# InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation

arXiv: https://arxiv.org/abs/2407.00788 · HTML: https://mlsanity.com/paper/2407.00788/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 25 references that are in the map.
- First posted on arXiv 30 Jun 2024.

## Builds on (most cited first, 24 of 24)

- [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)
- [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)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [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)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [InstantID: Zero-shot Identity-Preserving Generation in Seconds](https://mlsanity.com/paper/2401.07519/) (2024)
- [Style Aligned Image Generation via Shared Attention](https://mlsanity.com/paper/2312.02133/) (2023)
- [StyleDrop: Text-to-Image Generation in Any Style](https://mlsanity.com/paper/2306.00983/) (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)
- [InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation](https://mlsanity.com/paper/2404.02733/) (2024)
- [Implicit Style-Content Separation using B-LoRA](https://mlsanity.com/paper/2403.14572/) (2024)
- [ReNoise: Real Image Inversion Through Iterative Noising](https://mlsanity.com/paper/2403.14602/) (2024)
- [Measuring Style Similarity in Diffusion Models](https://mlsanity.com/paper/2404.01292/) (2024)
- [DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations](https://mlsanity.com/paper/2403.06951/) (2024)
- [Visual Style Prompting with Swapping Self-Attention](https://mlsanity.com/paper/2402.12974/) (2024)
- [RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control](https://mlsanity.com/paper/2405.17401/) (2024)
- [Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance](https://mlsanity.com/paper/2406.07540/) (2024)
- [FreeTuner: Any Subject in Any Style with Training-free Diffusion](https://mlsanity.com/paper/2405.14201/) (2024)

## Built on by (most cited first, 30 of 53)

- [CSGO: Content-Style Composition in Text-to-Image Generation](https://mlsanity.com/paper/2408.16766/) (2024)
- [StyleMaster: Stylize Your Video with Artistic Generation and Translation](https://mlsanity.com/paper/2412.07744/) (2024)
- [Follow-Your-Emoji-Faster: Towards Efficient, Fine-Controllable, and Expressive Freestyle Portrait Animation](https://mlsanity.com/paper/2509.16630/) (2025)
- [VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning](https://mlsanity.com/paper/2504.07960/) (2025)
- [Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement](https://mlsanity.com/paper/2411.06558/) (2024)
- [FreeEdit: Mask-free Reference-based Image Editing with Multi-modal Instruction](https://mlsanity.com/paper/2409.18071/) (2024)
- [OmniStyle: Filtering High Quality Style Transfer Data at Scale](https://mlsanity.com/paper/2505.14028/) (2025)
- [EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM](https://mlsanity.com/paper/2412.09618/) (2024)
- [StyleSSP: Sampling StartPoint Enhancement for Training-free Diffusion-based Method for Style Transfer](https://mlsanity.com/paper/2501.11319/) (2025)
- [IFAdapter: Instance Feature Control for Grounded Text-to-Image Generation](https://mlsanity.com/paper/2409.08240/) (2024)
- [UniVST: A Unified Framework for Training-free Localized Video Style Transfer](https://mlsanity.com/paper/2410.20084/) (2024)
- [Adv-CPG: A Customized Portrait Generation Framework with Facial Adversarial Attacks](https://mlsanity.com/paper/2503.08269/) (2025)
- [Color Conditional Generation with Sliced Wasserstein Guidance](https://mlsanity.com/paper/2503.19034/) (2025)
- [AttenST: A Training-Free Attention-Driven Style Transfer Framework with Pre-Trained Diffusion Models](https://mlsanity.com/paper/2503.07307/) (2025)
- [ConsisLoRA: Enhancing Content and Style Consistency for LoRA-based Style Transfer](https://mlsanity.com/paper/2503.10614/) (2025)
- [Free-Lunch Color-Texture Disentanglement for Stylized Image Generation](https://mlsanity.com/paper/2503.14275/) (2025)
- [Leveraging Semantic Attribute Binding for Free-Lunch Color Control in Diffusion Models](https://mlsanity.com/paper/2503.09864/) (2025)
- [RepText: Rendering Visual Text via Replicating](https://mlsanity.com/paper/2504.19724/) (2025)
- [FreeViS: Training-free Video Stylization with Inconsistent References](https://mlsanity.com/paper/2510.01686/) (2025)
- [StyleKeeper: Prevent Content Leakage using Negative Visual Query Guidance](https://mlsanity.com/paper/2510.06827/) (2025)
- [OmniTransfer: All-in-one Framework for Spatio-temporal Video Transfer](https://mlsanity.com/paper/2601.14250/) (2026)
- [Balanced Image Stylization with Style Matching Score](https://mlsanity.com/paper/2503.07601/) (2025)
- [A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models](https://mlsanity.com/paper/2503.13576/) (2025)
- [SplitFlux: Learning to Decouple Content and Style from a Single Image](https://mlsanity.com/paper/2511.15258/) (2025)
- [Training-free Color-Style Disentanglement for Constrained Text-to-Image Synthesis](https://mlsanity.com/paper/2409.02429/) (2024)
- [AIComposer: Any Style and Content Image Composition via Feature Integration](https://mlsanity.com/paper/2507.20721/) (2025)
- [Loom: Diffusion-Transformer for Interleaved Generation](https://mlsanity.com/paper/2512.18254/) (2025)
- [TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models](https://mlsanity.com/paper/2601.08011/) (2026)
- [Domain Generalizable Portrait Style Transfer](https://mlsanity.com/paper/2507.04243/) (2025)
- [SubZero: Composing Subject, Style, and Action via Zero-Shot Personalization](https://mlsanity.com/paper/2502.19673/) (2025)

## Abstract (arXiv)

Style transfer is an inventive process designed to create an image that maintains the essence of the original while embracing the visual style of another. Although diffusion models have demonstrated impressive generative power in personalized subject-driven or style-driven applications, existing state-of-the-art methods still encounter difficulties in achieving a seamless balance between content preservation and style enhancement. For example, amplifying the style's influence can often undermine the structural integrity of the content. To address these challenges, we deconstruct the style transfer task into three core elements: 1) Style, focusing on the image's aesthetic characteristics; 2) Spatial Structure, concerning the geometric arrangement and composition of visual elements; and 3) Semantic Content, which captures the conceptual meaning of the image. Guided by these principles, we introduce InstantStyle-Plus, an approach that prioritizes the integrity of the original content while seamlessly integrating the target style. Specifically, our method accomplishes style injection through an efficient, lightweight process, utilizing the cutting-edge InstantStyle framework. To reinforce the content preservation, we initiate the process with an inverted content latent noise and a versatile plug-and-play tile ControlNet for preserving the original image's intrinsic layout. We also incorporate a global semantic adapter to enhance the semantic content's fidelity. To safeguard against the dilution of style information, a style extractor is employed as discriminator for providing supplementary style guidance. Codes will be available at https://github.com/instantX-research/InstantStyle-Plus.