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Conditional diffusion models have demonstrated impressive performance inimage manipulation tasks. The general pipeline involves adding noise to theimage and then denoising it. However, this method faces a trade-off problem:adding too much noise affects the fidelity of the image while adding too littleaffects its editability. This largely limits their practical applicability. Inthis paper, we propose a novel framework, Selective Diffusion Distillation(SDD), that ensures both the fidelity and editability of images. Instead ofdirectly editing images with a diffusion model, we train a feedforward imagemanipulation network under the guidance of the diffusion model. Besides, wepropose an effective indicator to select the semantic-related timestep toobtain the correct semantic guidance from the diffusion model. This approachsuccessfully avoids the dilemma caused by the diffusion process. Our extensiveexperiments demonstrate the advantages of our framework. Code is released athttps://github.com/AndysonYs/Selective-Diffusion-Distillation.

Not All Steps are Created Equal: Selective Diffusion Distillation for Image Manipulation