IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models

• URL: http://arxiv.org/abs/2403.13535v2
• Date: Thu, 21 Mar 2024 02:31:58 GMT
• Title: IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models
• Authors: Siying Cui, Jia Guo, Xiang An, Jiankang Deng, Yongle Zhao, Xinyu Wei, Ziyong Feng,
• Abstract summary: IDAdapter is a tuning-free approach that enhances the diversity and identity preservation in personalized image generation from a single face image. During the training phase, we incorporate mixed features from multiple reference images of a specific identity to enrich identity-related content details.
• Score: 31.762112403595612
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Leveraging Stable Diffusion for the generation of personalized portraits has emerged as a powerful and noteworthy tool, enabling users to create high-fidelity, custom character avatars based on their specific prompts. However, existing personalization methods face challenges, including test-time fine-tuning, the requirement of multiple input images, low preservation of identity, and limited diversity in generated outcomes. To overcome these challenges, we introduce IDAdapter, a tuning-free approach that enhances the diversity and identity preservation in personalized image generation from a single face image. IDAdapter integrates a personalized concept into the generation process through a combination of textual and visual injections and a face identity loss. During the training phase, we incorporate mixed features from multiple reference images of a specific identity to enrich identity-related content details, guiding the model to generate images with more diverse styles, expressions, and angles compared to previous works. Extensive evaluations demonstrate the effectiveness of our method, achieving both diversity and identity fidelity in generated images.

Related papers

• DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation [22.599542105037443]
  DisEnvisioner is a novel approach for effectively extracting and enriching the subject-essential features while filtering out -irrelevant information. Specifically, the feature of the subject and other irrelevant components are effectively separated into distinctive visual tokens, enabling a much more accurate customization. Experiments demonstrate the superiority of our approach over existing methods in instruction response (editability), ID consistency, inference speed, and the overall image quality.
  arXiv  Detail & Related papers  (2024-10-02T22:29:14Z)
• Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models [66.05234562835136]
  We present MuDI, a novel framework that enables multi-subject personalization. Our main idea is to utilize segmented subjects generated by a foundation model for segmentation. Experimental results show that our MuDI can produce high-quality personalized images without identity mixing.
  arXiv  Detail & Related papers  (2024-04-05T17:45:22Z)
• Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm [31.06269858216316]
  We propose Infinite-ID, an ID-semantics decoupling paradigm for identity-preserved personalization. We introduce an identity-enhanced training, incorporating an additional image cross-attention module to capture sufficient ID information. We also introduce a feature interaction mechanism that combines a mixed attention module with an AdaIN-mean operation to seamlessly merge the two streams.
  arXiv  Detail & Related papers  (2024-03-18T13:39:53Z)
• 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)
• StableIdentity: Inserting Anybody into Anywhere at First Sight [57.99693188913382]
  We propose StableIdentity, which allows identity-consistent recontextualization with just one face image. We are the first to directly inject the identity learned from a single image into video/3D generation without finetuning.
  arXiv  Detail & Related papers  (2024-01-29T09:06:15Z)
• PortraitBooth: A Versatile Portrait Model for Fast Identity-preserved Personalization [92.90392834835751]
  PortraitBooth is designed for high efficiency, robust identity preservation, and expression-editable text-to-image generation. PortraitBooth eliminates computational overhead and mitigates identity distortion. It incorporates emotion-aware cross-attention control for diverse facial expressions in generated images.
  arXiv  Detail & Related papers  (2023-12-11T13:03:29Z)
• When StyleGAN Meets Stable Diffusion: a $\mathscr{W}_+$ Adapter for Personalized Image Generation [60.305112612629465]
  Text-to-image diffusion models have excelled in producing diverse, high-quality, and photo-realistic images. We present a novel use of the extended StyleGAN embedding space $mathcalW_+$ to achieve enhanced identity preservation and disentanglement for diffusion models. Our method adeptly generates personalized text-to-image outputs that are not only compatible with prompt descriptions but also amenable to common StyleGAN editing directions.
  arXiv  Detail & Related papers  (2023-11-29T09:05:14Z)
• PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models [19.519789922033034]
  PhotoVerse is an innovative methodology that incorporates a dual-branch conditioning mechanism in both text and image domains. After a single training phase, our approach enables generating high-quality images within only a few seconds.
  arXiv  Detail & Related papers  (2023-09-11T19:59:43Z)
• Identity Encoder for Personalized Diffusion [57.1198884486401]
  We propose an encoder-based approach for personalization. We learn an identity encoder which can extract an identity representation from a set of reference images of a subject. We show that our approach consistently outperforms existing fine-tuning based approach in both image generation and reconstruction.
  arXiv  Detail & Related papers  (2023-04-14T23:32:24Z)

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.