IntroStyle: Training-Free Introspective Style Attribution using Diffusion Features

• URL: http://arxiv.org/abs/2412.14432v1
• Date: Thu, 19 Dec 2024 01:21:23 GMT
• Title: IntroStyle: Training-Free Introspective Style Attribution using Diffusion Features
• Authors: Anand Kumar, Jiteng Mu, Nuno Vasconcelos,
• Abstract summary: We present a training-free framework to solve the style attribution problem, using the features produced by a diffusion model alone. This is denoted as introspective style attribution (IntroStyle) and demonstrates superior performance to state-of-the-art models for style retrieval. We also introduce a synthetic dataset of Style Hacks (SHacks) to isolate artistic style and evaluate fine-grained style attribution performance.
• Score: 89.95303251220734
• License:
• Abstract: Text-to-image (T2I) models have gained widespread adoption among content creators and the general public. However, this has sparked significant concerns regarding data privacy and copyright infringement among artists. Consequently, there is an increasing demand for T2I models to incorporate mechanisms that prevent the generation of specific artistic styles, thereby safeguarding intellectual property rights. Existing methods for style extraction typically necessitate the collection of custom datasets and the training of specialized models. This, however, is resource-intensive, time-consuming, and often impractical for real-time applications. Moreover, it may not adequately address the dynamic nature of artistic styles and the rapidly evolving landscape of digital art. We present a novel, training-free framework to solve the style attribution problem, using the features produced by a diffusion model alone, without any external modules or retraining. This is denoted as introspective style attribution (IntroStyle) and demonstrates superior performance to state-of-the-art models for style retrieval. We also introduce a synthetic dataset of Style Hacks (SHacks) to isolate artistic style and evaluate fine-grained style attribution performance.

Related papers

• SVP: Style-Enhanced Vivid Portrait Talking Head Diffusion Model [64.28263381647628]
  Talking Head Generation (THG) is an important task with broad application prospects in various fields such as digital humans, film production, and virtual reality. We propose a novel framework named Style-Enhanced Vivid Portrait (SVP) which fully leverages style-related information in THG. Our model generates diverse, vivid, and high-quality videos with flexible control over intrinsic styles, outperforming existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-05T06:27:32Z)
• MuseumMaker: Continual Style Customization without Catastrophic Forgetting [50.12727620780213]
  We propose MuseumMaker, a method that enables the synthesis of images by following a set of customized styles in a never-end manner. When facing with a new customization style, we develop a style distillation loss module to extract and learn the styles of the training data for new image generation. It can minimize the learning biases caused by content of new training images, and address the catastrophic overfitting issue induced by few-shot images.
  arXiv  Detail & Related papers  (2024-04-25T13:51:38Z)
• FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art Commissions [3.1676484382068315]
  FedStyle is a style-based federated learning crowdsourcing framework. It allows artists to train local style models and share model parameters rather than artworks for collaboration. It addresses extreme data heterogeneity by having artists learn their abstract style representations and align with the server.
  arXiv  Detail & Related papers  (2024-04-25T04:53:43Z)
• Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models [47.19481598385283]
  ArtSavant is a tool to determine the unique style of an artist by comparing it to a reference dataset of works from WikiArt. We then perform a large-scale empirical study to provide quantitative insight on the prevalence of artistic style copying across 3 popular text-to-image generative models.
  arXiv  Detail & Related papers  (2024-04-11T17:59:43Z)
• 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)
• HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models [84.12784265734238]
  The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. We propose HiCAST, which is capable of explicitly customizing the stylization results according to various source of semantic clues. A novel learning objective is leveraged for video diffusion model training, which significantly improve cross-frame temporal consistency.
  arXiv  Detail & Related papers  (2024-01-11T12:26:23Z)
• ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank [9.99530386586636]
  Artistic style transfer aims to repaint the content image with the learned artistic style. Existing artistic style transfer methods can be divided into two categories: small model-based approaches and pre-trained large-scale model-based approaches. We propose ArtBank, a novel artistic style transfer framework, to generate highly realistic stylized images.
  arXiv  Detail & Related papers  (2023-12-11T05:53:40Z)
• Few-shots Portrait Generation with Style Enhancement and Identity Preservation [3.6937810031393123]
  StyleIdentityGAN model can ensure the identity and artistry of the generated portrait at the same time. Style-enhanced module focuses on artistic style features decoupling and transferring to improve the artistry of generated virtual face images. Experiments demonstrate the superiority of StyleIdentityGAN over state-of-art methods in artistry and identity effects.
  arXiv  Detail & Related papers  (2023-03-01T10:02:12Z)

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.