Images can powerfully strengthen arguments, conveying ideas more immediately and compellingly than text alone. With the rise of text-to-image models, a broad audience can now generate custom visuals to illustrate their arguments. Yet a fundamental mismatch undermines this potential: these models are trained on concrete scene descriptions, while arguments operate at the level of general, abstract principles. Naively prompting such a model with an argumentative text therefore rarely produces images that genuinely illustrate the argument. To address this challenge, we propose an aspect-aware image generation approach. Given an argument, our method first identifies the key aspects that an illustrative image should convey, then constructs a detailed scene description grounded in both the argument and those aspects, and finally generates an image using that scene description as the prompt. A human-assessment evaluation demonstrates that this approach yields images that illustrate arguments significantly better than those produced by naive prompting.