# TediGAN: Text-Guided Diverse Face Image Generation and Manipulation

arXiv: https://arxiv.org/abs/2012.03308 · HTML: https://mlsanity.com/paper/2012.03308/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [GAN Inversion: A Survey](https://mlsanity.com/paper/2101.05278/) (2021)
- [Talk-to-Edit: Fine-Grained Facial Editing via Dialog](https://mlsanity.com/paper/2109.04425/) (2021)
- [Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network](https://mlsanity.com/paper/2204.10485/) (2022)
- [DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis](https://mlsanity.com/paper/2108.12141/) (2021)
- [StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation](https://mlsanity.com/paper/2112.08493/) (2021)
- [M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers](https://mlsanity.com/paper/2105.14211/) (2021)
- [Cycle-Consistent Inverse GAN for Text-to-Image Synthesis](https://mlsanity.com/paper/2108.01361/) (2021)
- [Towards Open-World Text-Guided Face Image Generation and Manipulation](https://mlsanity.com/paper/2104.08910/) (2021)
- [Embedding Arithmetic of Multimodal Queries for Image Retrieval](https://mlsanity.com/paper/2112.03162/) (2021)
- [iEdit: Localised Text-guided Image Editing with Weak Supervision](https://mlsanity.com/paper/2305.05947/) (2023)
- [M6-Fashion: High-Fidelity Multi-modal Image Generation and Editing](https://mlsanity.com/paper/2205.11705/) (2022)

## Abstract (arXiv)

In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: StyleGAN inversion module, visual-linguistic similarity learning, and instance-level optimization. The inversion module maps real images to the latent space of a well-trained StyleGAN. The visual-linguistic similarity learns the text-image matching by mapping the image and text into a common embedding space. The instance-level optimization is for identity preservation in manipulation. Our model can produce diverse and high-quality images with an unprecedented resolution at 1024. Using a control mechanism based on style-mixing, our TediGAN inherently supports image synthesis with multi-modal inputs, such as sketches or semantic labels, with or without instance guidance. To facilitate text-guided multi-modal synthesis, we propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real face images and corresponding semantic segmentation map, sketch, and textual descriptions. Extensive experiments on the introduced dataset demonstrate the superior performance of our proposed method. Code and data are available at https://github.com/weihaox/TediGAN.