# FreeTuner: Any Subject in Any Style with Training-free Diffusion

arXiv: https://arxiv.org/abs/2405.14201 · HTML: https://mlsanity.com/paper/2405.14201/

## Key facts

- Cited by 24 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 54 of its 59 references that are in the map.
- First posted on arXiv 23 May 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, 30 of 54)

- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [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)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [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)
- [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)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [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)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://mlsanity.com/paper/2305.14720/) (2023)
- [Blended Latent Diffusion](https://mlsanity.com/paper/2206.02779/) (2022)
- [ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation](https://mlsanity.com/paper/2302.13848/) (2023)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)

## Built on by (most cited first, 24 of 24)

- [InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation](https://mlsanity.com/paper/2407.00788/) (2024)
- [K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs](https://mlsanity.com/paper/2502.18461/) (2025)
- [OmniStyle: Filtering High Quality Style Transfer Data at Scale](https://mlsanity.com/paper/2505.14028/) (2025)
- [StyleSSP: Sampling StartPoint Enhancement for Training-free Diffusion-based Method for Style Transfer](https://mlsanity.com/paper/2501.11319/) (2025)
- [Personalized Image Generation with Deep Generative Models: A Decade Survey](https://mlsanity.com/paper/2502.13081/) (2025)
- [LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation](https://mlsanity.com/paper/2412.05148/) (2024)
- [DreamPoster: A Unified Framework for Image-Conditioned Generative Poster Design](https://mlsanity.com/paper/2507.04218/) (2025)
- [Adv-CPG: A Customized Portrait Generation Framework with Facial Adversarial Attacks](https://mlsanity.com/paper/2503.08269/) (2025)
- [ConsisLoRA: Enhancing Content and Style Consistency for LoRA-based Style Transfer](https://mlsanity.com/paper/2503.10614/) (2025)
- [Training-Free Multi-Style Fusion Through Reference-Based Adaptive Modulation](https://mlsanity.com/paper/2509.18602/) (2025)
- [Training-free Color-Style Disentanglement for Constrained Text-to-Image Synthesis](https://mlsanity.com/paper/2409.02429/) (2024)
- [Flow Matching Posterior Sampling: A Training-free Conditional Generation for Flow Matching](https://mlsanity.com/paper/2411.07625/) (2024)
- [AIComposer: Any Style and Content Image Composition via Feature Integration](https://mlsanity.com/paper/2507.20721/) (2025)
- [TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models](https://mlsanity.com/paper/2601.08011/) (2026)
- [Di2Pose: Discrete Diffusion Model for Occluded 3D Human Pose Estimation](https://mlsanity.com/paper/2405.17016/) (2024)
- [Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging](https://mlsanity.com/paper/2503.07363/) (2025)
- [Zero-to-Hero: Zero-Shot Initialization Empowering Reference-Based Video Appearance Editing](https://mlsanity.com/paper/2505.23134/) (2025)
- [OmniStyle2: Learning to Stylize by Learning to Destylize](https://mlsanity.com/paper/2509.05970/) (2025)
- [Mixture of Style Experts for Diverse Image Stylization](https://mlsanity.com/paper/2603.16649/) (2026)
- [Less is More: Masking Elements in Image Condition Features Avoids Content Leakages in Style Transfer Diffusion Models](https://mlsanity.com/paper/2502.07466/) (2025)
- [MONKEY: Masking ON KEY-Value Activation Adapter for Personalization](https://mlsanity.com/paper/2510.07656/) (2025)
- [Expanding the Content-Style Frontier: a Balanced Subspace Blending Approach for Content-Style LoRA Fusion](https://mlsanity.com/paper/2511.01355/) (2025)
- [AnyStyle: A Single LoRA is Sufficient for Image-Guided Style Transfer](https://mlsanity.com/paper/2607.04677/) (2026)
- [h-Flow: Flexible Flow-based Image Editing via Doob's h-Transform](https://mlsanity.com/paper/2607.10800/) (2026)

## Abstract (arXiv)

With the advance of diffusion models, various personalized image generation methods have been proposed. However, almost all existing work only focuses on either subject-driven or style-driven personalization. Meanwhile, state-of-the-art methods face several challenges in realizing compositional personalization, i.e., composing different subject and style concepts, such as concept disentanglement, unified reconstruction paradigm, and insufficient training data. To address these issues, we introduce FreeTuner, a flexible and training-free method for compositional personalization that can generate any user-provided subject in any user-provided style (see Figure 1). Our approach employs a disentanglement strategy that separates the generation process into two stages to effectively mitigate concept entanglement. FreeTuner leverages the intermediate features within the diffusion model for subject concept representation and introduces style guidance to align the synthesized images with the style concept, ensuring the preservation of both the subject's structure and the style's aesthetic features. Extensive experiments have demonstrated the generation ability of FreeTuner across various personalization settings.