TokenVerse: Versatile Multi-concept Personalization in Token Modulation Space
- URL: http://arxiv.org/abs/2501.12224v1
- Date: Tue, 21 Jan 2025 15:49:29 GMT
- Title: TokenVerse: Versatile Multi-concept Personalization in Token Modulation Space
- Authors: Daniel Garibi, Shahar Yadin, Roni Paiss, Omer Tov, Shiran Zada, Ariel Ephrat, Tomer Michaeli, Inbar Mosseri, Tali Dekel,
- Abstract summary: TokenVerse is a method for multi-concept personalization, leveraging a pre-trained text-to-image diffusion model.
Our framework can disentangle complex visual elements and attributes from as little as a single image.
Our work exploits a DiT-based text-to-image model, in which the input text affects the generation through both attention and modulation.
- Score: 36.9351027463136
- License:
- Abstract: We present TokenVerse -- a method for multi-concept personalization, leveraging a pre-trained text-to-image diffusion model. Our framework can disentangle complex visual elements and attributes from as little as a single image, while enabling seamless plug-and-play generation of combinations of concepts extracted from multiple images. As opposed to existing works, TokenVerse can handle multiple images with multiple concepts each, and supports a wide-range of concepts, including objects, accessories, materials, pose, and lighting. Our work exploits a DiT-based text-to-image model, in which the input text affects the generation through both attention and modulation (shift and scale). We observe that the modulation space is semantic and enables localized control over complex concepts. Building on this insight, we devise an optimization-based framework that takes as input an image and a text description, and finds for each word a distinct direction in the modulation space. These directions can then be used to generate new images that combine the learned concepts in a desired configuration. We demonstrate the effectiveness of TokenVerse in challenging personalization settings, and showcase its advantages over existing methods. project's webpage in https://token-verse.github.io/
Related papers
- 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) - 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) - ELITE: Encoding Visual Concepts into Textual Embeddings for Customized
Text-to-Image Generation [59.44301617306483]
We propose a learning-based encoder for fast and accurate customized text-to-image generation.
Our method enables high-fidelity inversion and more robust editability with a significantly faster encoding process.
arXiv Detail & Related papers (2023-02-27T14:49:53Z) - Improving Visual Quality of Image Synthesis by A Token-based Generator
with Transformers [51.581926074686535]
We present a new perspective of achieving image synthesis by viewing this task as a visual token generation problem.
The proposed TokenGAN has achieved state-of-the-art results on several widely-used image synthesis benchmarks.
arXiv Detail & Related papers (2021-11-05T12:57:50Z)
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.