Generative models are increasingly integrated into creative workflows. While text-to-image generation excels in visual quality and diversity, color accessibility for users with Color Vision Deficiencies (CVD) remains largely unexplored. Our work systematically evaluates color accessibility in images generated by a common pretrained diffusion model, prompted to improve accessibility across diverse categories. We quantify performance using established, off-the-shelf CVD simulation methods and introduce "CVDLoss", a new metric measuring differences in image gradients indicative of structural detail. We validate CVDLoss against a commonly used daltonization method, demonstrating its sensitivity to color accessibility modifications. Applying CVDLoss to model outputs reveals that existing diffusion models struggle to reliably respond to accessibility-focused prompts. Consequently, our study establishes CVDLoss as a valuable evaluation tool for accessibility-aware image generation and post-processing, offering insights into current generative models' limitations in addressing color accessibility.

翻译：生成模型正日益融入创意工作流程。尽管文本到图像生成在视觉质量与多样性方面表现出色，但对于色觉缺陷用户的色彩可访问性研究仍处于探索阶段。本研究系统评估了通用预训练扩散模型生成图像的色彩可访问性，通过提示指令引导模型在多样化类别中提升可访问性。我们采用成熟的现成CVD模拟方法量化性能，并提出了"CVDLoss"——一种通过图像梯度差异衡量结构细节特征的新指标。通过对比常用色盲模拟方法，我们验证了CVDLoss对色彩可访问性修改的敏感性。将CVDLoss应用于模型输出表明，现有扩散模型难以稳定响应以可访问性为导向的提示指令。因此，本研究确立CVDLoss作为可访问性图像生成与后处理的重要评估工具，为当前生成模型在解决色彩可访问性方面的局限性提供了新的见解。