the general image-text domain. The largest dataset size of many speciﬁc problems (e.g., object
shape/normal, pose understanding, etc.) is often under 100k, i.e., 5 × 104 times smaller than LAION-
5B. This would require robust neural network training method to avoid overﬁtting and to preserve
generalization ability when the large models are trained for speciﬁc problems.
Second, when image processing tasks are handled with data-driven solutions, large computation
clusters are not always available. This makes fast training methods important for optimizing large
models to speciﬁc tasks within an acceptable amount of time and memory space (e.g., on personal
devices). This would further require the utilization of pretrained weights, as well as ﬁne-tuning
strategies or transfer learning.
Third, various image processing problems have diverse forms of problem deﬁnitions, user controls,
or image annotations. When addressing these problems, although an image diffusion algorithm can
Adding Conditional Control to Text-to-Image Diffusion Models