Upload README.md

README.md

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+# StyleGAN2-ADA Pipeline for Image Projection
+
+This guide provides a step-by-step explanation of how to align a face image, project it into the latent space of StyleGAN2-ADA, and visualize the results.
+
+## Requirements
+
+### Dependencies
+- Python 3.7+
+- PyTorch
+- Required libraries installed via `requirements.txt` in the repository
+- Kaggle environment with internet enabled
+
+### Models and Methods Used
+- **Face Alignment:** `align_images.py` uses the `shape_predictor_68_face_landmarks.dat` model from DLib for precise facial alignment.
+- **Image Projection:** `projector.py` projects an aligned image into the latent space of StyleGAN2 using a pre-trained model (`ffhq.pkl` from NVIDIA Labs).
+- **Pre-trained Models:**
+  - Face landmark model: `shape_predictor_68_face_landmarks.dat`
+  - StyleGAN2-ADA pre-trained weights: `ffhq.pkl`
+
+---
+
+## Step-by-Step Execution
+
+### 1. Clone the Repository
+Clone the repository for StyleGAN2-ADA:
+```bash
+!git clone https://github.com/rkuo2000/stylegan2-ada-pytorch.git
+%cd stylegan2-ada-pytorch
+```
+
+### 2. Prepare the Raw Images
+Create a directory for raw images and copy the desired file:
+```bash
+!mkdir -p raw
+!cp /kaggle/input/test-notebook-images/profile-image.jpg raw/example.jpg
+```
+Verify the file:
+```bash
+!ls raw
+```
+
+### 3. Align the Face Image
+Run the face alignment script:
+```bash
+!python align_images.py raw aligned
+```
+- **Input:** `raw/example.jpg`
+- **Output:** Aligned image saved as `aligned/example_01.png`
+
+### 4. Verify Alignment
+List the aligned directory to confirm output:
+```bash
+!ls aligned
+```
+
+### 5. Project the Image into Latent Space
+Run the projection script:
+```bash
+!python projector.py --outdir=out --target=aligned/example_01.png \
+    --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl
+```
+- **Output:**
+  - Latent space projection results saved in the `out/` directory
+  - A video (`proj.mp4`) showing optimization progress
+
+---
+
+## Viewing Results
+
+### 1. Inline Video Playback
+Use the following command to view the progress video inline:
+```python
+from IPython.display import Video
+Video('out/proj.mp4', embed=True)
+```
+
+### 2. Download the Video
+To download the video file, use:
+```python
+from IPython.display import FileLink
+FileLink('out/proj.mp4')
+```
+Click the generated link to download `proj.mp4` to your local machine.
+
+---
+
+## Adding Gradio for Runtime Image Upload
+You can integrate Gradio to allow users to upload a photo and generate the GAN output (image and video) on runtime. Here is how to modify the pipeline:
+
+### Install Gradio
+```bash
+!pip install gradio
+```
+
+### Update the Code
+Add the following Python script to create a Gradio interface:
+
+```python
+import gradio as gr
+import subprocess
+from PIL import Image
+
+def process_image(input_image):
+    # Save the input image to raw directory
+    input_path = "raw/input_image.jpg"
+    input_image.save(input_path)
+
+    # Align the face
+    subprocess.run(["python", "align_images.py", "raw", "aligned"])
+
+    # Run projection
+    subprocess.run([
+        "python", "projector.py", "--outdir=out", "--target=aligned/input_image_01.png", \
+        "--network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl"
+    ])
+
+    # Load generated image and video
+    output_image_path = "out/proj.png"  # Adjust if necessary
+    output_video_path = "out/proj.mp4"
+    output_image = Image.open(output_image_path)
+
+    return output_image, output_video_path
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=process_image,
+    inputs=[gr.Image(type="pil", label="Upload an Image")],
+    outputs=[
+        gr.Image(type="pil", label="Generated Image"),
+        gr.Video(label="Projection Video")
+    ],
+    title="StyleGAN2-ADA Image Projection",
+    description="Upload a face image to generate GAN output and projection video."
+)
+
+demo.launch()
+```
+
+### Running the Gradio Interface
+Save the above script and run it in your environment. A Gradio web interface will open, allowing users to upload images and see the generated results in real time.
+
+---
+
+## Notes
+1. Ensure the internet is enabled in your Kaggle notebook for downloading required models.
+2. Verify input paths to match your dataset and file structure.
+3. Outputs are saved in the following structure:
+   - `raw/`: Original images
+   - `aligned/`: Aligned face images
+   - `out/`: Projection results and video
+
+---
+
+## Acknowledgments
+- StyleGAN2-ADA by NVIDIA Labs: [GitHub Repository](https://github.com/NVlabs/stylegan2-ada-pytorch)
+- DLib for face alignment: [DLib Library](http://dlib.net/)
+