllable latent-space augmentations** in the feature space of the [H-optimus-1 foundation model](https://huggingface.co/bioptimus/H-optimus-1). Instead of applying costly image-space augmentations on millions of WSI patches, HistAug operates **directly on patch embeddings** extracted from a given foundation model(here H-optimus-1). By conditioning on explicit transformation parameters (e.g., hue shift, erosion, HED color transform), HistAug generates realistic augmented embeddings while preserving semantic content. In practice, the H-optimus-1 variant of HistAug can reconstruct the corresponding ground-truth augmented embeddings with an average cosine similarity of **about 82%** at **10X, 20X, and 40X magnification**.
 
 This enables training of Multiple Instance Learning (MIL) models with:  
 - ⚡ **Fast augmentation** 
@@ -195,39 +195,52 @@ for bag_features, label in loader:  # bag_features: (num_patches, embed_dim)
 
 ## Offline usage (HPC clusters without internet)
 
-If your cluster doesn’t allow internet access on compute nodes, you have two ways to use HistAug offline:
+If compute nodes don’t have internet, **always** run jobs with the offline flags to **prevent unnecessary network calls** and force local loads:
 
-1. **Rely on the cache**: pre-download the model on the front-end/login node (with internet), so it’s cached locally, then run jobs offline:
+```bash
+# On your compute job (no internet):
+export HF_HUB_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+```
+
+Prepare the model **in advance** on a front-end/login node (with internet), then choose **either** approach below.
+
+### Option — Warm the cache (simplest)
 
 ```bash
 # On the front-end/login node (with internet):
 python -c "from transformers import AutoModel; AutoModel.from_pretrained('sofieneb/histaug-hoptimus1', trust_remote_code=True)"
+```
 
-# On your compute job (no internet):
-export HF_HUB_OFFLINE=1
-export TRANSFORMERS_OFFLINE=1
-````
+Then in your offline job/script:
 
-This prevents unnecessary network calls and ensures `transformers` loads HistAug from the local cache.
+```python
+from transformers import AutoModel
+model = AutoModel.from_pretrained(
+    "sofieneb/histaug-hoptimus1",
+    trust_remote_code=True,
+    local_files_only=True,  # uses local cache only
+)
+```
 
-2. **Explicitly download with `hf download`**: instead of relying on the cache variables above, you can download the model files manually on the front-end/login node (with internet) and always point to the local folder:
+### Option — Download to a local folder with `hf download`
 
 ```bash
 # On the front-end/login node (with internet):
 hf download sofieneb/histaug-hoptimus1 --local-dir ./histaug-hoptimus1
 ```
 
-Then load the model from that directory in your script:
+Then in your offline job/script:
 
 ```python
 from transformers import AutoModel
-
 cross_transformer = AutoModel.from_pretrained(
     "./histaug-hoptimus1",   # local path instead of hub ID
     trust_remote_code=True,
-    local_files_only=True
+    local_files_only=True,  # uses local files only
 )
 ```
+
 ---
 ## Citation
 If our work contributes to your research, or if you incorporate part of this code, please consider citing our paper: