  Recent years have witnessed remarkable progress in image generation task,
where users can create visually astonishing images with high-quality. However,
existing text-to-image diffusion models are proficient in generating concrete
concepts (dogs) but encounter challenges with more abstract ones (emotions).
Several efforts have been made to modify image emotions with color and style
adjustments, facing limitations in effectively conveying emotions with fixed
image contents. In this work, we introduce Emotional Image Content Generation
(EICG), a new task to generate semantic-clear and emotion-faithful images given
emotion categories. Specifically, we propose an emotion space and construct a
mapping network to align it with the powerful Contrastive Language-Image
Pre-training (CLIP) space, providing a concrete interpretation of abstract
emotions. Attribute loss and emotion confidence are further proposed to ensure
the semantic diversity and emotion fidelity of the generated images. Our method
outperforms the state-of-the-art text-to-image approaches both quantitatively
and qualitatively, where we derive three custom metrics, i.e., emotion
accuracy, semantic clarity and semantic diversity. In addition to generation,
our method can help emotion understanding and inspire emotional art design.
