# StyleShot: A Snapshot on Any Style

arXiv: https://arxiv.org/abs/2407.01414 · HTML: https://mlsanity.com/paper/2407.01414/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 82 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 30 of 59)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [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)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://mlsanity.com/paper/2202.09778/) (2022)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)

## Built on by (most cited first, 30 of 62)

- [OmniGen: Unified Image Generation](https://mlsanity.com/paper/2409.11340/) (2024)
- [OmniGen2: Towards Instruction-Aligned Multimodal Generation](https://mlsanity.com/paper/2506.18871/) (2025)
- [CSGO: Content-Style Composition in Text-to-Image Generation](https://mlsanity.com/paper/2408.16766/) (2024)
- [OmniStyle: Filtering High Quality Style Transfer Data at Scale](https://mlsanity.com/paper/2505.14028/) (2025)
- [USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning](https://mlsanity.com/paper/2508.18966/) (2025)
- [Multimodal LLMs Can Reason about Aesthetics in Zero-Shot](https://mlsanity.com/paper/2501.09012/) (2025)
- [Towards Enhanced Image Generation Via Multi-modal Chain of Thought in Unified Generative Models](https://mlsanity.com/paper/2503.01298/) (2025)
- [FaceShot: Bring Any Character into Life](https://mlsanity.com/paper/2503.00740/) (2025)
- [Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration](https://mlsanity.com/paper/2601.10744/) (2026)
- [CharacterShot: Controllable and Consistent 4D Character Animation](https://mlsanity.com/paper/2508.07409/) (2025)
- [DiffArtist: Towards Structure and Appearance Controllable Image Stylization](https://mlsanity.com/paper/2407.15842/) (2024)
- [StyleMe3D: Stylization with Disentangled Priors by Multiple Encoders on 3D Gaussians](https://mlsanity.com/paper/2504.15281/) (2025)
- [TeleStyle: Content-Preserving Style Transfer in Images and Videos](https://mlsanity.com/paper/2601.20175/) (2026)
- [MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping](https://mlsanity.com/paper/2604.08364/) (2026)
- [ArtCrafter: Text-Image Aligning Style Transfer via Embedding Reframing](https://mlsanity.com/paper/2501.02064/) (2025)
- [StyleAR: Customizing Multimodal Autoregressive Model for Style-Aligned Text-to-Image Generation](https://mlsanity.com/paper/2505.19874/) (2025)
- [From Noise to Nuance: Advances in Deep Generative Image Models](https://mlsanity.com/paper/2412.09656/) (2024)
- [Superpowering Open-Vocabulary Object Detectors for X-ray Vision](https://mlsanity.com/paper/2503.17071/) (2025)
- [SPAST: Arbitrary Style Transfer with Style Priors via Pre-trained Large-scale Model](https://mlsanity.com/paper/2505.08695/) (2025)
- [Tuning-free Visual Effect Transfer across Videos](https://mlsanity.com/paper/2601.07833/) (2026)
- [CleanStyle: Plug-and-Play Style Conditioning Purification for Text-to-Image Stylization](https://mlsanity.com/paper/2602.20721/) (2026)
- [Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging](https://mlsanity.com/paper/2503.07363/) (2025)
- [Fine-grained Image Retrieval via Dual-Vision Adaptation](https://mlsanity.com/paper/2506.16273/) (2025)
- [TransLight: Image-Guided Customized Lighting Control with Generative Decoupling](https://mlsanity.com/paper/2508.14814/) (2025)
- [MimicParts: Part-aware Style Injection for Speech-Driven 3D Motion Generation](https://mlsanity.com/paper/2510.13208/) (2025)
- [StyleGallery: Training-free and Semantic-aware Personalized Style Transfer from Arbitrary Image References](https://mlsanity.com/paper/2603.10354/) (2026)
- [Semi-supervised Latent Disentangled Diffusion Model for Textile Pattern Generation](https://mlsanity.com/paper/2603.16747/) (2026)
- [Compressing Image Style Training into a Single Model Forward](https://mlsanity.com/paper/2606.13809/) (2026)
- [Scale-Separated Conditioning for Style-Encoder-Free Diffusion Stylization](https://mlsanity.com/paper/2608.19719/) (2026)
- [ACCORD: Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffusion Personalization](https://mlsanity.com/paper/2503.01122/) (2025)

## Abstract (arXiv)

In this paper, we show that, a good style representation is crucial and sufficient for generalized style transfer without test-time tuning. We achieve this through constructing a style-aware encoder and a well-organized style dataset called StyleGallery. With dedicated design for style learning, this style-aware encoder is trained to extract expressive style representation with decoupling training strategy, and StyleGallery enables the generalization ability. We further employ a content-fusion encoder to enhance image-driven style transfer. We highlight that, our approach, named StyleShot, is simple yet effective in mimicking various desired styles, i.e., 3D, flat, abstract or even fine-grained styles, without test-time tuning. Rigorous experiments validate that, StyleShot achieves superior performance across a wide range of styles compared to existing state-of-the-art methods. The project page is available at: https://styleshot.github.io/.