The initial noise image has demonstrated a significant influence on image
generation, and manipulating the initial noise image can effectively increase
control over the generation. All of the current generation is based only on a
single initial noise drawn from a normal distribution, which may not be suited
to the desired content specified by the prompt. In this research, we propose a
novel approach using pre-collected, semantically-informed pixel blocks from
multiple initial noises for the initial image construction to enhance control
over the image generation. The inherent tendencies of these pixel blocks can
easily generate specific content, thus effectively guiding the generation
process towards the desired content. The pursuit of tailored initial image
construction inevitably leads to deviations from the normal distribution, and
our experimental results show that the diffusion model exhibits a certain
degree of tolerance towards the distribution of initial images. Our approach
achieves state-of-the-art performance in the training-free layout-to-image
synthesis task, demonstrating the adaptability of the initial image
construction in guiding the content of the generated image. Our code will be
made publicly available.

使用预先收集的、具有语义信息的像素块，通过多个初始噪声进行初始图像构建，以增强对图像生成的控制。实验结果显示了初始图像构建在引导生成图像内容方面的适应性，并在无需训练的布局到图像合成任务中取得了最先进的性能。