  Diffusion models (DMs) synthesize high-quality images in various domains.
However, controlling their generative process is still hazy because the
intermediate variables in the process are not rigorously studied. Recently, the
bottleneck feature of the U-Net, namely $h$-space, is found to convey the
semantics of the resulting image. It enables StyleCLIP-like latent editing
within DMs. In this paper, we explore further usage of $h$-space beyond
attribute editing, and introduce a method to inject the content of one image
into another image by combining their features in the generative processes.
Briefly, given the original generative process of the other image, 1) we
gradually blend the bottleneck feature of the content with proper
normalization, and 2) we calibrate the skip connections to match the injected
content. Unlike custom-diffusion approaches, our method does not require
time-consuming optimization or fine-tuning. Instead, our method manipulates
intermediate features within a feed-forward generative process. Furthermore,
our method does not require supervision from external networks. The code is
available at https://curryjung.github.io/InjectFusion/
