EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models

Awe" "Amusement" "Contentment" "Anger" "Disgust" "Fear" "Excitement" "Sadness" * Corresponding author tions. Attribute loss and emotion confidence are further proposed to ensure the semantic diversity and emotion fidelity of the generated images. Our method outperforms the stateof-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. Project