Training-Free Multi-Concept LoRA Composition with Prompt-Aware Weighting

• URL: http://arxiv.org/abs/2606.03792v1
• Date: Tue, 02 Jun 2026 15:42:09 GMT
• Title: Training-Free Multi-Concept LoRA Composition with Prompt-Aware Weighting
• Abstract summary: Low-Rank Adaptation (LoRA) successfully enables personalization in text-to-image generation by adapting pre-trained diffusion models to specific visual concepts and styles.<n>This paper proposes a simple yet effective approach for multi-concept customization by optimally combining the outputs of multiple LoRA modules.
• Score: 6.850015735130428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-Rank Adaptation (LoRA) successfully enables personalization in text-to-image generation by adapting pre-trained diffusion models to specific visual concepts and styles. However, extending such models to multi-concept customization remains challenging. Naively combining multiple LoRA weights or their outputs often leads to interference among concepts, resulting in degraded visual quality and reduced fidelity to the reference images of individual concepts. This paper proposes a simple yet effective approach for multi-concept customization by optimally combining the outputs of multiple LoRA modules. We leverage the relative importance of each concept during generation, as inferred from its corresponding prompt tokens and introduce two methods, W-Switch and W-Composite, that employ a prompt-aware importance weighting strategy in which each LoRA is weighted according to the semantic influence of its trigger words in the target prompt. In addition, we extend existing quantitative evaluation metrics by proposing a new image-based similarity evaluation framework that assesses image fidelity and identity preservation through comparisons between real-world reference images and automatically segmented concept regions from generated images. We evaluate our approach on the ComposLoRA testbed and demonstrate consistent improvements over existing state-of-the-art methods in terms of visual quality, identity preservation and compositionality. Qualitative evaluations, including a Large Language Model (LLM) based assessment and a user study, further validate the effectiveness of the proposed methods and align with the newly introduced quantitative image-based metrics. Our code is available at https://github.com/GeorgeTsoumplekas/Prompt-Aware-Multi-LoRA-Composition.

Related papers

• Gate-and-Merge: Zero-shot Compositional Personalization of Vision Language Models [60.25065199948178]
  We introduce Gate-and-Merge, a framework that enables compositional personalization without the need for co-occurrence training.<n>During personalization, each concept is learned independently as a lightweight LoRA adapter, paired with a concept token.<n>At inference, we enable composition by merging concept-specific LoRA updates directly in weight space.
  arXiv  Detail & Related papers  (2026-05-09T05:32:28Z)
• Beyond Heuristic Prompting: A Concept-Guided Bayesian Framework for Zero-Shot Image Recognition [81.2779530670268]
  Vision-Language Models (VLMs) have significantly advanced zero-shot image recognition.<n>This paper enhances prompt by incorporating class-specific concepts.<n>Our method consistently outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-03-09T03:11:11Z)
• Hierarchical Concept-to-Appearance Guidance for Multi-Subject Image Generation [22.845591588026366]
  We propose a framework that provides explicit, structured supervision from high-level concepts to fine-grained appearances.<n>At the conceptual level, we introduce a VAE dropout training strategy that randomly omits reference VAE features.<n>At the appearance level, we integrate the VLM-derived correspondences into a correspondence-aware masked attention module.
  arXiv  Detail & Related papers  (2026-02-03T12:13:29Z)
• Modular Customization of Diffusion Models via Blockwise-Parameterized Low-Rank Adaptation [73.16975077770765]
  Modular customization is essential for applications like concept stylization and multi-concept customization.<n>Instant merging methods often cause identity loss and interference of individual merged concepts.<n>We propose BlockLoRA, an instant merging method designed to efficiently combine multiple concepts while accurately preserving individual concepts' identity.
  arXiv  Detail & Related papers  (2025-03-11T16:10:36Z)
• LoRACLR: Contrastive Adaptation for Customization of Diffusion Models [84.04930416829264]
  LoRACLR is a novel approach for multi-concept image generation that merges multiple LoRA models, each fine-tuned for a distinct concept, into a single, unified model.<n>LoRACLR uses a contrastive objective to align and merge the weight spaces of these models, ensuring compatibility while minimizing interference.<n>Our results highlight the effectiveness of LoRACLR in accurately merging multiple concepts, advancing the capabilities of personalized image generation.
  arXiv  Detail & Related papers  (2024-12-12T18:59:55Z)
• LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models [33.379758040084894]
  Multi-concept customization emerges as the challenging task within this domain. Existing approaches often rely on training a fusion matrix of multiple Low-Rank Adaptations (LoRAs) to merge various concepts into a single image. LoRA-Composer is a training-free framework designed for seamlessly integrating multiple LoRAs.
  arXiv  Detail & Related papers  (2024-03-18T09:58:52Z)
• Visual Concept-driven Image Generation with Text-to-Image Diffusion Model [65.96212844602866]
  Text-to-image (TTI) models have demonstrated impressive results in generating high-resolution images of complex scenes.<n>Recent approaches have extended these methods with personalization techniques that allow them to integrate user-illustrated concepts.<n>However, the ability to generate images with multiple interacting concepts, such as human subjects, as well as concepts that may be entangled in one, or across multiple, image illustrations remains illusive.<n>We propose a concept-driven TTI personalization framework that addresses these core challenges.
  arXiv  Detail & Related papers  (2024-02-18T07:28:37Z)
• NEUCORE: Neural Concept Reasoning for Composed Image Retrieval [16.08214739525615]
  We propose a NEUral COncept REasoning model which incorporates multi-modal concept alignment and progressive multimodal fusion over aligned concepts. Our proposed approach is evaluated on three datasets and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2023-10-02T17:21:25Z)
• TSIT: A Simple and Versatile Framework for Image-to-Image Translation [103.92203013154403]
  We introduce a simple and versatile framework for image-to-image translation. We provide a carefully designed two-stream generative model with newly proposed feature transformations. This allows multi-scale semantic structure information and style representation to be effectively captured and fused by the network. A systematic study compares the proposed method with several state-of-the-art task-specific baselines, verifying its effectiveness in both perceptual quality and quantitative evaluations.
  arXiv  Detail & Related papers  (2020-07-23T15:34:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.