Are you tired of spending hours in Photoshop trying to perfect your images? Do you wish there was a faster and more intuitive way to edit your photos? Well, your wish has been granted with DragGAN! In this blog, we will explore the fascinating world of DragGAN, an open-source AI photo editing tool that allows you to manipulate and edit images effortlessly. Get ready to be amazed!
But first, let’s take a moment to appreciate the incredible advancements in AI technology that have made DragGAN possible. Developed by researchers from Google, the Max Planck Institute of Informatics, and MIT CSAIL, DragGAN utilizes a cutting-edge technique called Generative Adversarial Network (GAN).
What is DragGAN?
DragGAN is an innovative AI-based photo editing tool that allows users to interactively manipulate images with exceptional precision. By “dragging” specific points in the image, users can control the pose, shape, expression, and layout of various objects. The system leverages the power of generative adversarial networks (GANs) and features a motion supervision mechanism and point-tracking approach.
With DragGAN, anyone can deform images and achieve realistic outputs, even in challenging scenarios. It offers superior control and flexibility compared to previous methods and showcases its effectiveness in both image manipulation and point-tracking tasks. DragGAN revolutionizes the controllability of GANs, providing a powerful tool for synthesizing visually appealing content.
Now, let’s dive into the exciting features and functionalities of DragGAN!
To truly grasp the power of DragGAN, let’s take a look at some captivating demo videos that showcase its capabilities. These videos will give you a glimpse of how this innovative deep-learning model works its magic on various types of images.
As you can see, DragGAN allows you to achieve remarkable transformations with just a few clicks. Whether you want to change the perspective of an image or turn a neutral face into a laughing one, DragGAN does it all in a matter of seconds. It even works its magic on natural landscapes, opening up endless possibilities for image editing.
If you’re eager to explore more about DragGAN and its potential, head over to DragGAN’s official website. There, you’ll find an array of demos and the DragGAN research paper, which delves deeper into the innovative work behind this AI tool. Keep an eye on their website for updates and releases.
How DragGAN Works?
When it comes to synthesizing visual content, it’s crucial to have precise control over the pose, shape, expression, and layout of the generated objects. Existing methods often rely on annotated training data or 3D models, but they lack the flexibility, precision, and generality required for effective control. That’s where DragGAN comes into play.
DragGAN takes a unique approach to control generative adversarial networks (GANs) by allowing users to interactively “drag” points on an image to achieve precise target positions. This user-interactive method gives you unparalleled control over the manipulation of images.
So, how does DragGAN achieve this level of control? It consists of two main components:
- Feature-Based Motion Supervision: DragGAN utilizes a feature-based motion supervision technique to guide the movement of handle points toward their target positions. This mechanism ensures that the desired transformations are accurately applied to the image.
- Discriminative GAN Feature Point Tracking: To keep track of the handle points’ positions during the dragging process, DragGAN employs a novel point-tracking approach that leverages the discriminative features of the GAN. This enables precise localization of the handle points, even in complex scenarios.
By using these components, DragGAN empowers anyone to deform images with exceptional control over the movement of pixels. This level of precision enables manipulation of the pose, shape, expression, and layout of various categories, including animals, cars, humans, landscapes, and more.
The transformations performed on the learned generative image manifold of the GAN result in remarkably realistic outputs, even when faced with challenges like hallucinating occluded content or deforming shapes that maintain object rigidity.
Download and Setup DragGAN
Excited to get your hands on DragGAN and try it out for yourself? While the official code for DragGAN is not yet released, you can still experience its power through an unofficial version.
Let’s explore two ways you can use DragGAN for image editing: without coding and with coding.
DragGAN without Coding
If you’re not keen on diving into coding, don’t worry! DragGAN provides an official application deployed on Huggingface that allows you to harness its AI-based image editing capabilities without writing a single line of code.
Let’s learn how you can set up DragGAN for a seamless editing experience.
Step 1: Select the Model
The first step is to choose the appropriate pre-trained model, also known as the StyleGAN2 model, based on the type of image you’re working with. Currently, the available options are focused on human faces and dog pictures:
- stylegan_human_v2_512: The pre-trained model for human pictures.
- stylegan2_dogs_1024_pytorch: The pre-trained model for dog pictures.
Step 2: Plant the Seed
By changing the seed number, you can generate different variations of the image according to the selected model. Feel free to experiment and see the magic unfold!
Step 3: Step Size
Adjust the step size according to your preference. A lower step size allows you to observe each frame of the drag process, while a higher step size speeds up the process. Find the perfect balance that suits your editing needs.
Step 4: Latent Space
Choose between optimizing the w space or
w+ space. Opting for the
w space may have a greater influence on the image, while the
w+ space generally produces better results at a slower pace. Keep in mind that changing the latent space will reset the image, points, and mask.
Step 5: Add Points
Now it’s time to interact with the image! Select the starting and ending points on the image that you want to manipulate. These points will serve as the foundation for your editing process.
Step 6: Start the Drag
With the points in place, click the start button to initiate the drag process. Sit back and watch as DragGAN works its magic in real-time, transforming the image according to your desired modifications.
Step 7: Stop
If you’re satisfied with the output and want to halt the editing process, simply click the stop button. You have full control over when to conclude the drag optimization.
Step 8: Reset the Points
Feel free to reset the previous points by clicking the reset button. This allows you to start afresh and add new points for further editing.
Step 9: Edit Flexible Area
By clicking on “Edit Flexible Area,” you can create a mask to specify the region that should remain unchanged during the editing process. Add source and destination points within the flexible area by clicking on the “Add Points” button and commence the editing process by starting the drag.
Step 10: Reset the Image
If you ever want to return to the initial state of the image and discard all modifications, simply hit the reset button. This gives you a clean slate to begin anew.
DragGAN with Coding
Now that you’re excited to experience the power of DragGAN, let’s dive into the process of setting it up with coding. Don’t worry, it’s not as complex as it sounds. Just follow the steps below, and you’ll be ready to unleash the capabilities of DragGAN on your machine.
Step 1: Install the Required Dependencies
To begin, make sure you have cloned the repository and the necessary dependencies are installed on your system.
Installation Using CUDA
If you have a CUDA graphics card, you can follow the requirements specified by NVlabs/stylegan3. Simply run the following commands, and they will take care of setting up the correct CUDA version and all the required Python packages:
$ conda env create -f environment.yml $ conda activate stylegan3
Once you’ve activated the stylegan3 environment, you can proceed to install the additional requirements using the following command:
$ pip install -r requirements.txt
Installation Without CUDA
In case you don’t have a CUDA graphics card or you’re using GPU acceleration on MacOS with Silicon Mac M1/M2, or if you simply want to run DragGAN on CPU, try the alternative steps below:
$ cat environment.yml | \ grep -v -E 'nvidia|cuda' > environment-no-nvidia.yml && \ conda env create -f environment-no-nvidia.yml $ conda activate stylegan3
If you’re using MacOS, it’s important to set the following environment variable for compatibility:
$ export PYTORCH_ENABLE_MPS_FALLBACK=1
Step 2: Download Pre-trained StyleGAN2 Weights
To utilize the power of DragGAN, you’ll need to download pre-trained StyleGAN2 weights. Simply run the following command to download the weights:
$ python scripts/download_model.py
If you’re interested in trying out other pre-trained StyleGAN models such as StyleGAN-Human or the Landscapes HQ (LHQ) dataset, you can download the respective weights from the provided links. Make sure to place the downloaded weights under the
Step 3: Run the DragGAN GUI
Now, let’s start the DragGAN GUI, which allows you to edit GAN-generated images. Please note that editing real images requires GAN inversion using tools like PTI, followed by loading the new latent code and model weights into the GUI.
To start the DragGAN GUI, run the appropriate script based on your operating system. If you’re using Windows, use the following command:
For other operating systems, run:
$ sh scripts/gui.sh
And that’s it! The DragGAN GUI will be up and running, ready for you to explore the fascinating world of image editing powered by AI.
Additionally, you can also try the DragGAN Gradio demo, which works universally on both Windows and Linux systems. Simply run the following command:
$ python visualizer_drag_gradio.py
Now that you have successfully set up DragGAN with coding, it’s time to unleash your creativity and explore the endless possibilities of image editing with this revolutionary AI tool. Get ready to witness the transformative power of DragGAN in your hands!
Run DragGAN Visualizer in Docker
If you prefer to try out the DragGAN visualizer in a Docker environment, here’s how you can quickly get started. Please note that the provided Docker image is based on the NGC PyTorch repository and requires about 25GB of disk space.
First, build the Docker image by running the following command:
$ docker build . -t draggan:latest
Once the image is built, you can run the Docker container with the following command:
$ docker run -p 7860:7860 -v "$PWD":/workspace/src -it draggan:latest bash
After entering the container, navigate to the source directory and start the Gradio visualizer by running the following commands:
$ cd src $ python visualizer_drag_gradio.py --listen
The Gradio visualizer will provide a shared link, which you can open in your browser. Keep in mind that the visualizer may take a few moments to load due to the computational requirements.
Read: How to use ChatGPT API?
The emergence of DragGAN has revolutionized image editing by offering a fast and intuitive way to manipulate images using AI. With DragGAN’s ability to generate highly realistic images and its user-friendly interface, it has the potential to transform the image editing landscape. While DragGAN is currently in beta mode but its official code has been released, the official version allows users to experience the power of this innovative AI tool.
Whether you choose to explore DragGAN without coding through the Huggingface application or run it on your local system using the provided code, you can unleash your creativity and witness the incredible transformations that DragGAN can achieve.
Remember to stay updated with the official website for demos, releases, and more information about this groundbreaking AI image editing tool.
So, what are you waiting for? Dive into the world of DragGAN and experience the magic of AI-driven image editing like never before!
Frequently Asked Questions (FAQs)
DragGAN is an innovative AI photo editing tool that allows users to manipulate and edit images with unprecedented control. It utilizes a unique approach where users can interactively “drag” points on an image to achieve precise target positions, enabling transformations in pose, shape, expression, and layout.
DragGAN consists of feature-based motion supervision and discriminative GAN feature point tracking. The feature-based motion supervision guides handle points to their target positions, while the discriminative GAN feature point tracking ensures precise localization. These components empower users to achieve accurate and realistic image transformations.
DragGAN is being developed by researchers from Google, the Max Planck Institute of Informatics, and MIT CSAIL.
DragGAN utilizes a technique called Generative Adversarial Network (GAN) to achieve its image manipulation capabilities.