# Concept Conductor: Orchestrating Multiple Personalized Concepts in Text-to-Image Synthesis

arXiv: https://arxiv.org/abs/2408.03632 · HTML: https://mlsanity.com/paper/2408.03632/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 30 references that are in the map.
- First posted on arXiv 7 Aug 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)
- [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)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [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)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion](https://mlsanity.com/paper/2307.10816/) (2023)
- [Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models](https://mlsanity.com/paper/2305.18292/) (2023)
- [SpaText: Spatio-Textual Representation for Controllable Image Generation](https://mlsanity.com/paper/2211.14305/) (2022)
- [Grounded Text-to-Image Synthesis with Attention Refocusing](https://mlsanity.com/paper/2306.05427/) (2023)
- [Dense Text-to-Image Generation with Attention Modulation](https://mlsanity.com/paper/2308.12964/) (2023)
- [Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing](https://mlsanity.com/paper/2403.03431/) (2024)
- [Cones 2: Customizable Image Synthesis with Multiple Subjects](https://mlsanity.com/paper/2305.19327/) (2023)
- [Directed Diffusion: Direct Control of Object Placement through Attention Guidance](https://mlsanity.com/paper/2302.13153/) (2023)
- [Localized Text-to-Image Generation for Free via Cross Attention Control](https://mlsanity.com/paper/2306.14636/) (2023)
- [DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter](https://mlsanity.com/paper/2211.11337/) (2022)
- [Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models](https://mlsanity.com/paper/2404.03913/) (2024)

## Built on by (most cited first, 4 of 4)

- [FaR: Enhancing Multi-Concept Text-to-Image Diffusion via Concept Fusion and Localized Refinement](https://mlsanity.com/paper/2504.03292/) (2025)
- [A Training-Free Approach for Multi-ID Customization via Attention Adjustment and Spatial Control](https://mlsanity.com/paper/2511.20401/) (2025)
- [Delta-K: Boosting Multi-Instance Generation via Cross-Attention Augmentation](https://mlsanity.com/paper/2603.10210/) (2026)
- [V-Engram: Trigger-Indexed External Memory for Modular Text-to-Image Personalization](https://mlsanity.com/paper/2609.37198/) (2026)

## Abstract (arXiv)

The customization of text-to-image models has seen significant advancements, yet generating multiple personalized concepts remains a challenging task. Current methods struggle with attribute leakage and layout confusion when handling multiple concepts, leading to reduced concept fidelity and semantic consistency. In this work, we introduce a novel training-free framework, Concept Conductor, designed to ensure visual fidelity and correct layout in multi-concept customization. Concept Conductor isolates the sampling processes of multiple custom models to prevent attribute leakage between different concepts and corrects erroneous layouts through self-attention-based spatial guidance. Additionally, we present a concept injection technique that employs shape-aware masks to specify the generation area for each concept. This technique injects the structure and appearance of personalized concepts through feature fusion in the attention layers, ensuring harmony in the final image. Extensive qualitative and quantitative experiments demonstrate that Concept Conductor can consistently generate composite images with accurate layouts while preserving the visual details of each concept. Compared to existing baselines, Concept Conductor shows significant performance improvements. Our method supports the combination of any number of concepts and maintains high fidelity even when dealing with visually similar concepts. The code and models are available at https://github.com/Nihukat/Concept-Conductor.