mlsanityData to 7 Oct 2026

CLIPstyler: Image Style Transfer with a Single Text Condition

arXiv 2112.00374 ↗First posted 1 Dec 2021cs.CV cs.CL eess.IVcited by 203

Not on the map. Why?

Cited by 46 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Existing neural style transfer methods require reference style images to transfer texture information of style images to content images. However, in many practical situations, users may not have reference style images but still be interested in transferring styles by just imagining them. In order to deal with such applications, we propose a new framework that enables a style transfer `without' a style image, but only with a text description of the desired style. Using the pre-trained text-image embedding model of CLIP, we demonstrate the modulation of the style of content images only with a single text condition. Specifically, we propose a patch-wise text-image matching loss with multiview augmentations for realistic texture transfer. Extensive experimental results confirmed the successful image style transfer with realistic textures that reflect semantic query texts.

Key facts. Cited by 203 papers in the map (data through 7 Oct 2026). Builds on 27 of its 32 references that are in the map. First posted on arXiv 1 Dec 2021.

Builds on

27 of 32 references.

References · 27

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  5. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  6. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  7. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  8. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  9. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 990
  10. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  11. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 575
  12. 2017Universal Style Transfer via Feature Transformscited by 352
15 more
  1. 2017StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 229
  2. 2017Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesiscited by 215
  3. 2018Arbitrary Style Transfer with Style-Attentional Networkscited by 197
  4. 2021CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoderscited by 186
  5. 2021AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfercited by 178
  6. 2019Photorealistic Style Transfer via Wavelet Transformscited by 166
  7. 2021StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generatorscited by 164
  8. 2019ManiGAN: Text-Guided Image Manipulationcited by 156
  9. 2018A Closed-form Solution to Photorealistic Image Stylizationcited by 133
  10. 2020Arbitrary Style Transfer via Multi-Adaptation Networkcited by 70
  11. 2021Rethinking Style Transfer: From Pixels to Parameterized Brushstrokescited by 42
  12. 2021Domain-Aware Universal Style Transfercited by 40
  13. 2021DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfercited by 38
  14. 2019Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfercited by 36
  15. 2016Style-Transfer via Texture-Synthesiscited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 35; 2023: 75; 2024: 50; 2025: 29; 2026: 13.

203 papers.

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  1. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  2. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
  3. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
  4. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  5. 2022Null-text Inversion for Editing Real Images using Guided Diffusion Modelscited by 1,168
  6. 2023Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Modelscited by 696
  7. 2022Text2LIVE: Text-Driven Layered Image and Video Editingcited by 312
  8. 2022Inversion-Based Style Transfer with Diffusion Modelscited by 290
  9. 2022VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidancecited by 285
  10. 2023P+: Extended Textual Conditioning in Text-to-Image Generationcited by 242
  11. 2023Break-A-Scene: Extracting Multiple Concepts from a Single Imagecited by 230
  12. 2023ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAscited by 194
38 more
  1. 2023Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfercited by 194
  2. 2023StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Modelscited by 154
  3. 2022Diffusion-based Image Translation using Disentangled Style and Content Representationcited by 148
  4. 2023Delta Denoising Scorecited by 120
  5. 2023ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Modelscited by 75
  6. 2023StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editingcited by 68
  7. 2024SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?cited by 64
  8. 2023ZONE: Zero-Shot Instruction-Guided Local Editingcited by 61
  9. 2022PODA: Prompt-driven Zero-shot Domain Adaptationcited by 60
  10. 2022AvatarGen: A 3D Generative Model for Animatable Human Avatarscited by 59
  11. 2023Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfercited by 59
  12. 2023PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalizationcited by 59
  13. 2023Shape-aware Text-driven Layered Video Editingcited by 54
  14. 2021Multimodal Image Synthesis and Editing: The Generative AI Eracited by 52
  15. 2022ArtFID: Quantitative Evaluation of Neural Style Transfercited by 49
  16. 2023Natural Language-Assisted Sign Language Recognitioncited by 46
  17. 2023GeneCIS: A Benchmark for General Conditional Image Similaritycited by 46
  18. 2022DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylizationcited by 45
  19. 2023DiffUTE: Universal Text Editing Diffusion Modelcited by 44
  20. 2023CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Searchcited by 42
  21. 2022CLIP-Actor: Text-Driven Recommendation and Stylization for Animating Human Meshescited by 41
  22. 2023ControlStyle: Text-Driven Stylized Image Generation Using Diffusion Priorscited by 40
  23. 2024PromptFix: You Prompt and We Fix the Photocited by 40
  24. 2023Highly Personalized Text Embedding for Image Manipulation by Stable Diffusioncited by 39
  25. 2023ArtAdapter: Text-to-Image Style Transfer using Multi-Level Style Encoder and Explicit Adaptationcited by 39
  26. 2023High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learningcited by 37
  27. 2024EditWorld: Simulating World Dynamics for Instruction-Following Image Editingcited by 37
  28. 2023Text2Layer: Layered Image Generation using Latent Diffusion Modelcited by 35
  29. 2022CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphicscited by 34
  30. 2023PromptPaint: Steering Text-to-Image Generation Through Paint Medium-like Interactionscited by 34
  31. 2023Collaborating Foundation Models for Domain Generalized Semantic Segmentationcited by 33
  32. 2023Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Trainingcited by 32
  33. 2022One-Shot Adaptation of GAN in Just One CLIPcited by 31
  34. 2023Text-Driven Image Editing via Learnable Regionscited by 31
  35. 2022Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusioncited by 30
  36. 2023InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalistscited by 30
  37. 2024Unified Language-driven Zero-shot Domain Adaptationcited by 30
  38. 2023A Simple Recipe for Language-guided Domain Generalized Segmentationcited by 27

50 most cited of 203 All 203 (JSON)