mlsanityData to 7 Oct 2026

StyleDrop: Text-to-Image Generation in Any Style

arXiv 2306.00983 ↗First posted 1 Jun 2023cs.CV cs.AIcited by 197

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Cited by 91 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language and out-of-distribution effects make it hard to synthesize image styles, that leverage a specific design pattern, texture or material. In this paper, we introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. More results are available at our project website: https://styledrop.github.io

Key facts. Cited by 197 papers in the map (data through 7 Oct 2026). Builds on 38 of its 49 references that are in the map. First posted on arXiv 1 Jun 2023.

Builds on

38 of 49 references.

References · 38

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  6. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  7. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  8. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  9. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  10. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  11. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  12. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
26 more
  1. 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Modelscited by 1,339
  2. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
  3. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,099
  4. 2022MaskGIT: Masked Generative Image Transformercited by 1,082
  5. 2023ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generationcited by 1,010
  6. 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 973
  7. 2022Autoregressive Image Generation using Residual Quantizationcited by 706
  8. 2021Vector-quantized Image Modeling with Improved VQGANcited by 656
  9. 2023Muse: Text-To-Image Generation via Masked Generative Transformerscited by 640
  10. 2022Compositional Visual Generation with Composable Diffusion Modelscited by 619
  11. 2015A Neural Algorithm of Artistic Stylecited by 480
  12. 2022Phenaki: Variable Length Video Generation From Open Domain Textual Descriptioncited by 475
  13. 2023Aligning Text-to-Image Models using Human Feedbackcited by 418
  14. 2023SVDiff: Compact Parameter Space for Diffusion Fine-Tuningcited by 338
  15. 2023Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discoverycited by 329
  16. 2022Inversion-Based Style Transfer with Diffusion Modelscited by 290
  17. 2023Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMCcited by 216
  18. 2017Neural Style Transfer: A Reviewcited by 199
  19. 2021StyTr2: Image Style Transfer with Transformerscited by 186
  20. 2017Demystifying Neural Style Transfercited by 152
  21. 2023Cones: Concept Neurons in Diffusion Models for Customized Generationcited by 150
  22. 2015Attribute2Image: Conditional Image Generation from Visual Attributescited by 93
  23. 2022Visual Prompt Tuning for Generative Transfer Learningcited by 75
  24. 2021Unsupervised Learning of Compositional Energy Conceptscited by 71
  25. 2023MaskSketch: Unpaired Structure-guided Masked Image Generationcited by 27
  26. 2022QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelitycited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 30; 2024: 75; 2025: 65; 2026: 27.

197 papers.

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  1. 2024Lumiere: A Space-Time Diffusion Model for Video Generationcited by 450
  2. 2023PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embeddingcited by 324
  3. 2024AnyEdit: Mastering Unified High-Quality Image Editing for Any Ideacited by 235
  4. 2023HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Modelscited by 224
  5. 2023Style Aligned Image Generation via Shared Attentioncited by 214
  6. 2024Diffusion Model-Based Image Editing: A Surveycited by 212
  7. 2023ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAscited by 194
  8. 2024InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generationcited by 166
  9. 2023Cross-Image Attention for Zero-Shot Appearance Transfercited by 135
  10. 2024Implicit Style-Content Separation using B-LoRAcited by 135
  11. 2023State of the Art on Diffusion Models for Visual Computingcited by 126
  12. 2024Improving Diffusion Models for Authentic Virtual Try-on in the Wildcited by 109
38 more
  1. 2024UniReal: Universal Image Generation and Editing via Learning Real-world Dynamicscited by 100
  2. 2025ACE++: Instruction-Based Image Creation and Editing via Context-Aware Content Fillingcited by 95
  3. 2023Improving Tuning-Free Real Image Editing with Proximal Guidancecited by 87
  4. 2024Controllable Generation with Text-to-Image Diffusion Models: A Surveycited by 82
  5. 2024Multi-LoRA Composition for Image Generationcited by 80
  6. 2023ImagenHub: Standardizing the evaluation of conditional image generation modelscited by 75
  7. 2023PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Modelscited by 70
  8. 2023Orthogonal Adaptation for Modular Customization of Diffusion Modelscited by 70
  9. 2024DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representationscited by 68
  10. 2024ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuningcited by 68
  11. 2024Instruct-Imagen: Image Generation with Multi-modal Instructioncited by 65
  12. 2023ZONE: Zero-Shot Instruction-Guided Local Editingcited by 61
  13. 2025Insert Anything: Image Insertion via In-Context Editing in DiTcited by 61
  14. 2023RealFill: Reference-Driven Generation for Authentic Image Completioncited by 58
  15. 2023DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedbackcited by 57
  16. 20244M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalitiescited by 52
  17. 2024InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generationcited by 51
  18. 2024Still-Moving: Customized Video Generation without Customized Video Datacited by 49
  19. 2025K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAscited by 48
  20. 2024StyleMaster: Stylize Your Video with Artistic Generation and Translationcited by 47
  21. 2024RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Controlcited by 46
  22. 2025SnapMoGen: Human Motion Generation from Expressive Textscited by 44
  23. 2023StyleAdapter: A Unified Stylized Image Generation Modelcited by 39
  24. 2024Efficient Diffusion Models: A Comprehensive Survey from Principles to Practicescited by 39
  25. 2025VisualCloze: A Universal Image Generation Framework via Visual In-Context Learningcited by 39
  26. 2023Alchemist: Parametric Control of Material Properties with Diffusion Modelscited by 38
  27. 2024EditWorld: Simulating World Dynamics for Instruction-Following Image Editingcited by 37
  28. 2024Customizing Text-to-Image Models with a Single Image Paircited by 36
  29. 2024Motion Inversion for Video Customizationcited by 30
  30. 2025OmniStyle: Filtering High Quality Style Transfer Data at Scalecited by 30
  31. 2024A Survey on Personalized Content Synthesis with Diffusion Modelscited by 29
  32. 2024StyleBooth: Image Style Editing with Multimodal Instructioncited by 28
  33. 2023DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Modelscited by 26
  34. 2024DreamFlow: High-Quality Text-to-3D Generation by Approximating Probability Flowcited by 26
  35. 2024Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimizationcited by 25
  36. 2025UniCombine: Unified Multi-Conditional Combination with Diffusion Transformercited by 25
  37. 2024FreeTuner: Any Subject in Any Style with Training-free Diffusioncited by 24
  38. 2024UnZipLoRA: Separating Content and Style from a Single Imagecited by 24

50 most cited of 197 All 197 (JSON)

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  1. 2025StyleBlend: Enhancing Style-Specific Content Creation in Text-to-Image Diffusion Models
  2. 2024StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models
  3. 2023ControlStyle: Text-Driven Stylized Image Generation Using Diffusion Priors
  4. 2024StyleTokenizer: Defining Image Style by a Single Instance for Controlling Diffusion Models
  5. 2024StyleMamba : State Space Model for Efficient Text-driven Image Style Transfer
  6. 2024Style-Friendly SNR Sampler for Style-Driven Generation
  7. 2024ArtWeaver: Advanced Dynamic Style Integration via Diffusion Model
  8. 2025A Style is Worth One Code: Unlocking Code-to-Style Image Generation with Discrete Style Space