# DE-Net: Dynamic Text-guided Image Editing Adversarial Networks

arXiv: https://arxiv.org/abs/2206.01160 · HTML: https://mlsanity.com/paper/2206.01160/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 38 references that are in the map.
- First posted on arXiv 2 Jun 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [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)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [CogView: Mastering Text-to-Image Generation via Transformers](https://mlsanity.com/paper/2105.13290/) (2021)
- [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https://mlsanity.com/paper/1711.10485/) (2017)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis](https://mlsanity.com/paper/1904.01310/) (2019)
- [StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1710.10916/) (2017)
- [DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis](https://mlsanity.com/paper/2008.05865/) (2020)
- [ManiGAN: Text-Guided Image Manipulation](https://mlsanity.com/paper/1912.06203/) (2019)
- [Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation](https://mlsanity.com/paper/1904.06807/) (2019)
- [M6: A Chinese Multimodal Pretrainer](https://mlsanity.com/paper/2103.00823/) (2021)
- [Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language](https://mlsanity.com/paper/1810.11919/) (2018)
- [Semantic Image Synthesis via Adversarial Learning](https://mlsanity.com/paper/1707.06873/) (2017)
- [Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation](https://mlsanity.com/paper/1912.12215/) (2019)
- [Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model](https://mlsanity.com/paper/2111.13333/) (2021)
- [Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation](https://mlsanity.com/paper/2010.12136/) (2020)
- [Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach](https://mlsanity.com/paper/2008.04200/) (2020)
- [Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions](https://mlsanity.com/paper/2008.01576/) (2020)
- [Towards Open-World Text-Guided Face Image Generation and Manipulation](https://mlsanity.com/paper/2104.08910/) (2021)
- [ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generation](https://mlsanity.com/paper/2204.04428/) (2022)

## Built on by (most cited first, 12 of 12)

- [Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting](https://mlsanity.com/paper/2212.06909/) (2022)
- [GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis](https://mlsanity.com/paper/2301.12959/) (2023)
- [Alchemist: Parametric Control of Material Properties with Diffusion Models](https://mlsanity.com/paper/2312.02970/) (2023)
- [FreeCompose: Generic Zero-Shot Image Composition with Diffusion Prior](https://mlsanity.com/paper/2407.04947/) (2024)
- [Text-to-image Editing by Image Information Removal](https://mlsanity.com/paper/2305.17489/) (2023)
- [DialogPaint: A Dialog-based Image Editing Model](https://mlsanity.com/paper/2303.10073/) (2023)
- [Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis](https://mlsanity.com/paper/2307.12084/) (2023)
- [A Survey of Multimodal Composite Editing and Retrieval](https://mlsanity.com/paper/2409.05405/) (2024)
- [Robust Text-driven Image Editing Method that Adaptively Explores Directions in Latent Spaces of StyleGAN and CLIP](https://mlsanity.com/paper/2304.00964/) (2023)
- [AnyDesign: Versatile Area Fashion Editing via Mask-Free Diffusion](https://mlsanity.com/paper/2408.11553/) (2024)
- [Towards Generalized and Training-Free Text-Guided Semantic Manipulation](https://mlsanity.com/paper/2504.17269/) (2025)
- [An Evaluation of Hybrid Annotation Workflows on High-Ambiguity Spatiotemporal Video Footage](https://mlsanity.com/paper/2510.21798/) (2025)

## Abstract (arXiv)

Text-guided image editing models have shown remarkable results. However, there remain two problems. First, they employ fixed manipulation modules for various editing requirements (e.g., color changing, texture changing, content adding and removing), which results in over-editing or insufficient editing. Second, they do not clearly distinguish between text-required and text-irrelevant parts, which leads to inaccurate editing. To solve these limitations, we propose: (i) a Dynamic Editing Block (DEBlock) which composes different editing modules dynamically for various editing requirements. (ii) a Composition Predictor (Comp-Pred) which predicts the composition weights for DEBlock according to the inference on target texts and source images. (iii) a Dynamic text-adaptive Convolution Block (DCBlock) which queries source image features to distinguish text-required parts and text-irrelevant parts. Extensive experiments demonstrate that our DE-Net achieves excellent performance and manipulates source images more correctly and accurately. Code is available at urlhttps://github.com/tobran/DE-Net.