Facial-Style-Transfer-for-Realistic-Artwork-Generation

Stylize Script

This script allows you to stylize images using a pre-trained model. It takes command-line arguments to specify the input image, the checkpoint file containing the pre-trained model, and the path to save the stylized image.

Requirements

Python 3.x
torch
opencv-python
torchvision

Usage

Clone the repository:

$ git clone https://github.com/mahdid-lilia/Facial-Style-Transfer-for-Realistic-Artwork-Generation.git
$ cd Facial-Style-Transfer-for-Realistic-Artwork-Generation

Run the script with the following command:
```
python setup.py <option> <arguments>
```
Replace <option> and <arguments> with the appropriate values:
- <option>: Specify the option for stylizing the image. Currently, options supported are single_image and folder.
- <arguments>:
  - --content: Path to the content image (for single image stylization) or folder path containing images (for batch stylization).
  - --checkpoint: Path to the checkpoint file containing the pre-trained model.
  - --save: Path to save the stylized image or folder path to save stylized images.
  - --model: Specify the model name for stylization.
  - Additional arguments specific to the chosen model (e.g., alpha for TransformerResNextNetwork_Pruned model).

Example

Stylize a single image:

content_path="/workspaces/Facial-Style-Transfer-for-Realistic-Artwork-Generation/data/test/test_1.jpeg"
checkpoint_path="/workspaces/Facial-Style-Transfer-for-Realistic-Artwork-Generation/models/3. TransformerResNextNetwork_Pruned03/checkpoints/checkpoint_d1500.pth"
output_path="image.png"
model_name="TransformerResNextNetwork_Pruned"
alpha=0.3

python setup.py single_image --content "$content_path" --checkpoint "$checkpoint_path" --save "$output_path" --model "$model_name" "$alpha"

Stylize images in a folder:

folder_path="/workspaces/Facial-Style-Transfer-for-Realistic-Artwork-Generation/data/test/"
checkpoint_path="models/3. TransformerResNextNetwork_Pruned03/checkpoints/checkpoint_d1500.pth"
output_path="data/output/3. TransformerResNextNetwork_Pruned03/"
model_name="TransformerResNextNetwork_Pruned"
alpha=0.3

python setup.py folder --folder "$folder_path" --checkpoint "$checkpoint_path" --save "$output_path" --model "$model_name" "$alpha"