# StyleSSP: Sampling StartPoint Enhancement for Training-free Diffusion-based Method for Style Transfer

arXiv: https://arxiv.org/abs/2501.11319 · HTML: https://mlsanity.com/paper/2501.11319/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 52 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Builds on (most cited first, 30 of 41)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://mlsanity.com/paper/2305.14720/) (2023)
- [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014)
- [Inversion-Based Style Transfer with Diffusion Models](https://mlsanity.com/paper/2211.13203/) (2022)
- [BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion](https://mlsanity.com/paper/2403.06976/) (2024)
- [Arbitrary Style Transfer with Style-Attentional Networks](https://mlsanity.com/paper/1812.02342/) (2018)
- [Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer](https://mlsanity.com/paper/2312.09008/) (2023)
- [FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling](https://mlsanity.com/paper/2310.15169/) (2023)
- [InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation](https://mlsanity.com/paper/2404.02733/) (2024)
- [Style Transfer by Relaxed Optimal Transport and Self-Similarity](https://mlsanity.com/paper/1904.12785/) (2019)
- [InstructDiffusion: A Generalist Modeling Interface for Vision Tasks](https://mlsanity.com/paper/2309.03895/) (2023)
- [Diffusion-based Image Translation using Disentangled Style and Content Representation](https://mlsanity.com/paper/2209.15264/) (2022)
- [Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyond](https://mlsanity.com/paper/2304.04968/) (2023)
- [Online and Linear-Time Attention by Enforcing Monotonic Alignments](https://mlsanity.com/paper/1704.00784/) (2017)
- [StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing](https://mlsanity.com/paper/2303.15649/) (2023)

## Built on by (most cited first, 23 of 23)

- [Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics](https://mlsanity.com/paper/2507.18015/) (2025)
- [AttenST: A Training-Free Attention-Driven Style Transfer Framework with Pre-Trained Diffusion Models](https://mlsanity.com/paper/2503.07307/) (2025)
- [FreeViS: Training-free Video Stylization with Inconsistent References](https://mlsanity.com/paper/2510.01686/) (2025)
- [TherA: Thermal-Aware Visual-Language Prompting for Controllable RGB-to-Thermal Infrared Translation](https://mlsanity.com/paper/2602.19430/) (2026)
- [FantasyStyle: Controllable Stylized Distillation for 3D Gaussian Splatting](https://mlsanity.com/paper/2508.08136/) (2025)
- [CleanStyle: Plug-and-Play Style Conditioning Purification for Text-to-Image Stylization](https://mlsanity.com/paper/2602.20721/) (2026)
- [Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation](https://mlsanity.com/paper/2510.25279/) (2025)
- [StyleGallery: Training-free and Semantic-aware Personalized Style Transfer from Arbitrary Image References](https://mlsanity.com/paper/2603.10354/) (2026)
- [GIDE: Unlocking Diffusion LLMs for Precise Training-Free Image Editing](https://mlsanity.com/paper/2603.21176/) (2026)
- [Inversion-Free Style Transfer with Dual Rectified Flows](https://mlsanity.com/paper/2511.20986/) (2025)
- [FREE-Switch: Frequency-based Dynamic LoRA Switch for Style Transfer](https://mlsanity.com/paper/2604.10023/) (2026)
- [MAST: Mask-Guided Attention Control for Training-Free Regional-Multi Style Transfer](https://mlsanity.com/paper/2604.12281/) (2026)
- [Structured 3D Latents Are Surprisingly Powerful: Unleashing Generalizable Style with 2D Diffusion](https://mlsanity.com/paper/2605.04412/) (2026)
- [Scheduled Style Injection: Expanding the Style-Content Pareto Frontier in Training-Free Diffusion-based Style Transfer](https://mlsanity.com/paper/2605.26538/) (2026)
- [DDTNet: Degradation Disentanglement and Transfer Network for Test-Time All-in-One De-weathering Adaptation](https://mlsanity.com/paper/2606.16298/) (2026)
- [COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models](https://mlsanity.com/paper/2606.28696/) (2026)
- [PoseAlign: Sculpting Pose-Consistent Meshes via Text-Guided Deformation](https://mlsanity.com/paper/2607.10560/) (2026)
- [Anchoring and Steering Diffusion: Enhancing the Faithfulness of Text-to-Image Generation at Inference Time](https://mlsanity.com/paper/2607.26647/) (2026)
- [FreeShadow: Training-Free Shadow Removal via Illumination Transfer and Selective Content Preservation in Diffusion Models](https://mlsanity.com/paper/2607.26715/) (2026)
- [Staying True to the Origin: Continuous Image Stylization with Smooth Transitions](https://mlsanity.com/paper/2608.08125/) (2026)
- [Through Van Gogh's Eyes: Global Style Transfer with Diffusion Model](https://mlsanity.com/paper/2608.11546/) (2026)
- [Text2Thermal: Physics-Aware Thermal Image Synthesis from Textual Priors](https://mlsanity.com/paper/2609.03585/) (2026)
- [SafeStyle: Calibrated Style Residual Injection for Controllable Style-Leakage Trade-off in Diffusion Stylization](https://mlsanity.com/paper/2609.21242/) (2026)

## Abstract (arXiv)

Training-free diffusion-based methods have achieved remarkable success in style transfer, eliminating the need for extensive training or fine-tuning. However, due to the lack of targeted training for style information extraction and constraints on the content image layout, training-free methods often suffer from layout changes of original content and content leakage from style images. Through a series of experiments, we discovered that an effective startpoint in the sampling stage significantly enhances the style transfer process. Based on this discovery, we propose StyleSSP, which focuses on obtaining a better startpoint to address layout changes of original content and content leakage from style image. StyleSSP comprises two key components: (1) Frequency Manipulation: To improve content preservation, we reduce the low-frequency components of the DDIM latent, allowing the sampling stage to pay more attention to the layout of content images; and (2) Negative Guidance via Inversion: To mitigate the content leakage from style image, we employ negative guidance in the inversion stage to ensure that the startpoint of the sampling stage is distanced from the content of style image. Experiments show that StyleSSP surpasses previous training-free style transfer baselines, particularly in preserving original content and minimizing the content leakage from style image. Project page: https://github.com/bytedance/StyleSSP.