How to install DragGAN in google colab?

To install DragGAN in Google Colab, follow these steps:

1. Change the runtime type to GPU. This is necessary because DragGAN requires a GPU to run. To do this, click on the Runtime menu and select Change runtime type. Under Hardware accelerator, select GPU. Click Save.
2. Connect to the runtime. Once the runtime type has been changed, click on the Connect button to connect to the runtime.
3. Clone the DragGAN repository. To do this, run the following command:

!git clone https://github.com/XingangPan/DragGAN.git

4. Navigate to the DragGAN directory. To do this, run the following command:

cd DragGAN

5. Install the required dependencies. To do this, run the following command:

!pip install -r requirements.txt

6. Download the pre-trained weights. To do this, run the following command:

!python download_weights.py

7. Launch DragGAN. To do this, run the following command:

python draggan.py

This will launch DragGAN in a new browser tab. You can then upload an image and start editing it.

Here is an example of a complete Google Colab notebook for installing and running DragGAN:

Python

# Change the runtime type to GPU.
!nvidia-smi

# Connect to the runtime.
!google.colab

# Clone the DragGAN repository.
!git clone https://github.com/XingangPan/DragGAN.git

# Navigate to the DragGAN directory.
cd DragGAN

# Install the required dependencies.
!pip install -r requirements.txt

# Download the pre-trained weights.
!python download_weights.py

# Launch DragGAN.
python draggan.py

Once DragGAN has launched, you can upload an image and start editing it. To do this, click on the Upload image button and select the image you want to edit. You can then start dragging points on the image to manipulate it.

Here are some tips for using DragGAN:

• Use high-resolution images for best results.
• Start by editing small changes and gradually make larger changes.
• Use the preview button to see how your changes will look on the entire image.
• If you make a mistake, you can always undo your changes.
• Experiment and have fun! DragGAN is a powerful tool for creative expression.

Other Ways to do same process:

To install DrgGAN AI in Google Colab, follow these steps:

1. Open a new Google Colab notebook.
2. Change the runtime type to GPU. To do this, go to Runtime > Change runtime type and select GPU.
3. Clone the DrgGAN AI repository. To do this, run the following command in a code cell:

Python

!git clone https://github.com/XingangPan/DrgGAN.git

4. Navigate to the DrgGAN AI directory. To do this, run the following command in a code cell:

Python

cd DrgGAN

5. Install the required dependencies. To do this, run the following command in a code cell:

Python

!pip install -r requirements.txt

6. Download the pre-trained weights. To do this, run the following command in a code cell:

Python

!python download_weights.py

Once you have completed these steps, you will have successfully installed DrgGAN AI in Google Colab.

To use DrgGAN AI, simply follow these steps:

1. Load the pre-trained weights. To do this, run the following code in a code cell:

Python

import torch
model = torch.load("pretrained_weights.pt")

2. Preprocess the input image. To do this, you can use the following code:

Python

from PIL import Image

def preprocess(image):
    image = image.resize((256, 256))
    image = image.convert("RGB")
    image = torch.from_numpy(np.array(image)).float() / 255.0
    image = image.unsqueeze(0)
    return image

input_image = preprocess(Image.open("input.jpg"))

3. Generate the output image. To do this, run the following code:

Python

with torch.no_grad():
    output_image = model(input_image)

4. Save the output image. To do this, you can use the following code:

Python

from PIL import Image

output_image = output_image[0].permute(1, 2, 0).clamp(0, 1).cpu().numpy()
output_image = Image.fromarray(np.uint8(output_image * 255.0))
output_image.save("output.jpg")

This will save the output image to a file called output.jpg.

You can also use the following code to generate an interactive image editing interface:

Python

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

app = dash.Dash()

app.layout = html.Div([
    html.Img(id="input-image", src="input.jpg"),
    html.Img(id="output-image", src=""),
    dcc.Slider(
        id="drag-point-x",
        min=0,
        max=255,
        value=128,
        marks={i: str(i) for i in range(0, 256, 32)}
    ),
    dcc.Slider(
        id="drag-point-y",
        min=0,
        max=255,
        value=128,
        marks={i: str(i) for i in range(0, 256, 32)}
    ),
    html.Button("Update", id="update-button"),
])

@app.callback(
    Output("output-image", "src"),
    [Input("drag-point-x", "value"), Input("drag-point-y", "value")],
    [State("input-image", "src")],
)
def update_image(drag_point_x, drag_point_y, input_image_src):
    input_image = preprocess(Image.open(input_image_src))
    output_image = model(input_image)

    # Apply the drag point transformation
    output_image = output_image[0].permute(1, 2, 0)
    output_image = output_image.clamp(0, 1).cpu().numpy()
    output_image = Image.fromarray(np.uint8(output_image * 255.0))

    # Draw the drag point on the output image
    output_image.draw(

Sources

1. https://www.datacamp.com/tutorial/learn-build-dash-python

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