More Control for Free! Image Synthesis with Semantic Diffusion Guidance [article]

Xihui Liu, Dong Huk Park, Samaneh Azadi, Gong Zhang, Arman Chopikyan, Yuxiao Hu, Humphrey Shi, Anna Rohrbach, Trevor Darrell
<span title="2022-04-14">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Controllable image synthesis models allow creation of diverse images based on text instructions or guidance from a reference image. Recently, denoising diffusion probabilistic models have been shown to generate more realistic imagery than prior methods, and have been successfully demonstrated in unconditional and class-conditional settings. We investigate fine-grained, continuous control of this model class, and introduce a novel unified framework for semantic diffusion guidance, which allows
more &raquo; ... ther language or image guidance, or both. Guidance is injected into a pretrained unconditional diffusion model using the gradient of image-text or image matching scores. We explore CLIP-based language guidance as well as both content and style-based image guidance in a unified framework. Our text-guided synthesis approach can be applied to datasets without associated text annotations. We conduct experiments on FFHQ and LSUN datasets, and show results on fine-grained text-guided image synthesis, synthesis of images related to a style or content reference image, and examples with both textual and image guidance.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:2112.05744v3</a> <a target="_blank" rel="external noopener" href="">fatcat:vlb23oi6ybh6hizkulhaqphuo4</a> </span>
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