AttenCraft: Attention-guided Disentanglement of Multiple Concepts for Text-to-Image Customization

• URL: http://arxiv.org/abs/2405.17965v2
• Date: Thu, 16 Oct 2025 06:42:33 GMT
• Title: AttenCraft: Attention-guided Disentanglement of Multiple Concepts for Text-to-Image Customization
• Authors: Junjie Shentu, Matthew Watson, Noura Al Moubayed,
• Abstract summary: We propose AttenCraft, an attention-based method for multiple-concept disentanglement.<n>We introduce an adaptive algorithm based on attention scores to estimate sampling ratios for different concepts.<n>Our model effectively mitigates two issues, achieving state-of-the-art image fidelity and comparable prompt fidelity to baseline models.
• Score: 3.5066393042242123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-image (T2I) customization empowers users to adapt the T2I diffusion model to new concepts absent in the pre-training dataset. On this basis, capturing multiple new concepts from a single image has emerged as a new task, allowing the model to learn multiple concepts simultaneously or discard unwanted concepts. However, multiple-concept disentanglement remains a key challenge. Existing disentanglement models often exhibit two main issues: feature fusion and asynchronous learning across different concepts. To address these issues, we propose AttenCraft, an attention-based method for multiple-concept disentanglement. Our method uses attention maps to generate accurate masks for each concept in a single initialization step, aiding in concept disentanglement without requiring mask preparation from humans or specialized models. Moreover, we introduce an adaptive algorithm based on attention scores to estimate sampling ratios for different concepts, promoting balanced feature acquisition and synchronized learning. AttenCraft also introduces a feature-retaining training framework that employs various loss functions to enhance feature recognition and prevent fusion. Extensive experiments show that our model effectively mitigates these two issues, achieving state-of-the-art image fidelity and comparable prompt fidelity to baseline models.

Related papers

• ConceptSplit: Decoupled Multi-Concept Personalization of Diffusion Models via Token-wise Adaptation and Attention Disentanglement [15.939409734710198]
  We present ConceptSplit, a novel framework to split individual concepts through training and inference.<n>Our framework comprises two key components. First, we introduce Token-wise Value Adaptation (ToVA), a merging-free training method.<n>Second, we propose Latent Optimization for Disentangled Attention (LODA), which alleviates attention entanglement during inference.
  arXiv  Detail & Related papers  (2025-10-06T10:22:46Z)
• Mod-Adapter: Tuning-Free and Versatile Multi-concept Personalization via Modulation Adapter [57.49476151976054]
  We propose a tuning-free method for multi-concept personalization that can effectively customize both object and abstract concepts without test-time fine-tuning.<n>Our method achieves state-of-the-art performance in multi-concept personalization, supported by quantitative, qualitative, and human evaluations.
  arXiv  Detail & Related papers  (2025-05-24T09:21:32Z)
• 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)
• FlipConcept: Tuning-Free Multi-Concept Personalization for Text-to-Image Generation [26.585985828583304]
  We propose FlipConcept, a novel approach that seamlessly integrates multiple personalized concepts into a single image.<n>We introduce guided appearance attention, mask-guided noise mixing, and background dilution to minimize concept leakage.<n>Despite not requiring tuning, our method outperforms existing models in both single and multiple personalized concept inference.
  arXiv  Detail & Related papers  (2025-02-21T04:37:18Z)
• FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept Composition [49.2208591663092]
  FreeCustom is a tuning-free method to generate customized images of multi-concept composition based on reference concepts. We introduce a new multi-reference self-attention (MRSA) mechanism and a weighted mask strategy. Our method outperforms or performs on par with other training-based methods in terms of multi-concept composition and single-concept customization.
  arXiv  Detail & Related papers  (2024-05-22T17:53:38Z)
• Non-confusing Generation of Customized Concepts in Diffusion Models [135.4385383284657]
  We tackle the common challenge of inter-concept visual confusion in compositional concept generation using text-guided diffusion models (TGDMs) Existing customized generation methods only focus on fine-tuning the second stage while overlooking the first one. We propose a simple yet effective solution called CLIF: contrastive image-language fine-tuning.
  arXiv  Detail & Related papers  (2024-05-11T05:01:53Z)
• Attention Calibration for Disentangled Text-to-Image Personalization [12.339742346826403]
  We propose an attention calibration mechanism to improve the concept-level understanding of the T2I model. We demonstrate that our method outperforms the current state of the art in both qualitative and quantitative evaluations.
  arXiv  Detail & Related papers  (2024-03-27T13:31:39Z)
• 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. Recent approaches have extended these methods with personalization techniques that allow them to integrate user-illustrated concepts. 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. We propose a concept-driven TTI personalization framework that addresses these core challenges.
  arXiv  Detail & Related papers  (2024-02-18T07:28:37Z)
• Textual Localization: Decomposing Multi-concept Images for Subject-Driven Text-to-Image Generation [5.107886283951882]
  We introduce a localized text-to-image model to handle multi-concept input images. Our method incorporates a novel cross-attention guidance to decompose multiple concepts. Notably, our method generates cross-attention maps consistent with the target concept in the generated images.
  arXiv  Detail & Related papers  (2024-02-15T14:19:42Z)
• Multi-Concept T2I-Zero: Tweaking Only The Text Embeddings and Nothing Else [75.6806649860538]
  We consider a more ambitious goal: natural multi-concept generation using a pre-trained diffusion model. We observe concept dominance and non-localized contribution that severely degrade multi-concept generation performance. We design a minimal low-cost solution that overcomes the above issues by tweaking the text embeddings for more realistic multi-concept text-to-image generation.
  arXiv  Detail & Related papers  (2023-10-11T12:05:44Z)
• Break-A-Scene: Extracting Multiple Concepts from a Single Image [80.47666266017207]
  We introduce the task of textual scene decomposition. We propose augmenting the input image with masks that indicate the presence of target concepts. We then present a novel two-phase customization process.
  arXiv  Detail & Related papers  (2023-05-25T17:59:04Z)
• Key-Locked Rank One Editing for Text-to-Image Personalization [43.195870616558935]
  We present Perfusion, a T2I personalization method that addresses challenges using dynamic rank-1 updates to the underlying T2I model. Perfusion avoids overfitting by introducing a new mechanism that "locks" new concepts' cross-attention Keys to their superordinate category. We show that Perfusion outperforms strong baselines in both qualitative and quantitative terms.
  arXiv  Detail & Related papers  (2023-05-02T17:56:06Z)
• Designing an Encoder for Fast Personalization of Text-to-Image Models [57.62449900121022]
  We propose an encoder-based domain-tuning approach for text-to-image personalization. We employ two components: First, an encoder that takes as an input a single image of a target concept from a given domain. Second, a set of regularized weight-offsets for the text-to-image model that learn how to effectively ingest additional concepts.
  arXiv  Detail & Related papers  (2023-02-23T18:46:41Z)

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.