# JoJoGAN: One Shot Face Stylization

arXiv: https://arxiv.org/abs/2112.11641 · HTML: https://mlsanity.com/paper/2112.11641/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 41 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 30 of 35)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Designing an Encoder for StyleGAN Image Manipulation](https://mlsanity.com/paper/2102.02766/) (2021)
- [Closed-Form Factorization of Latent Semantics in GANs](https://mlsanity.com/paper/2007.06600/) (2020)
- [InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs](https://mlsanity.com/paper/2005.09635/) (2020)
- [PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models](https://mlsanity.com/paper/2003.03808/) (2020)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [Towards Real-World Blind Face Restoration with Generative Facial Prior](https://mlsanity.com/paper/2101.04061/) (2021)
- [StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation](https://mlsanity.com/paper/2011.12799/) (2020)
- [ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement](https://mlsanity.com/paper/2104.02699/) (2021)
- [Few-shot Image Generation via Cross-domain Correspondence](https://mlsanity.com/paper/2104.06820/) (2021)
- [Arbitrary Style Transfer with Style-Attentional Networks](https://mlsanity.com/paper/1812.02342/) (2018)
- [GAN Prior Embedded Network for Blind Face Restoration in the Wild](https://mlsanity.com/paper/2105.06070/) (2021)
- [Effectively Unbiased FID and Inception Score and where to find them](https://mlsanity.com/paper/1911.07023/) (2019)
- [StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators](https://mlsanity.com/paper/2108.00946/) (2021)
- [Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs](https://mlsanity.com/paper/2002.10964/) (2020)
- [Few-shot Image Generation with Elastic Weight Consolidation](https://mlsanity.com/paper/2012.02780/) (2020)
- [Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains](https://mlsanity.com/paper/2010.05334/) (2020)
- [Improved StyleGAN Embedding: Where are the Good Latents?](https://mlsanity.com/paper/2012.09036/) (2020)
- [Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks](https://mlsanity.com/paper/2110.08398/) (2021)
- [Few-Shot Adaptation of Generative Adversarial Networks](https://mlsanity.com/paper/2010.11943/) (2020)
- [BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation](https://mlsanity.com/paper/2110.11728/) (2021)

## Built on by (most cited first, 30 of 49)

- [ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs](https://mlsanity.com/paper/2311.13600/) (2023)
- [Diffusion-based Image Translation using Disentangled Style and Content Representation](https://mlsanity.com/paper/2209.15264/) (2022)
- [OBJECT 3DIT: Language-guided 3D-aware Image Editing](https://mlsanity.com/paper/2307.11073/) (2023)
- [StyleGAN knows Normal, Depth, Albedo, and More](https://mlsanity.com/paper/2306.00987/) (2023)
- [3DAvatarGAN: Bridging Domains for Personalized Editable Avatars](https://mlsanity.com/paper/2301.02700/) (2023)
- [Training-free Content Injection using h-space in Diffusion Models](https://mlsanity.com/paper/2303.15403/) (2023)
- [DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model](https://mlsanity.com/paper/2211.16374/) (2022)
- [One-Shot Adaptation of GAN in Just One CLIP](https://mlsanity.com/paper/2203.09301/) (2022)
- [UnZipLoRA: Separating Content and Style from a Single Image](https://mlsanity.com/paper/2412.04465/) (2024)
- [MapPrior: Bird's-Eye View Map Layout Estimation with Generative Models](https://mlsanity.com/paper/2308.12963/) (2023)
- [Generalized One-shot Domain Adaptation of Generative Adversarial Networks](https://mlsanity.com/paper/2209.03665/) (2022)
- [A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot](https://mlsanity.com/paper/2307.14397/) (2023)
- [Domain Re-Modulation for Few-Shot Generative Domain Adaptation](https://mlsanity.com/paper/2302.02550/) (2023)
- [One-Shot Structure-Aware Stylized Image Synthesis](https://mlsanity.com/paper/2402.17275/) (2024)
- [Deformable One-shot Face Stylization via DINO Semantic Guidance](https://mlsanity.com/paper/2403.00459/) (2024)
- [Bridging CLIP and StyleGAN through Latent Alignment for Image Editing](https://mlsanity.com/paper/2210.04506/) (2022)
- [WeditGAN: Few-Shot Image Generation via Latent Space Relocation](https://mlsanity.com/paper/2305.06671/) (2023)
- [Diffusion-driven GAN Inversion for Multi-Modal Face Image Generation](https://mlsanity.com/paper/2405.04356/) (2024)
- [CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning](https://mlsanity.com/paper/2203.08612/) (2022)
- [StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation](https://mlsanity.com/paper/2212.10229/) (2022)
- [Portrait Diffusion: Training-free Face Stylization with Chain-of-Painting](https://mlsanity.com/paper/2312.02212/) (2023)
- [Contrastive Test-Time Composition of Multiple LoRA Models for Image Generation](https://mlsanity.com/paper/2403.19776/) (2024)
- [Video2StyleGAN: Disentangling Local and Global Variations in a Video](https://mlsanity.com/paper/2205.13996/) (2022)
- [WebtoonMe: A Data-Centric Approach for Full-Body Portrait Stylization](https://mlsanity.com/paper/2210.10335/) (2022)
- [StyO: Stylize Your Face in Only One-shot](https://mlsanity.com/paper/2303.03231/) (2023)
- [AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning](https://mlsanity.com/paper/2303.14297/) (2023)
- [Target-Aware Generative Augmentations for Single-Shot Adaptation](https://mlsanity.com/paper/2305.13284/) (2023)
- [StyLitGAN: Prompting StyleGAN to Produce New Illumination Conditions](https://mlsanity.com/paper/2205.10351/) (2022)
- [Leveraging GAN Priors for Few-Shot Part Segmentation](https://mlsanity.com/paper/2207.13428/) (2022)
- [Stylized Face Sketch Extraction via Generative Prior with Limited Data](https://mlsanity.com/paper/2403.11263/) (2024)

## Abstract (arXiv)

A style mapper applies some fixed style to its input images (so, for example, taking faces to cartoons). This paper describes a simple procedure -- JoJoGAN -- to learn a style mapper from a single example of the style. JoJoGAN uses a GAN inversion procedure and StyleGAN's style-mixing property to produce a substantial paired dataset from a single example style. The paired dataset is then used to fine-tune a StyleGAN. An image can then be style mapped by GAN-inversion followed by the fine-tuned StyleGAN. JoJoGAN needs just one reference and as little as 30 seconds of training time. JoJoGAN can use extreme style references (say, animal faces) successfully. Furthermore, one can control what aspects of the style are used and how much of the style is applied. Qualitative and quantitative evaluation show that JoJoGAN produces high quality high resolution images that vastly outperform the current state-of-the-art.