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Diffusion models have the ability to generate high quality images bydenoising pure Gaussian noise images. While previous research has primarilyfocused on improving the control of image generation through adjusting thedenoising process, we propose a novel direction of manipulating the initialnoise to control the generated image. Through experiments on stable diffusion,we show that blocks of pixels in the initial latent images have a preferencefor generating specific content, and that modifying these blocks cansignificantly influence the generated image. In particular, we show thatmodifying a part of the initial image affects the corresponding region of thegenerated image while leaving other regions unaffected, which is useful forrepainting tasks. Furthermore, we find that the generation preferences of pixelblocks are primarily determined by their values, rather than their position. Bymoving pixel blocks with a tendency to generate user-desired content touser-specified regions, our approach achieves state-of-the-art performance inlayout-to-image generation. Our results highlight the flexibility and power ofinitial image manipulation in controlling the generated image.

Guided Image Synthesis via Initial Image Editing in Diffusion Model