# LLaVA-Deepfake Model

## Overview
The `LLaVA-Deepfake` model is a fine-tuned version of `LLaVA-v1.5-13B`, specifically designed for detecting and analyzing deepfake images. This multimodal large language model (MLLM) not only identifies whether an image is a deepfake but also provides detailed explanations of manipulated areas, highlighting specific features such as irregularities in the eyes, mouth, or overall facial texture. The model leverages advanced vision and language processing capabilities, making it a powerful tool for forensic deepfake detection.

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## Installation

Follow these steps to set up and run the `LLaVA-Deepfake` model:

### Step 1: Clone the Repository
Start by cloning the model repository:
```bash
git clone https://huggingface.co/pou876/llava-deepfake-model
cd llava-deepfake-model
```


### Step 2: Create a Python Environment
Set up a dedicated Python environment for running the model:
```bash
conda create -n llava_deepfake python=3.10 -y
conda activate llava_deepfake
pip install --upgrade pip
pip install -r requirements.txt
```

## Running the Model

### Step 1: Start the Controller
The controller manages the communication between components:
```bash
python -m llava.serve.controller --host 0.0.0.0 --port 10000
```

### Step 2: Start the Model Worker
The worker loads the deepfake detection model and processes inference requests:
```bash
python -m llava.serve.model_worker --host 0.0.0.0 \
    --controller http://localhost:10000 --port 40000 \
    --worker http://localhost:40000 \
    --model-path ./llava-deepfake-model --load-4bit
```

### Step 3: Start the Gradio Web Server
The Gradio web server provides a user-friendly interface for interacting with the model:
```bash
python -m llava.serve.gradio_web_server \
    --controller http://localhost:10000 --model-list-mode reload --share
```
Once the web server is running, a URL (e.g., http://127.0.0.1:7860) will be generated. Open this link in your browser to start using the interface.