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

• URL: http://arxiv.org/abs/2405.13870v1
• Date: Wed, 22 May 2024 17:53:38 GMT
• Title: FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept Composition
• Authors: Ganggui Ding, Canyu Zhao, Wen Wang, Zhen Yang, Zide Liu, Hao Chen, Chunhua Shen,
• Abstract summary: 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.
• Score: 49.2208591663092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• AttenCraft: Attention-guided Disentanglement of Multiple Concepts for Text-to-Image Customization [4.544788024283586]
  AttenCraft is an attention-guided method for multiple concept disentanglement. We introduce Uniform sampling and Reweighted sampling schemes to alleviate the non-synchronicity of feature acquisition from different concepts. Our method outperforms baseline models in terms of image-alignment, and behaves comparably on text-alignment.
  arXiv  Detail & Related papers  (2024-05-28T08:50:14Z)
• MC$^2$: Multi-concept Guidance for Customized Multi-concept Generation [49.935634230341904]
  We introduce the Multi-concept guidance for Multi-concept customization, termed MC$2$, for improved flexibility and fidelity. MC$2$ decouples the requirements for model architecture via inference time optimization. It adaptively refines the attention weights between visual and textual tokens, directing image regions to focus on their associated words.
  arXiv  Detail & Related papers  (2024-04-08T07:59:04Z)
• Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models [85.14042557052352]
  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.
  arXiv  Detail & Related papers  (2024-04-05T06:41:27Z)
• Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition [47.07564907486087]
  Recent text-to-image diffusion models are able to learn and synthesize images containing novel, personalized concepts. This paper tackles two interconnected issues within this realm of personalizing text-to-image diffusion models.
  arXiv  Detail & Related papers  (2024-02-23T18:55:09Z)
• 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)
• 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.