Semantica: An Adaptable Image-Conditioned Diffusion Model

Manoj Kumar, Neil Houlsby, Emiel Hoogeboom

Semantica: An Adaptable Image-Conditioned Diffusion Model: 9 upvotes on Hugging Face Daily Papers, #13 of 17 papers on 2024-05-24. Day-by-day upvote history.

We investigate the task of adapting image generative models to different datasets without finetuneing. To this end, we introduce Semantica, an image-conditioned diffusion model capable of generating images based on the semantics of a conditioning image. Semantica is trained exclusively on web-scale image pairs, that is it receives a random image from a webpage as conditional input and models another random image from the same webpage. Our experiments highlight the expressivity of pretrained image encoders and necessity of semantic-based data filtering in achieving high-quality image generation. Once trained, it can adaptively generate new images from a dataset by simply using images from that dataset as input. We study the transfer properties of Semantica on ImageNet, LSUN Churches, LSUN Bedroom and SUN397.

Paper page on Hugging Face · arXiv

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