  In this paper, we investigate the emotion manipulation capabilities of
diffusion models with "in-the-wild" images, a rather unexplored application
area relative to the vast and rapidly growing literature for image-to-image
translation tasks. Our proposed method encapsulates several pieces of prior
work, with the most important being Latent Diffusion models and text-driven
manipulation with CLIP latents. We conduct extensive qualitative and
quantitative evaluations on AffectNet, demonstrating the superiority of our
approach in terms of image quality and realism, while achieving competitive
results relative to emotion translation compared to a variety of GAN-based
counterparts. Code is released as a publicly available repo.
