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

通过稳定的扩散实验，我们发现初始图像中的像素块倾向于生成特定的内容，我们通过修改它们可以显著影响生成的图像，这种影响只影响特定的区域，而不影响其他区域，这对于重绘任务非常有用。我们发现像素块的生成偏好主要由其值而不是位置决定，通过将具有生成用户所需内容倾向的像素块移动到用户指定的区域，我们的方法在布局到图像生成方面实现了最先进的性能，我们的结果凸显了初始图像操作在控制生成图像方面的灵活性和能力。