With the presence of large text-to-image models, generating a visually appealing image may require
only a short descriptive prompt entered by users. After typing some texts and getting the images, we
may naturally come up with several questions: does this prompt-based control satisfy our needs? For
example in image processing, considering many long-standing tasks with clear problem formulations,
can these large models be applied to facilitate these speciﬁc tasks? What kind of framework should
we build to handle the wide range of problem conditions and user controls? In speciﬁc tasks, can
large models preserve the advantages and capabilities obtained from billions of images?
To answer these questions, we investigate various image processing applications and have three
ﬁndings. First, the available data scale in a task-speciﬁc domain is not always as large as that in
the general image-text domain. The largest dataset size of many speciﬁc problems (e.g., object
Adding Conditional Control to Text-to-Image Diffusion Models