A Data Perspective on Enhanced Identity Preservation for Diffusion Personalization

• URL: http://arxiv.org/abs/2311.04315v4
• Date: Wed, 06 Nov 2024 05:35:40 GMT
• Title: A Data Perspective on Enhanced Identity Preservation for Diffusion Personalization
• Authors: Xingzhe He, Zhiwen Cao, Nicholas Kolkin, Lantao Yu, Kun Wan, Helge Rhodin, Ratheesh Kalarot,
• Abstract summary: Large text-to-image models have revolutionized the ability to generate imagery using natural language. This has led to interest in how to personalize a text-to-image model. We introduce a novel regularization dataset generation strategy on both the text and image level.
• Score: 25.56082131075747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large text-to-image models have revolutionized the ability to generate imagery using natural language. However, particularly unique or personal visual concepts, such as pets and furniture, will not be captured by the original model. This has led to interest in how to personalize a text-to-image model. Despite significant progress, this task remains a formidable challenge, particularly in preserving the subject's identity. Most researchers attempt to address this issue by modifying model architectures. These methods are capable of keeping the subject structure and color but fail to preserve identity details. Towards this issue, our approach takes a data-centric perspective. We introduce a novel regularization dataset generation strategy on both the text and image level. This strategy enables the model to preserve fine details of the desired subjects, such as text and logos. Our method is architecture-agnostic and can be flexibly applied on various text-to-image models. We show on established benchmarks that our data-centric approach forms the new state of the art in terms of identity preservation and text alignment.

Related papers

• Reverse Personalization [48.09783075634403]
  We analyze the identity generation process and introduce a reverse personalization framework for face anonymization.<n>Unlike prior anonymization methods, which lack control over facial attributes, our framework supports attribute-controllable anonymization.
  arXiv  Detail & Related papers  (2025-12-28T16:06:55Z)
• Personalized Image Generation with Deep Generative Models: A Decade Survey [51.26287478042516]
  We present a review of generalized personalized image generation across various generative models.<n>We first define a unified framework that standardizes the personalization process across different generative models.<n>We then provide an in-depth analysis of personalization techniques within each generative model, highlighting their unique contributions and innovations.
  arXiv  Detail & Related papers  (2025-02-18T17:34:04Z)
• Bringing Characters to New Stories: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting [71.29100512700064]
  We present T-Prompter, a training-free method for theme-specific image generation. T-Prompter integrates reference images into generative models, allowing users to seamlessly specify the target theme. Our approach enables consistent story generation, character design, realistic character generation, and style-guided image generation.
  arXiv  Detail & Related papers  (2025-01-26T19:01:19Z)
• Nested Attention: Semantic-aware Attention Values for Concept Personalization [78.90196530697897]
  We introduce Nested Attention, a novel mechanism that injects a rich and expressive image representation into the model's existing cross-attention layers. Our key idea is to generate query-dependent subject values, derived from nested attention layers that learn to select relevant subject features for each region in the generated image.
  arXiv  Detail & Related papers  (2025-01-02T18:52:11Z)
• Imagine yourself: Tuning-Free Personalized Image Generation [39.63411174712078]
  We introduce Imagine yourself, a state-of-the-art model designed for personalized image generation. It operates as a tuning-free model, enabling all users to leverage a shared framework without individualized adjustments. Our study demonstrates that Imagine yourself surpasses the state-of-the-art personalization model, exhibiting superior capabilities in identity preservation, visual quality, and text alignment.
  arXiv  Detail & Related papers  (2024-09-20T09:21:49Z)
• Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement Learning [40.06403155373455]
  We propose a novel reinforcement learning framework for personalized text-to-image generation. Our proposed approach outperforms existing state-of-the-art methods by a large margin on visual fidelity while maintaining text-alignment.
  arXiv  Detail & Related papers  (2024-07-09T08:11:53Z)
• JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation [49.997839600988875]
  Existing personalization methods rely on finetuning a text-to-image foundation model on a user's custom dataset. We propose Joint-Image Diffusion (jedi), an effective technique for learning a finetuning-free personalization model. Our model achieves state-of-the-art generation quality, both quantitatively and qualitatively, significantly outperforming both the prior finetuning-based and finetuning-free personalization baselines.
  arXiv  Detail & Related papers  (2024-07-08T17:59:02Z)
• Measuring Style Similarity in Diffusion Models [118.22433042873136]
  We present a framework for understanding and extracting style descriptors from images. Our framework comprises a new dataset curated using the insight that style is a subjective property of an image. We also propose a method to extract style attribute descriptors that can be used to style of a generated image to the images used in the training dataset of a text-to-image model.
  arXiv  Detail & Related papers  (2024-04-01T17:58:30Z)
• Training-Free Consistent Text-to-Image Generation [80.4814768762066]
  Text-to-image models can portray the same subject across diverse prompts. Existing approaches fine-tune the model to teach it new words that describe specific user-provided subjects. We present ConsiStory, a training-free approach that enables consistent subject generation by sharing the internal activations of the pretrained model.
  arXiv  Detail & Related papers  (2024-02-05T18:42:34Z)
• 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)
• Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning [6.288699905490906]
  We propose Subject-Diffusion, a novel open-domain personalized image generation model. Our method outperforms other SOTA frameworks in single, multiple, and human customized image generation.
  arXiv  Detail & Related papers  (2023-07-21T08:09:47Z)
• Evaluating Data Attribution for Text-to-Image Models [62.844382063780365]
  We evaluate attribution through "customization" methods, which tune an existing large-scale model toward a given exemplar object or style. Our key insight is that this allows us to efficiently create synthetic images that are computationally influenced by the exemplar by construction. By taking into account the inherent uncertainty of the problem, we can assign soft attribution scores over a set of training images.
  arXiv  Detail & Related papers  (2023-06-15T17:59:51Z)
• Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models [55.04969603431266]
  This paper proposes a method for generating images of customized objects specified by users. The method is based on a general framework that bypasses the lengthy optimization required by previous approaches. We demonstrate through experiments that our proposed method is able to synthesize images with compelling output quality, appearance diversity, and object fidelity.
  arXiv  Detail & Related papers  (2023-04-05T17:59:32Z)
• Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion [34.662798793560995]
  We present a simple yet highly effective approach to personalization using highly personalized (PerHi) text embedding. Our method does not require model fine-tuning or identifiers, yet still enables manipulation of background, texture, and motion with just a single image and target text.
  arXiv  Detail & Related papers  (2023-03-15T17:07:45Z)
• StyleID: Identity Disentanglement for Anonymizing Faces [4.048444203617942]
  The main contribution of the paper is the design of a feature-preserving anonymization framework, StyleID. As part of the contribution, we present a novel disentanglement metric, three complementing disentanglement methods, and new insights into identity disentanglement. StyleID provides tunable privacy, has low computational complexity, and is shown to outperform current state-of-the-art solutions.
  arXiv  Detail & Related papers  (2022-12-28T12:04:24Z)

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.