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In this paper, we investigate the emotion manipulation capabilities ofdiffusion models with "in-the-wild" images, a rather unexplored applicationarea relative to the vast and rapidly growing literature for image-to-imagetranslation tasks. Our proposed method encapsulates several pieces of priorwork, with the most important being Latent Diffusion models and text-drivenmanipulation with CLIP latents. We conduct extensive qualitative andquantitative evaluations on AffectNet, demonstrating the superiority of ourapproach in terms of image quality and realism, while achieving competitiveresults relative to emotion translation compared to a variety of GAN-basedcounterparts. Code is released as a publicly available repo.

Photorealistic and Identity-Preserving Image-Based Emotion Manipulation with Latent Diffusion Models