With recent advancements in diffusion models, users can generate high-quality
images by writing text prompts in natural language. However, generating images
with desired details requires proper prompts, and it is often unclear how a
model reacts to different prompts and what the best prompts are. To help
researchers tackle these critical challenges, we introduce DiffusionDB, the
first large-scale text-to-image prompt dataset. DiffusionDB contains 14 million
images generated by Stable Diffusion using prompts and hyperparameters
specified by real users. We analyze prompts in the dataset and discuss key
properties of these prompts. The unprecedented scale and diversity of this
human-actuated dataset provide exciting research opportunities in understanding
the interplay between prompts and generative models, detecting deepfakes, and
designing human-AI interaction tools to help users more easily use these
models. DiffusionDB is publicly available at:
this https URL.

通过 DiffusionDB 这个大规模的文本引导数据集，我们可以更好地理解中自然语言提示与生成模型之间的相互作用，检测深度伪造，并设计帮助用户更轻松使用这些模型的人工智能交互工具。

DiffusionDB：一个用于文本到图像生成模型的大规模提示库数据集

DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models

Evaluation of biases in language models is often limited to synthetically
generated datasets. This dependence traces back to the need for a prompt-style
dataset to trigger specific behaviors of language models. In this paper, we
address this gap by creating a prompt dataset with respect to occupations
collected from real-world natural sentences present in Wikipedia. We aim to
understand the differences between using template-based prompts and natural
sentence prompts when studying gender-occupation biases in language models. We
find bias evaluations are very sensitive to the design choices of template
prompts, and we propose using natural sentence prompts for systematic
evaluations to step away from design choices that could introduce bias in the
observations.

本文通过创建一个基于职业的自然句子语料库来评估语言模型上的偏差，与以往只使用合成数据集的研究方法有所不同，证明使用基于自然句子的提示会比基于预设模板的提示更为准确和系统化地评估性别 - 职业偏差。