# FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept Composition

arXiv: https://arxiv.org/abs/2405.13870 · HTML: https://mlsanity.com/paper/2405.13870/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 43 references that are in the map.
- First posted on arXiv 22 May 2024.

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

- [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)
- [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)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [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)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [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)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (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)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [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)
- [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://mlsanity.com/paper/2305.14720/) (2023)
- [ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation](https://mlsanity.com/paper/2302.13848/) (2023)
- [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023)
- [Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models](https://mlsanity.com/paper/2305.18292/) (2023)
- [P+: Extended Textual Conditioning in Text-to-Image Generation](https://mlsanity.com/paper/2303.09522/) (2023)
- [Break-A-Scene: Extracting Multiple Concepts from a Single Image](https://mlsanity.com/paper/2305.16311/) (2023)
- [HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models](https://mlsanity.com/paper/2307.06949/) (2023)

## Built on by (most cited first, 30 of 49)

- [Less-to-More Generalization: Unlocking More Controllability by In-Context Generation](https://mlsanity.com/paper/2504.02160/) (2025)
- [CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design](https://mlsanity.com/paper/2505.19114/) (2025)
- [Movie Weaver: Tuning-Free Multi-Concept Video Personalization with Anchored Prompts](https://mlsanity.com/paper/2502.07802/) (2025)
- [StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements](https://mlsanity.com/paper/2412.08503/) (2024)
- [Personalize Anything for Free with Diffusion Transformer](https://mlsanity.com/paper/2503.12590/) (2025)
- [Nested Attention: Semantic-aware Attention Values for Concept Personalization](https://mlsanity.com/paper/2501.01407/) (2025)
- [CINEMA: Coherent Multi-Subject Video Generation via MLLM-Based Guidance](https://mlsanity.com/paper/2503.10391/) (2025)
- [A Unified Agentic Framework for Evaluating Conditional Image Generation](https://mlsanity.com/paper/2504.07046/) (2025)
- [Mod-Adapter: Tuning-Free and Versatile Multi-concept Personalization via Modulation Adapter](https://mlsanity.com/paper/2505.18612/) (2025)
- [ID-Patch: Robust ID Association for Group Photo Personalization](https://mlsanity.com/paper/2411.13632/) (2024)
- [AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation](https://mlsanity.com/paper/2406.05000/) (2024)
- [LatexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending](https://mlsanity.com/paper/2503.06956/) (2025)
- [VidSketch: Hand-drawn Sketch-Driven Video Generation with Diffusion Control](https://mlsanity.com/paper/2502.01101/) (2025)
- [MagicFace: Training-free Universal-Style Human Image Customized Synthesis](https://mlsanity.com/paper/2408.07433/) (2024)
- [Object Isolated Attention for Consistent Story Visualization](https://mlsanity.com/paper/2503.23353/) (2025)
- [Multi-party Collaborative Attention Control for Image Customization](https://mlsanity.com/paper/2505.01428/) (2025)
- [FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization](https://mlsanity.com/paper/2507.01792/) (2025)
- [IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting](https://mlsanity.com/paper/2501.15641/) (2025)
- [Contrastive Learning Guided Latent Diffusion Model for Image-to-Image Translation](https://mlsanity.com/paper/2503.20484/) (2025)
- [Interact-Custom: Customized Human Object Interaction Image Generation](https://mlsanity.com/paper/2508.19575/) (2025)
- [Continual Personalization for Diffusion Models](https://mlsanity.com/paper/2510.02296/) (2025)
- [Foundation Cures Personalization: Improving Personalized Models' Prompt Consistency via Hidden Foundation Knowledge](https://mlsanity.com/paper/2411.15277/) (2024)
- [FlipConcept: Tuning-Free Multi-Concept Personalization for Text-to-Image Generation](https://mlsanity.com/paper/2502.15203/) (2025)
- [FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image Generation](https://mlsanity.com/paper/2504.15958/) (2025)
- [Learning Zero-Shot Subject-Driven Video Generation Using 1% Compute](https://mlsanity.com/paper/2504.17816/) (2025)
- [ShowFlow: From Robust Single Concept to Condition-Free Multi-Concept Generation](https://mlsanity.com/paper/2506.18493/) (2025)
- [Imbalance in Balance: Online Concept Balancing in Generation Models](https://mlsanity.com/paper/2507.13345/) (2025)
- [Category-Aware 3D Object Composition with Disentangled Texture and Shape Multi-view Diffusion](https://mlsanity.com/paper/2509.02357/) (2025)
- [DreamAudio: Customized Text-to-Audio Generation with Diffusion Models](https://mlsanity.com/paper/2509.06027/) (2025)
- [Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach](https://mlsanity.com/paper/2511.19316/) (2025)

## Abstract (arXiv)

Benefiting from large-scale pre-trained text-to-image (T2I) generative models, impressive progress has been achieved in customized image generation, which aims to generate user-specified concepts. Existing approaches have extensively focused on single-concept customization and still encounter challenges when it comes to complex scenarios that involve combining multiple concepts. These approaches often require retraining/fine-tuning using a few images, leading to time-consuming training processes and impeding their swift implementation. Furthermore, the reliance on multiple images to represent a singular concept increases the difficulty of customization. To this end, we propose FreeCustom, a novel tuning-free method to generate customized images of multi-concept composition based on reference concepts, using only one image per concept as input. Specifically, we introduce a new multi-reference self-attention (MRSA) mechanism and a weighted mask strategy that enables the generated image to access and focus more on the reference concepts. In addition, MRSA leverages our key finding that input concepts are better preserved when providing images with context interactions. Experiments show that our method's produced images are consistent with the given concepts and better aligned with the input text. Our method outperforms or performs on par with other training-based methods in terms of multi-concept composition and single-concept customization, but is simpler. Codes can be found at https://github.com/aim-uofa/FreeCustom.