# Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt arXiv: https://arxiv.org/abs/2404.11474 ยท HTML: https://mlsanity.com/paper/2404.11474/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. - Builds on 28 of its 45 references that are in the map. - First posted on arXiv 17 Apr 2024. ## Placed in - Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/) ## Builds on (most cited first, 28 of 28) - [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) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021) - [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022) - [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021) - [BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion](https://mlsanity.com/paper/2307.10816/) (2023) - [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017) - [U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation](https://mlsanity.com/paper/1907.10830/) (2019) - [3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos](https://mlsanity.com/paper/2403.01444/) (2024) - [Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping](https://mlsanity.com/paper/1809.05852/) (2018) - [ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models](https://mlsanity.com/paper/2305.16225/) (2023) - [The Spatially-Correlative Loss for Various Image Translation Tasks](https://mlsanity.com/paper/2104.00854/) (2021) - [A Style-Aware Content Loss for Real-time HD Style Transfer](https://mlsanity.com/paper/1807.10201/) (2018) - [ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank](https://mlsanity.com/paper/2312.06135/) (2023) - [An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis](https://mlsanity.com/paper/2311.11919/) (2023) - [Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion](https://mlsanity.com/paper/2209.13360/) (2022) - [Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2404.04785/) (2024) - [AesUST: Towards Aesthetic-Enhanced Universal Style Transfer](https://mlsanity.com/paper/2208.13016/) (2022) - [VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs](https://mlsanity.com/paper/2304.13386/) (2023) - [Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation](https://mlsanity.com/paper/2308.12968/) (2023) - [Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption](https://mlsanity.com/paper/2309.03729/) (2023) - [Creative Painting with Latent Diffusion Models](https://mlsanity.com/paper/2209.14697/) (2022) - [Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning](https://mlsanity.com/paper/2303.13133/) (2023) ## Built on by (most cited first, 16 of 16) - [No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves](https://mlsanity.com/paper/2505.02831/) (2025) - [Distribution Matching Distillation Meets Reinforcement Learning](https://mlsanity.com/paper/2511.13649/) (2025) - [Diffusion-Based Visual Art Creation: A Survey and New Perspectives](https://mlsanity.com/paper/2408.12128/) (2024) - [FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance](https://mlsanity.com/paper/2408.08189/) (2024) - [U-StyDiT: Ultra-high Quality Artistic Style Transfer Using Diffusion Transformers](https://mlsanity.com/paper/2503.08157/) (2025) - [RAGAR: Retrieval Augmented Personalized Image Generation Guided by Recommendation](https://mlsanity.com/paper/2505.01657/) (2025) - [A Critical Assessment of Modern Generative Models' Ability to Replicate Artistic Styles](https://mlsanity.com/paper/2502.15856/) (2025) - [Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation](https://mlsanity.com/paper/2508.08949/) (2025) - [Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning](https://mlsanity.com/paper/2404.13584/) (2024) - [DyArtbank: Diverse Artistic Style Transfer via Pre-trained Stable Diffusion and Dynamic Style Prompt Artbank](https://mlsanity.com/paper/2503.08392/) (2025) - [SPAST: Arbitrary Style Transfer with Style Priors via Pre-trained Large-scale Model](https://mlsanity.com/paper/2505.08695/) (2025) - [EmoStyle: Emotion-Driven Image Stylization](https://mlsanity.com/paper/2512.05478/) (2025) - [StyMam: A Mamba-Based Generator for Artistic Style Transfer](https://mlsanity.com/paper/2601.12954/) (2026) - [Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis](https://mlsanity.com/paper/2503.15060/) (2025) - [Language-Grounded Multi-Domain Image Translation via Semantic Difference Guidance](https://mlsanity.com/paper/2601.07221/) (2026) - [LLM-driven Multimodal Recommendation](https://mlsanity.com/paper/2602.05474/) (2026) ## Abstract (arXiv) Artistic style transfer aims to transfer the learned artistic style onto an arbitrary content image, generating artistic stylized images. Existing generative adversarial network-based methods fail to generate highly realistic stylized images and always introduce obvious artifacts and disharmonious patterns. Recently, large-scale pre-trained diffusion models opened up a new way for generating highly realistic artistic stylized images. However, diffusion model-based methods generally fail to preserve the content structure of input content images well, introducing some undesired content structure and style patterns. To address the above problems, we propose a novel pre-trained diffusion-based artistic style transfer method, called LSAST, which can generate highly realistic artistic stylized images while preserving the content structure of input content images well, without bringing obvious artifacts and disharmonious style patterns. Specifically, we introduce a Step-aware and Layer-aware Prompt Space, a set of learnable prompts, which can learn the style information from the collection of artworks and dynamically adjusts the input images' content structure and style pattern. To train our prompt space, we propose a novel inversion method, called Step-ware and Layer-aware Prompt Inversion, which allows the prompt space to learn the style information of the artworks collection. In addition, we inject a pre-trained conditional branch of ControlNet into our LSAST, which further improved our framework's ability to maintain content structure. Extensive experiments demonstrate that our proposed method can generate more highly realistic artistic stylized images than the state-of-the-art artistic style transfer methods.