Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?
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Cited by 98 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.
Key facts. Cited by 675 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Apr 2019.
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Citing papers per year: 2021: 148; 2022: 193; 2023: 183; 2024: 95; 2025: 42; 2026: 14.
675 papers.
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- 2022Generative Adversarial Networkscited by 5,627
- 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
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- 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 635
- 2021Designing an Encoder for StyleGAN Image Manipulationcited by 544
38 more
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- 2021Pivotal Tuning for Latent-based Editing of Real Imagescited by 427
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- 2021StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2cited by 301
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- 2021StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generationcited by 289
- 2024Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wildcited by 286
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50 most cited of 675 All 675 (JSON)
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50 newest of 675 All 675 (JSON)
- 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
- 2022Null-text Inversion for Editing Real Images using Guided Diffusion Modelscited by 1,179
- 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,178
- 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,106
- 2021Blended Diffusion for Text-driven Editing of Natural Imagescited by 927
- 2021DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulationcited by 642
- 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 635
- 2021Diffusion Autoencoders: Toward a Meaningful and Decodable Representationcited by 449
- 2022Perception Prioritized Training of Diffusion Modelscited by 303
- 2023DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editingcited by 294
- 2021More Control for Free! Image Synthesis with Semantic Diffusion Guidancecited by 212
- 2023Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Modelscited by 203
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- 2026AHOY! Animatable Humans under Occlusion from YouTube Videos with Gaussian Splatting and Video Diffusion Priors18 Mar 2026
- 2026Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion12 Jan 2026
- 2025Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation16 Oct 2025
- 2025AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models7 Oct 2025
- 2025IntrinsicEdit: Precise generative image manipulation in intrinsic space13 May 2025
- 2025DIVE: Inverting Conditional Diffusion Models for Discriminative Tasks24 Apr 2025
- 2025Bringing Diversity from Diffusion Models to Semantic-Guided Face Asset Generation21 Apr 2025
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- 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
- 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,937
- 2021Designing an Encoder for StyleGAN Image Manipulationcited by 544
- 2023Zero-shot Image-to-Image Translationcited by 505
- 2022StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasetscited by 492
- 2023Scaling up GANs for Text-to-Image Synthesiscited by 492
- 2021Pivotal Tuning for Latent-based Editing of Real Imagescited by 427
- 2023ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generationcited by 416
- 2021CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fieldscited by 370
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- 2026Residual-Space Evolutionary Optimization via Flow-based Generative Models18 Jun 2026
- 2026ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Society19 May 2026
- 2026Learning Spatial-Temporal Coherent Correlations for Speech-Preserving Facial Expression Manipulation22 Apr 2026
- 2026From Masks to Pixels and Meaning: A New Taxonomy, Benchmark, and Metrics for VLM Image Tampering20 Mar 2026
- 2026Style-Aware Gloss Control for Generative Non-Photorealistic Rendering18 Feb 2026
- 2026Visual Personalization Turing Test30 Jan 2026
- 2026SelfieAvatar: Real-time Head Avatar reenactment from a Selfie Video26 Jan 2026
- 2026Audio-Driven Talking Face Generation with Blink Embedding and Hash Grid Landmarks Encoding26 Jan 2026
- 2025GPTFace: Generative Pre-training of Facial-Linguistic Transformer by Span Masking and Weakly Correlated Text-image Data21 Oct 2025
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