# DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models

arXiv: https://arxiv.org/abs/2309.06933 · HTML: https://mlsanity.com/paper/2309.06933/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 24 of its 27 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 24 of 24)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [Perception Prioritized Training of Diffusion Models](https://mlsanity.com/paper/2204.00227/) (2022)
- [Inversion-Based Style Transfer with Diffusion Models](https://mlsanity.com/paper/2211.13203/) (2022)
- [P+: Extended Textual Conditioning in Text-to-Image Generation](https://mlsanity.com/paper/2303.09522/) (2023)
- [StyleDrop: Text-to-Image Generation in Any Style](https://mlsanity.com/paper/2306.00983/) (2023)
- [StyTr2: Image Style Transfer with Transformers](https://mlsanity.com/paper/2105.14576/) (2021)
- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks](https://mlsanity.com/paper/2307.09724/) (2023)
- [UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance](https://mlsanity.com/paper/2210.16031/) (2022)
- [AesUST: Towards Aesthetic-Enhanced Universal Style Transfer](https://mlsanity.com/paper/2208.13016/) (2022)
- [Interactive Cartoonization with Controllable Perceptual Factors](https://mlsanity.com/paper/2212.09555/) (2022)

## Built on by (most cited first, 27 of 27)

- [The Chosen One: Consistent Characters in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2311.10093/) (2023)
- [StyleShot: A Snapshot on Any Style](https://mlsanity.com/paper/2407.01414/) (2024)
- [Mini-DALLE3: Interactive Text to Image by Prompting Large Language Models](https://mlsanity.com/paper/2310.07653/) (2023)
- [FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models](https://mlsanity.com/paper/2401.15636/) (2024)
- [Diffusion-Based Visual Art Creation: A Survey and New Perspectives](https://mlsanity.com/paper/2408.12128/) (2024)
- [InstructG2I: Synthesizing Images from Multimodal Attributed Graphs](https://mlsanity.com/paper/2410.07157/) (2024)
- [Imperceptible Protection against Style Imitation from Diffusion Models](https://mlsanity.com/paper/2403.19254/) (2024)
- [Free-Lunch Color-Texture Disentanglement for Stylized Image Generation](https://mlsanity.com/paper/2503.14275/) (2025)
- [Isolated Diffusion: Optimizing Multi-Concept Text-to-Image Generation Training-Freely with Isolated Diffusion Guidance](https://mlsanity.com/paper/2403.16954/) (2024)
- [Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder](https://mlsanity.com/paper/2407.05552/) (2024)
- [Font Style Interpolation with Diffusion Models](https://mlsanity.com/paper/2402.14311/) (2024)
- [D2Styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods](https://mlsanity.com/paper/2408.03558/) (2024)
- [Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment](https://mlsanity.com/paper/2502.05153/) (2025)
- [Control and Realism: Best of Both Worlds in Layout-to-Image without Training](https://mlsanity.com/paper/2506.15563/) (2025)
- [CleanStyle: Plug-and-Play Style Conditioning Purification for Text-to-Image Stylization](https://mlsanity.com/paper/2602.20721/) (2026)
- [StyleBlend: Enhancing Style-Specific Content Creation in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2502.09064/) (2025)
- [Personalized Image Filter: Mastering Your Photographic Style](https://mlsanity.com/paper/2510.16791/) (2025)
- [HSI: A Holistic Style Injector for Arbitrary Style Transfer](https://mlsanity.com/paper/2502.04369/) (2025)
- [Semantix: An Energy Guided Sampler for Semantic Style Transfer](https://mlsanity.com/paper/2503.22344/) (2025)
- [Edge-Assisted Collaborative Fine-Tuning for Multi-User Personalized Artificial Intelligence Generated Content (AIGC)](https://mlsanity.com/paper/2508.04745/) (2025)
- [Cross-Domain Image Conversion by CycleDM](https://mlsanity.com/paper/2403.02919/) (2024)
- [Solar Altitude Guided Scene Illumination](https://mlsanity.com/paper/2507.05812/) (2025)
- [SPG: Style-Prompting Guidance for Style-Specific Content Creation](https://mlsanity.com/paper/2508.11476/) (2025)
- [Image Generation Based on Image Style Extraction](https://mlsanity.com/paper/2510.01347/) (2025)
- [PC-Diffusion: Aligning Diffusion Models with Human Preferences via Preference Classifier](https://mlsanity.com/paper/2511.07806/) (2025)
- [Jailbreaking Text-to-Image Models Through Cracks: Navigating Heterogeneous Safety Filters via Multi-Agent Debate](https://mlsanity.com/paper/2609.01168/) (2026)
- [Beyond User-Side Defenses: Image Protection in Open-Weight Diffusion Models](https://mlsanity.com/paper/2610.16835/) (2026)

## Abstract (arXiv)

Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristics of an artwork (e.g. brushwork, colortone, or composition) with text prompts alone may encounter limitations due to the inherent constraints of verbal description. To this end, we introduce DreamStyler, a novel framework designed for artistic image synthesis, proficient in both text-to-image synthesis and style transfer. DreamStyler optimizes a multi-stage textual embedding with a context-aware text prompt, resulting in prominent image quality. In addition, with content and style guidance, DreamStyler exhibits flexibility to accommodate a range of style references. Experimental results demonstrate its superior performance across multiple scenarios, suggesting its promising potential in artistic product creation.