  Beyond the superiority of the text-to-image diffusion model in generating
high-quality images, recent studies have attempted to uncover its potential for
adapting the learned semantic knowledge to visual perception tasks. In this
work, instead of translating a generative diffusion model into a visual
perception model, we explore to retain the generative ability with the
perceptive adaptation. To accomplish this, we present Zippo, a unified
framework for zipping the color and transparency distributions into a single
diffusion model by expanding the diffusion latent into a joint representation
of RGB images and alpha mattes. By alternatively selecting one modality as the
condition and then applying the diffusion process to the counterpart modality,
Zippo is capable of generating RGB images from alpha mattes and predicting
transparency from input images. In addition to single-modality prediction, we
propose a modality-aware noise reassignment strategy to further empower Zippo
with jointly generating RGB images and its corresponding alpha mattes under the
text guidance. Our experiments showcase Zippo's ability of efficient
text-conditioned transparent image generation and present plausible results of
Matte-to-RGB and RGB-to-Matte translation.
