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@@ -15,7 +15,7 @@ However, diffusion models require noisy input images, which destroys information
 We introduce CleanDIFT, a novel method to extract noise-free, timestep-independent features by enabling diffusion models to work directly with clean input images.
-The approach is efficient, training on a single GPU in just 30 minutes. We publish these models alongside our paper ["CleanDIFT: Diffusion Features without Noise"](https://compvis.github.io/CleanDIFT/).
 We provide checkpoints for Stable Diffusion 1.5 and Stable Diffusion 2.1.

 We introduce CleanDIFT, a novel method to extract noise-free, timestep-independent features by enabling diffusion models to work directly with clean input images.
+The approach is efficient, training on a single GPU in just 30 minutes. We publish these models alongside our paper ["CleanDIFT: Diffusion Features without Noise"](https://compvis.github.io/cleandift/).
 We provide checkpoints for Stable Diffusion 1.5 and Stable Diffusion 2.1.