Former to detect the segmentations again to compute the
reconstructed IoUs (Table 2). Besides, we use Frechet Incep-
tion Distance (FID) [28] to measure the distribution distance
over randomly generated 512×512 image sets using differ-
ent segmentation-conditioned methods, as well as text-image
CLIP scores [66] and CLIP aesthetic score [79] in Table 3.
See also the supplementary material for detailed settings.

4.4. Comparison to Previous Methods

Figure 9 presents a visual comparison of baselines and our
method (Stable Diffusion + ControlNet). Specifically, we
show the results of PITI [89], Sketch-Guided Diffusion [88],
and Taming Transformers [19]. (Note that the backbone of
PITI is OpenAI GLIDE [57] that have different visual quality
and performance.) We observe that ControlNet can robustly
handle diverse conditioning images and achieves sharp and
clean results.

4.5. Discussion
AddingConditionalControltoText-to-ImageDiffusionModels