One-Shot Face Stylization with JoJoGAN

Using JoJoGAN for artistic applications and image stylizing

One-Shot Face Stylization with JoJoGAN
Turn an input image into a cool stylized variant using JoJoGAN

Creating visually stunning, personalized, and stylized faces is no longer a job confined to expert artists or graphic designers. With JoJoGAN, you can now convert any face image into an artistic masterpiece with the click of a button. This guide is tailored to help you understand JoJoGAN’s features, capabilities, and how you can integrate it into your applications or creative projects.

In this guide, you'll find information on the use cases of JoJoGAN, technical implementation details, and limitations. We'll also cover the specific inputs and outputs required for the model. Lastly, a step-by-step walkthrough will guide you through the actual usage of the model via code.

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We'll also explore how you can use to find similar models to JoJoGAN and decide which one suits your needs the best. Let's dive in.

Use Cases and Target Audience

JoJoGAN is not just a tool for creating captivating images; it's a versatile asset for:

  • Artistic Applications: Artists can create unique and visually appealing portraits with ease.
  • Virtual Avatars: Developers in the gaming and virtual reality sectors can create highly customizable and realistic virtual characters.
  • Social Media Filters: Social media platforms can integrate JoJoGAN to offer personalized profile picture effects to their users.
  • Advertising and Marketing: Brands can stylize their ambassadors' faces in campaigns for higher visual impact.

The flexibility and efficiency of JoJoGAN make it ideal for artists, software developers, social media platforms, and marketing agencies.

About JoJoGAN

JoJoGAN is a deep-learning model designed for one-shot face stylization. Developed by mchong6, it's powered by a generative adversarial network (GAN). It takes an input image of a person's face and generates a stylized version based on a given reference image. The model leverages a blend of perceptual loss and identity loss to produce outputs that are visually compelling while remaining true to the individual's identity. You can find more details about this on its creator’s page and model details page.

Technical Implementation

The model is implemented using Nvidia T4 GPUs and has an average runtime of 14 seconds per run, with a cost of $0.0077 USD per run. Technically, the model is impressive because it employs GAN architecture with a blend of perceptual and identity loss functions. This unique combination ensures that the output images are both aesthetically pleasing and accurate in terms of identity.


While JoJoGAN offers a lot, there are some limitations to be aware of:

  • Limited to Facial Images: The model is specifically designed for stylizing faces; it is not suitable for full-body or non-facial images.
  • Style Constraints: The output is constrained by the style of the reference image provided.
  • Resource Intensive: Requires a powerful GPU for optimal performance.

Understanding the Inputs and Outputs of JoJoGAN

Before diving into the usage guide, let's understand what JoJoGAN requires as inputs and what it provides as outputs.


  • input_face: A file containing the photo of the human face you want to stylize.
  • pretrained: A string identifier of a pretrained style. Allowed values include art, arcane_multi, sketch_multi, etc.
  • style_img_0 to style_img_3: Optional face style images. These are unused if a pretrained style is set.
  • preserve_color: A boolean to decide whether to preserve the colors of the original image.
  • num_iter: An integer specifying the number of fine-tuning steps.


  • The model outputs a file containing the stylized face image.

With this understanding, let’s move to the step-by-step guide.

Step-by-Step Guide to Using JoJoGAN

If you don't want to code, you can play around with the JoJoGAN demo on Replicate. However, if you are up for some coding, this guide will walk you through how to interact with JoJoGAN via Replicate's API.

Step 1: Install Dependencies

First, you'll need to install the Node.js client for Replicate.

npm install replicate

Step 2: Set API Token

Copy your API token and authenticate it by setting it as an environment variable.

export REPLICATE_API_TOKEN=your_api_token_here

Step 3: Run the Model

Use the following Node.js code to run the model.

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,

const output = await

Model("mchong6/jojogan", {
  input_face: "path/to/your/input/image.jpg",
  pretrained: "art",

const stylizedImage = output.files.stylized_face;

Step 4: Download and Review Output

After the model has finished running, the output will be saved in stylizedImage. You can review the stylized face in that object.


JoJoGAN offers a plethora of possibilities in the realm of artistic image editing and stylization. Its use cases extend far beyond the art world and into practical applications for developers, marketers, and social media platforms. If you’re interested in discovering more models like JoJoGAN, offers a comprehensive database that helps you find the best AI models tailored to your needs.

Further Reading

For those interested in diving deeper into JoJoGAN, image stylization, and related topics, here's a curated list of resources to help you further your understanding and application:

  • JoJoGAN GitHub Repository: The official GitHub repository for the JoJoGAN project provides code, pre-trained models, and technical explanations. A valuable resource for developers interested in diving deeper.
  • Generative Adversarial Networks Paper by Ian Goodfellow: This seminal paper introduced the concept of Generative Adversarial Networks (GANs). A must-read to grasp the foundational principles that JoJoGAN builds upon.
  • Coursera GAN Specialization: This Coursera course offers an end-to-end understanding of GANs, from theory to practical application. Suitable for those looking to apply GANs in various domains.
  • Artistic Stylization in Computer Graphics ACM Paper: This academic paper provides an in-depth look at stylization techniques in computer graphics, an essential concept for JoJoGAN and similar models.
  • A comprehensive platform for discovering new AI models. Users can filter, sort, and read detailed descriptions, making it easier to find models like JoJoGAN tailored to specific use-cases.

By leveraging these resources, you can deepen your understanding and practical application of GANs, image stylization, and JoJoGAN. Whether you're a developer, a founder, or an AI enthusiast, these resources offer something for everyone.

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