Edit README.md

README.md

@@ -19,60 +19,68 @@ metrics:
   - CER
 ---
 
-# VATr++ (Hugging Face Version)
+# VATr++ (Local Clone Version)
 
-This is a re-upload of the **VATr++** styled handwritten text generation model to the Hugging Face Model Hub. The original code and more detailed documentation can be found in the [VATr-pp GitHub repository](https://github.com/EDM-Research/VATr-pp). 
+This is a local-clone-friendly version of the **VATr++** styled handwritten text generation model. If you prefer not to rely on `transformers`’s `trust_remote_code=True`, you can simply clone this repository and load the model directly.
 
-> **Note**: Please refer to the original repo for:
+> **Note**: For:
 > - Full training instructions  
-> - In-depth code details  
-> - Extended usage and references  
+> - Advanced features (style cycle loss, punctuation modes, etc.)  
+> - Original code details  
 
-This Hugging Face version allows you to directly load the **VATr++** model with `AutoModel.from_pretrained(...)` and use it in your pipelines or scripts without manually handling checkpoints. The usage differs slightly from the original GitHub repository, primarily because we leverage Hugging Face’s `transformers` interface here.
+please see the [VATr-pp GitHub repository](https://github.com/EDM-Research/VATr-pp). This local version is intended primarily for inference and basic usage.
 
 ---
 
-## Installation
+## Installation & Setup
 
-1. **Create a conda environment (recommended)**:
+1. **Clone this repository (via Git LFS)**:
+   ```bash
+   git lfs clone https://huggingface.co/blowing-up-groundhogs/vatrpp
+   ```
+
+2. **Create (and activate) a conda environment (recommended)**:
    ```bash
    conda create --name vatr python=3.9
    conda activate vatr
    ```
 
-2. **Install PyTorch and CUDA (if available)**:
+3. **Install PyTorch (with CUDA if available)**:
    ```bash
-   conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
+   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
    ```
 
-3. **Install additional requirements** (including `transformers`, `opencv`, etc.):
+4. **Install additional requirements**:
    ```bash
-   pip install transformers opencv-python
+   pip install opencv-python matplotlib
    ```
-   *You may need to adjust or add libraries based on your specific environment needs.*
 
 ---
 
-## Loading the Model
+## Loading the Model Locally
+
+With the repository cloned, you can load either **VATr++** or the **original VATr** model locally.
+
+### **VATr++**
 
-#### **VATr++**
-To load the **VATr++** version:
 ```python
-from transformers import AutoModel
+from vatrpp import VATrPP
 
-model_vatr_pp = AutoModel.from_pretrained(
-    "blowing-up-groundhogs/vatrpp", 
-    trust_remote_code=True
+model_vatr_pp = VATrPP.from_pretrained(
+    "vatrpp",            # Local folder name or path
+    local_files_only=True
 )
 ```
 
-#### **VATr (original)**
-To load the **original VATr** model (instead of VATr++), specify the `subfolder` argument:
+### **VATr (original)**
+
 ```python
-model_vatr = AutoModel.from_pretrained(
-    "blowing-up-groundhogs/vatrpp",
-    subfolder="vatr", 
-    trust_remote_code=True
+from vatrpp import VATr
+
+model_vatr = VATr.from_pretrained(
+    "vatrpp",
+    local_files_only=True,
+    subfolder="vatr"     # Points to the original VATr checkpoint
 )
 ```
 
@@ -80,23 +88,21 @@ model_vatr = AutoModel.from_pretrained(
 
 ## Usage (Inference Example)
 
-Below is a **minimal** usage example that demonstrates how to:
+Below is a **minimal** usage example demonstrating how to:
 
-1. Load the VATr++ model from the Hugging Face Hub.  
+1. Load the **VATr++** model from your local clone.  
 2. Preprocess a style image (an image of handwriting).  
-3. Generate a new handwritten line of text in the style of the provided image.  
-
-> **Important**: This model requires `trust_remote_code=True` to properly load its custom generation logic.
+3. Generate new handwritten text in the style of the provided image.
 
 ```python
 import numpy as np
 from PIL import Image
 import torch
 from torchvision import transforms as T
-from transformers import AutoModel
 
 # 1. Load the model (VATr++)
-model = AutoModel.from_pretrained("blowing-up-groundhogs/vatrpp", trust_remote_code=True)
+from vatrpp import VATrPP
+model = VATrPP.from_pretrained("vatrpp", local_files_only=True)
 
 # 2. Helper functions to load and process style images
 def load_image(img, chunk_width=192):
@@ -168,27 +174,22 @@ generated_pil_image.save("generated_output.png")
 
 - **`style_imgs`**: A batch of fixed-width image chunks from your style reference. In practice, you can supply multiple small style samples or a single line image split into chunks.
 - **`gen_text`**: The text to render in the given style.
-- **`align_words`** and **`at_once`**: Optional arguments that control how the text is laid out and generated.
+- **`align_words`** and **`at_once`**: Optional arguments controlling how the text is laid out and generated.
 
 ---
 
 ## Original Repository
 
-This model is built upon the code from [**EDM-Research/VATr-pp**](https://github.com/EDM-Research/VATr-pp), which is itself an improvement on the [VATr](https://github.com/aimagelab/VATr) project. If you need to:
-- Train your own model from scratch
-- Explore advanced features (like style cycle loss, punctuation modes, or advanced augmentation)
-- Examine experimental details or replicate the original paper's setup
+This model is built upon the code from [**EDM-Research/VATr-pp**](https://github.com/EDM-Research/VATr-pp), itself an improvement on the [VATr](https://github.com/aimagelab/VATr) project. Please visit those repositories if you need to:
 
-Please visit the original GitHub repos for comprehensive documentation and support files.
+- Train your own model from scratch  
+- Explore advanced features (like style cycle loss, punctuation modes, or advanced augmentation)  
+- Examine experimental details or replicate the original paper's setup  
 
 ---
 
 ## License and Acknowledgments
 
 - The original code and model are under the license found in [the GitHub repository](https://github.com/EDM-Research/VATr-pp).  
-- All credit goes to the original authors and maintainers for creating VATr++ and releasing it openly.  
-- This Hugging Face re-upload is merely intended to **simplify inference** and **model sharing**; no changes have been made to the core training code or conceptual pipeline.
-
----
-
-**Enjoy generating styled handwritten text!** For any issues specific to this Hugging Face version, feel free to open an issue or pull request here. Otherwise, for deeper technical questions, please consult the original repository or its authors.
+- All credit goes to the original authors and maintainers for creating VATr++ and releasing it openly.
+- This local version is intended to simplify offline usage and keep everything self-contained.