Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models

• URL: http://arxiv.org/abs/2404.03913v1
• Date: Fri, 5 Apr 2024 06:41:27 GMT
• Title: Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models
• Authors: Gihyun Kwon, Simon Jenni, Dingzeyu Li, Joon-Young Lee, Jong Chul Ye, Fabian Caba Heilbron,
• Abstract summary: We introduce Concept Weaver, a method for composing customized text-to-image diffusion models at inference time. We show that Concept Weaver can generate multiple custom concepts with higher identity fidelity compared to alternative approaches.
• Score: 85.14042557052352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While there has been significant progress in customizing text-to-image generation models, generating images that combine multiple personalized concepts remains challenging. In this work, we introduce Concept Weaver, a method for composing customized text-to-image diffusion models at inference time. Specifically, the method breaks the process into two steps: creating a template image aligned with the semantics of input prompts, and then personalizing the template using a concept fusion strategy. The fusion strategy incorporates the appearance of the target concepts into the template image while retaining its structural details. The results indicate that our method can generate multiple custom concepts with higher identity fidelity compared to alternative approaches. Furthermore, the method is shown to seamlessly handle more than two concepts and closely follow the semantic meaning of the input prompt without blending appearances across different subjects.

Related papers

• TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation [67.97044071594257]
  TweedieMix is a novel method for composing customized diffusion models. Our framework can be effortlessly extended to image-to-video diffusion models.
  arXiv  Detail & Related papers  (2024-10-08T01:06:01Z)
• 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)
• 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)
• Pick-and-Draw: Training-free Semantic Guidance for Text-to-Image Personalization [56.12990759116612]
  Pick-and-Draw is a training-free semantic guidance approach to boost identity consistency and generative diversity for personalization methods. The proposed approach can be applied to any personalized diffusion models and requires as few as a single reference image.
  arXiv  Detail & Related papers  (2024-01-30T05:56:12Z)
• 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)
• 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)
• Multi-Concept Customization of Text-to-Image Diffusion [51.8642043743222]
  We propose Custom Diffusion, an efficient method for augmenting existing text-to-image models. We find that only optimizing a few parameters in the text-to-image conditioning mechanism is sufficiently powerful to represent new concepts. Our model generates variations of multiple new concepts and seamlessly composes them with existing concepts in novel settings.
  arXiv  Detail & Related papers  (2022-12-08T18:57:02Z)

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.