IntroStyle: Training-Free Introspective Style Attribution using Diffusion Features

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.

Single-shot multispectral quantitative phase imaging using deep neural network

Multi-spectral quantitative phase imaging (MS-QPI) is a cutting-edge label-free technique to determine the morphological changes, refractive index variations and spectroscopic information of the specimens. The bottleneck to implement this technique to extract quantitative information, is the need of more than two measurements for generating MS-QPI images. We propose a single-shot MS-QPI technique using highly spatially sensitive digital holographic microscope assisted with deep neural network (DNN). Our method first acquires the interferometric datasets corresponding to multiple wavelengths ({\lambda}=532, 633 and 808 nm used here). The acquired datasets are used to train generative adversarial network (GAN) to generate multi-spectral quantitative phase maps from a single input interferogram. The network is trained and validated on two different samples, the optical waveguide and a MG63 osteosarcoma cells. Further, validation of the framework is performed by comparing the predicted phase maps with experimentally acquired and processed multi-spectral phase maps. The current MS-QPI+DNN framework can further empower spectroscopic QPI to improve the chemical specificity without complex instrumentation and color-cross talk.