Style Transfer in Lens Studio: A Comprehensive Guide with Fritz AI

Unlock the power of style transfer in Lens Studio, a popular and accessible machine learning task for lens creators! Style transfer, a computer vision technique, lets you recompose one image's content in another's style. This allows you to create artistic and unique augmented reality lenses. This guide explores how to train a style transfer model with Fritz AI and use it within Lens Studio's segmentation template for stunning visual effects.

What is Style Transfer?

Style transfer is a powerful computer vision technique that allows you to transform the visual style of one image using the style of another

. Think of it as repainting a photograph in the style of Van Gogh or turning a video into a living watercolor painting. Style transfer algorithms analyze the content and style of two images, then synthesize a new image that combines the content of the first with the style of the Second.

This opens up a world of creative possibilities for lens creators. Imagine applying the artistic style of Monet or Picasso to live camera feeds or transforming everyday objects into surreal works of art. This makes it a prime way to create lenses.

Key components:

• Content image: The image whose content (objects, scene) will be preserved.
• Style image: The image whose style (texture, color palette, brushstrokes) will be transferred.
• Output image: The resulting image, exhibiting the content of the content image and the style of the style image.

Style transfer leverages deep learning models, often convolutional neural networks (CNNs), to extract and recombine image features related to content and style. This sophisticated process makes it simpler to create cool AR lenses and filters.

Lens Studio: Your AR Creation Hub

Lens Studio is Snapchat's powerful desktop application for building augmented reality (AR) lenses and filters . It provides an intuitive interface, scripting capabilities, and a wide range of tools for creating interactive and engaging AR experiences.

Lens Studio empowers creators to:

• Design custom AR lenses and filters for Snapchat.
• Utilize face tracking, object recognition, and other advanced features.
• Share lenses with the world through Snapchat Snapcodes.
• Leverage machine learning (ML) models to enhance lens functionality.

Lens Studio's support for custom ML models, including style transfer models trained with Fritz AI, expands the creative horizons for lens creators. This integration lets you experiment with advanced AI techniques to craft unique and sophisticated AR experiences. Adding Machine learning is a great way to further expand how much you can do with your lens.

Training Style Transfer Models with Fritz AI

Fritz AI simplifies the process of training style transfer models, making it accessible to creators with limited ML experience.

Training a style transfer model is Simplified as this model only requires a single dataset image known as a 'style image'. The Fritz AI platform provides a user-friendly web app for training custom models, along with pre-trained models and other valuable resources.

Fritz AI offers:

• A web-based training platform: Train custom style transfer models without coding.
• Pre-trained models: Leverage readily available models for various ML tasks.
• SDK Documentation: Get to know how to best utilize the software.
• SDK Documentation: Access in depth guides, tutorials and other help guides.

• SDK Documentation: Allows access to other android and ios demo apps.

  The need for only one data set image to train the model is in stark contrast to object detection and segmentation, as these require thousands of datasets for training.

Using Fritz AI, creators can train style transfer models with just a single style image, eliminating the need for extensive datasets and complex coding . This dramatically reduces the barrier to entry for ML in lens creation.

Choosing the Right Style Image for Optimal Results

Not all style images are created equal! The quality and characteristics of your style image significantly impact the resulting style transfer effect

. For best results, select images with:

• Large geometric Patterns: Clear and distinct shapes will transfer effectively.
• Bold, contrasting color palettes: Vibrant and contrasting colors yield striking results.
• Strong edges and textures: Defined edges and textures contribute to a more pronounced style transfer.

Images that are 512 by 512 pixels in size tend to have the best results.

Style Image Best Practices

• Large geometric patterns
• Bold, contrasting color palettes
• Strong edges and textures

Our style transfer guide provides detailed recommendations for choosing the right style images for each project.

Consider what type of effect you are trying to make when utilizing a style transfer model. Do you want a bold, geometric look, or is your goal more of a texture focused outcome? Be sure to properly plan for what you expect to see from the effect

.

Adjusting Training Parameters for Fine-Tuning

Fritz AI provides adjustable training parameters that allow fine-tuning the style transfer model's behavior to achieve the desired aesthetic . These parameters control the relative influence of content and style, as well as other aspects of the output image.

Key parameters include:

*   **Style weight:**  The strength of the style transfer (higher = more pronounced style).
*   **Content weight:** The degree to which the original content is preserved (higher = more content retention).
*   **Total Variation weight:** A value to give a much smoother stylized image by washing out minor textures.
*   **Stability weight:**  Stabilizes the videos, but this can reduce minor textures as well.

Changing the parameter values is the best way to change the aesthetic of the model. There are 4 loss terms, Style Weight, Content Weight, Total Variation Weight, and Stability Weight . Here is a more detailed description of these weights:

• Style Weight: This parameter controls the amount of style that will be borrowed from the image. If you are trying to create a very geometric look from a photograph image, you will want to up this level quite a bit.
• Content Weight:This is going to represent how much of the cameras original content will be maintained. A higher weight indicates more of the original output and content, which is ideal if you don't want to drastically change the way that the camera looks.
• Total Variation Weight:This parameter will allow you to have a smoother Stylized image. With this, you will be able to wash out many of the smaller textures.
• Stability Weight: The amount that the model will stabilize the videos. The higher the value, the more stabilized that the videos will be .

By experimenting with these parameters, creators can customize their models to meet unique stylistic goals. In general, it is better to test using only one or two of these parameters before fully investing a lot of time.

Exporting and Implementing the Style Transfer Model in Lens Studio

Once the style transfer model is trained, it can be easily exported from Fritz AI and implemented within Lens Studio.

Fritz will allow you to download and implement your file. Simply download it and then implement it via the lens studio. Exporting and implementing your model is simple and fast.

Step 1: Train a Style Transfer Model with Fritz AI

To start, you need a trained style transfer model. Create a new Fritz AI project and choose 'Style Transfer' as the project type. Upload your chosen style image and adjust the training parameters to fine-tune the model

. Then, train your model within the project.

Keep in mind that not all style images are equal, check the previous section to find out what styles perform best for the model.

Things to consider for your model:

• Choose to apply style to full camera scene
• Choose a project type
• Define project name

Step 2: Exporting the Trained Model as a Standalone File

Once the email confirmation is received that your project has finished training, navigate to the 'Models' tab in your Fritz AI project . Export the model as a standalone ONNX file ('.onnx'). It is crucial that you don't export the lens studio project.

Download an onnx file ONNX (Open Neural Network Exchange) is an open format that allows AI models to be transferred between frameworks.

Step 3: Importing and Configuring the ML Component in Lens Studio

In lens studio, you are going to want to begin a segmentation template project, you can begin this project from scratch. Once you open this project you will then need to implement an ml component from the top left corner. From there select the training model you just downloaded. Take the file and import it. It should then be set under the camera object.

Now set the output texture and input texture. The input is going to be the device camera and it should be set as 'NONE'. Set to device camera by clicking the camera object

.

Step 4: Editing the Segmentation Controller and Implementing

There should be two images: the image and the device camera texture. This portion involves the segmentation and editing of it to utilize the camera texture as its input. To accomplish this you will need to make a change so that there are effects. Select device camera texture from the segmentation, so that a tile panel floats in the background

.

You will also need to uncheck the tile option, when you do, you will be able to see this as a result from the preview image. Now the effect should work!

Now to get this model working you are going to want to uncheck the tile option, this might be the most confusing section when you are first working with it.