Diffusion in Style

Martin Nicolas Everaert, Marco Bocchio, Sami Arpa, Sabine Süsstrunk, Radhakrishna Achanta; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 2251-2261


We present Diffusion in Style, a simple method to adapt Stable Diffusion to any desired style, using only a small set of target images. It is based on the key observation that the style of the images generated by Stable Diffusion is tied to the initial latent tensor. Not adapting this initial latent tensor to the style makes fine-tuning slow, expensive, and impractical, especially when only a few target style images are available. In contrast, fine-tuning is much easier if this initial latent tensor is also adapted. Our Diffusion in Style is orders of magnitude more sample-efficient and faster. It also generates more pleasing images than existing approaches, as shown qualitatively and with quantitative comparisons.

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@InProceedings{Everaert_2023_ICCV, author = {Everaert, Martin Nicolas and Bocchio, Marco and Arpa, Sami and S\"usstrunk, Sabine and Achanta, Radhakrishna}, title = {Diffusion in Style}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {2251-2261} }