# StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Synthesis

arXiv: https://arxiv.org/abs/2111.03133 · HTML: https://mlsanity.com/paper/2111.03133/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 6 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [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)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders](https://mlsanity.com/paper/2106.14843/) (2021)
- [Style Transfer by Relaxed Optimal Transport and Self-Similarity](https://mlsanity.com/paper/1904.12785/) (2019)

## Built on by (most cited first, 15 of 15)

- [Zero-Shot Text-Guided Object Generation with Dream Fields](https://mlsanity.com/paper/2112.01455/) (2021)
- [SVGDreamer: Text Guided SVG Generation with Diffusion Model](https://mlsanity.com/paper/2312.16476/) (2023)
- [DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models](https://mlsanity.com/paper/2306.14685/) (2023)
- [High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learning](https://mlsanity.com/paper/2305.02572/) (2023)
- [Text2Light: Zero-Shot Text-Driven HDR Panorama Generation](https://mlsanity.com/paper/2209.09898/) (2022)
- [Name Your Style: An Arbitrary Artist-aware Image Style Transfer](https://mlsanity.com/paper/2202.13562/) (2022)
- [ExpCLIP: Bridging Text and Facial Expressions via Semantic Alignment](https://mlsanity.com/paper/2308.14448/) (2023)
- [End-to-End Visual Editing with a Generatively Pre-Trained Artist](https://mlsanity.com/paper/2205.01668/) (2022)
- [Open Vocabulary Semantic Scene Sketch Understanding](https://mlsanity.com/paper/2312.12463/) (2023)
- [Fine-Tuned but Zero-Shot 3D Shape Sketch View Similarity and Retrieval](https://mlsanity.com/paper/2306.08541/) (2023)
- [Bridging the prosody GAP: Genetic Algorithm with People to efficiently sample emotional prosody](https://mlsanity.com/paper/2205.04820/) (2022)
- [Birth of a Painting: Differentiable Brushstroke Reconstruction](https://mlsanity.com/paper/2511.13191/) (2025)
- [TextCLIP: Text-Guided Face Image Generation And Manipulation Without Adversarial Training](https://mlsanity.com/paper/2309.11923/) (2023)
- [Bridging Text and Image for Artist Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2410.09566/) (2024)
- [SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG Generation](https://mlsanity.com/paper/2411.17832/) (2024)

## Abstract (arXiv)

Generating images that fit a given text description using machine learning has improved greatly with the release of technologies such as the CLIP image-text encoder model; however, current methods lack artistic control of the style of image to be generated. We introduce StyleCLIPDraw which adds a style loss to the CLIPDraw text-to-drawing synthesis model to allow artistic control of the synthesized drawings in addition to control of the content via text. Whereas performing decoupled style transfer on a generated image only affects the texture, our proposed coupled approach is able to capture a style in both texture and shape, suggesting that the style of the drawing is coupled with the drawing process itself. More results and our code are available at https://github.com/pschaldenbrand/StyleCLIPDraw