On Synthetic Texture Datasets: Challenges, Creation, and Curation

TL;DR

This paper introduces a new large-scale, diverse texture dataset generated using text-to-image models, addressing challenges in texture synthesis, quality validation, and bias detection, to support texture-based machine learning tasks.

Contribution

The authors develop a novel pipeline for generating high-quality, diverse texture images using text prompts and diffusion models, creating the Prompted Textures Dataset (PTD) with over 246,000 images.

Findings

Texture images trigger high NSFW filter sensitivity, revealing biases.

The dataset is validated as high quality and diverse through metrics and human evaluation.

Generated textures support broad texture-based machine learning applications.

Abstract

The influence of textures on machine learning models has been an ongoing investigation, specifically in texture bias/learning, interpretability, and robustness. However, due to the lack of large and diverse texture data available, the findings in these works have been limited, as more comprehensive evaluations have not been feasible. Image generative models are able to provide data creation at scale, but utilizing these models for texture synthesis has been unexplored and poses additional challenges both in creating accurate texture images and validating those images. In this work, we introduce an extensible methodology and corresponding new dataset for generating high-quality, diverse texture images capable of supporting a broad set of texture-based tasks. Our pipeline consists of: (1) developing prompts from a range of descriptors to serve as input to text-to-image models, (2) adopting and adapting Stable Diffusion pipelines to generate and filter the corresponding images, and (3) further filtering down to the highest quality images. Through this, we create the Prompted Textures Dataset (PTD), a dataset of 246,285 texture images that span 56 textures. During the process of generating images, we find that NSFW safety filters in image generation pipelines are highly sensitive to texture (and flag up to 60\% of our texture images), uncovering a potential bias in these models and presenting unique challenges when working with texture data. Through both standard metrics and a human evaluation, we find that our dataset is high quality and diverse. Our dataset is available for download at https://zenodo.org/records/15359142.