NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging

TL;DR

Successful results indicate that the crop-and-merge operation of noises is a reasonable strategy to control image generation and it is shown that NoiseCollage can be integrated with ControlNet to use edges, sketches, and pose skeletons as additional conditions.

Abstract

Layout-aware text-to-image generation is a task to generate multi-object images that reflect layout conditions in addition to text conditions. The current layout-aware text-to-image diffusion models still have several issues, including mismatches between the text and layout conditions and quality degradation of generated images. This paper proposes a novel layout-aware text-to-image diffusion model called NoiseCollage to tackle these issues. During the denoising process, NoiseCollage independently estimates noises for individual objects and then crops and merges them into a single noise. This operation helps avoid condition mismatches; in other words, it can put the right objects in the right places. Qualitative and quantitative evaluations show that NoiseCollage outperforms several state-of-the-art models. These successful results indicate that the crop-and-merge operation of noises is a reasonable strategy to control image generation. We also show that NoiseCollage can be integrated with ControlNet to use edges, sketches, and pose skeletons as additional conditions. Experimental results show that this integration boosts the layout accuracy of ControlNet. The code is available at https://github.com/univ-esuty/noisecollage.

Figures (13)

• Figure 1: Denoising processes of NoiseCollage. (Although illustrated as a process in the image space, the actual denoising process is performed in a latent space like sd for computational efficiency.)
• Figure 2: Overview of the noise estimation process in our NoiseCollage.
• Figure 3: Images generated by NoiseCollage with layout conditions $L$ and text conditions $(S, s_\ast)$.
• Figure 4: Comparison of generated images and their generation process by NoiseCollage and Collage Diffusioncollage-diffusion.
• Figure 5: Comparison of generated images by NoiseCollage and Paint-with-wordsediff.