# A Critical Assessment of Modern Generative Models' Ability to Replicate Artistic Styles arXiv: https://arxiv.org/abs/2502.15856 ยท HTML: https://mlsanity.com/paper/2502.15856/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 51 references that are in the map. - First posted on arXiv 21 Feb 2025. ## Builds on (most cited first, 30 of 31) - [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) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [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) - [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022) - [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024) - [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019) - [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022) - [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022) - [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017) - [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023) - [Are Emergent Abilities of Large Language Models a Mirage?](https://mlsanity.com/paper/2304.15004/) (2023) - [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015) - [OmniGen: Unified Image Generation](https://mlsanity.com/paper/2409.11340/) (2024) - [GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image](https://mlsanity.com/paper/2306.08571/) (2023) - [Query-Key Normalization for Transformers](https://mlsanity.com/paper/2010.04245/) (2020) - [StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis](https://mlsanity.com/paper/2301.09515/) (2023) - [Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer](https://mlsanity.com/paper/2312.09008/) (2023) - [One-Step Image Translation with Text-to-Image Models](https://mlsanity.com/paper/2403.12036/) (2024) - [Understanding Emergent Abilities of Language Models from the Loss Perspective](https://mlsanity.com/paper/2403.15796/) (2024) - [CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images](https://mlsanity.com/paper/2303.14126/) (2023) - [The Creativity of Text-to-Image Generation](https://mlsanity.com/paper/2206.02904/) (2022) - [ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection](https://mlsanity.com/paper/2302.11970/) (2023) - [WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection](https://mlsanity.com/paper/2402.11843/) (2024) - [TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter](https://mlsanity.com/paper/2306.08310/) (2023) - [Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt](https://mlsanity.com/paper/2404.11474/) (2024) - [A Notion of Complexity for Theory of Mind via Discrete World Models](https://mlsanity.com/paper/2406.11911/) (2024) - [AI-Generated Images as Data Source: The Dawn of Synthetic Era](https://mlsanity.com/paper/2310.01830/) (2023) ## Built on by (most cited first, 6 of 6) - [Does CLIP perceive art the same way we do?](https://mlsanity.com/paper/2505.05229/) (2025) - [AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration](https://mlsanity.com/paper/2511.17906/) (2025) - [The Art That Poses Back: Assessing AI Pastiches after Contemporary Artworks](https://mlsanity.com/paper/2603.06324/) (2026) - [Concept Removal for Frontier Image Generative Models](https://mlsanity.com/paper/2606.25548/) (2026) - [Coachable agents for interactive gameplay](https://mlsanity.com/paper/2607.00642/) (2026) - [On the Separation of Human and AI-Generated Images in CLIP Embedding Space](https://mlsanity.com/paper/2608.25609/) (2026) ## Abstract (arXiv) In recent years, advancements in generative artificial intelligence have led to the development of sophisticated tools capable of mimicking diverse artistic styles, opening new possibilities for digital creativity and artistic expression. This paper presents a critical assessment of the style replication capabilities of contemporary generative models, evaluating their strengths and limitations across multiple dimensions. We examine how effectively these models reproduce traditional artistic styles while maintaining structural integrity and compositional balance in the generated images. The analysis is based on a new large dataset of AI-generated works imitating artistic styles of the past, holding potential for a wide range of applications: the "AI-pastiche" dataset. The study is supported by extensive user surveys, collecting diverse opinions on the dataset and investigation both technical and aesthetic challenges, including the ability to generate outputs that are realistic and visually convincing, the versatility of models in handling a wide range of artistic styles, and the extent to which they adhere to the content and stylistic specifications outlined in prompts. This paper aims to provide a comprehensive overview of the current state of generative tools in style replication, offering insights into their technical and artistic limitations, potential advancements in model design and training methodologies, and emerging opportunities for enhancing digital artistry, human-AI collaboration, and the broader creative landscape.