# Conditional Image Generation and Manipulation for User-Specified Content

arXiv: https://arxiv.org/abs/2005.04909 · HTML: https://mlsanity.com/paper/2005.04909/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 20 of 20)

- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions](https://mlsanity.com/paper/2111.10337/) (2021)
- [InstanceDiffusion: Instance-level Control for Image Generation](https://mlsanity.com/paper/2402.03290/) (2024)
- [CelebV-Text: A Large-Scale Facial Text-Video Dataset](https://mlsanity.com/paper/2303.14717/) (2023)
- [Prompt Tuning Inversion for Text-Driven Image Editing Using Diffusion Models](https://mlsanity.com/paper/2305.04441/) (2023)
- [Towards Implicit Text-Guided 3D Shape Generation](https://mlsanity.com/paper/2203.14622/) (2022)
- [Adversarial Text-to-Image Synthesis: A Review](https://mlsanity.com/paper/2101.09983/) (2021)
- [Multimodal Image Synthesis and Editing: The Generative AI Era](https://mlsanity.com/paper/2112.13592/) (2021)
- [UniTune: Text-Driven Image Editing by Fine Tuning a Diffusion Model on a Single Image](https://mlsanity.com/paper/2210.09477/) (2022)
- [AnyFace: Free-style Text-to-Face Synthesis and Manipulation](https://mlsanity.com/paper/2203.15334/) (2022)
- [ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation](https://mlsanity.com/paper/2209.04145/) (2022)
- [DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation](https://mlsanity.com/paper/2303.06285/) (2023)
- [Towards Open-World Text-Guided Face Image Generation and Manipulation](https://mlsanity.com/paper/2104.08910/) (2021)
- [Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images](https://mlsanity.com/paper/2308.16758/) (2023)
- [Geometrical Properties of Text Token Embeddings for Strong Semantic Binding in Text-to-Image Generation](https://mlsanity.com/paper/2503.23011/) (2025)
- [DreamStone: Image as Stepping Stone for Text-Guided 3D Shape Generation](https://mlsanity.com/paper/2303.15181/) (2023)
- [DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing](https://mlsanity.com/paper/2310.08785/) (2023)
- [Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation](https://mlsanity.com/paper/2510.22107/) (2025)
- [Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation](https://mlsanity.com/paper/2506.14015/) (2025)
- [MM-SVGEdit: A Multimodal-Driven SVG Editing for UI Design](https://mlsanity.com/paper/2609.06116/) (2026)

## Abstract (arXiv)

In recent years, Generative Adversarial Networks (GANs) have improved steadily towards generating increasingly impressive real-world images. It is useful to steer the image generation process for purposes such as content creation. This can be done by conditioning the model on additional information. However, when conditioning on additional information, there still exists a large set of images that agree with a particular conditioning. This makes it unlikely that the generated image is exactly as envisioned by a user, which is problematic for practical content creation scenarios such as generating facial composites or stock photos. To solve this problem, we propose a single pipeline for text-to-image generation and manipulation. In the first part of our pipeline we introduce textStyleGAN, a model that is conditioned on text. In the second part of our pipeline we make use of the pre-trained weights of textStyleGAN to perform semantic facial image manipulation. The approach works by finding semantic directions in latent space. We show that this method can be used to manipulate facial images for a wide range of attributes. Finally, we introduce the CelebTD-HQ dataset, an extension to CelebA-HQ, consisting of faces and corresponding textual descriptions.