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we present diffusion guided domain adaptation of image generators a novel method to use a text to image diffusion model as a guidance to fine tune an image generator previous works on domain adaptation shows generators can be shifted into new domains indicated by text prompts without one single gr truth sample from the target domain for example from photo realistic human face domain to any painting phase or pixel phase this method use clip loss to minimize the distance between generated image and the text prompts however clip is not a generative model and could be a suboptimal solution to guide a image generator our method instead use a text to image diffusion model as a Guidance the model on the left part is a standard Stan image G generator the model on the right part is our textual image diffusion model as a Guidance the generator takes in a lat noise and map it into an image the image is mapped into the latent space through the image encoder a noise is being sampled and added to th
