Method
Stable Diffusion
VQGAN [19](seg.)*
LDM [72](seg.)*
PITI [89](seg.)
ControlNet-lite
ControlNet

FID ↓ CLIP-score ↑ CLIP-aes. ↑
6.09
26.28
25.35
19.74
17.92
15.27

0.26
0.17
0.18
0.20
0.26
0.26

6.32
5.14
5.15
5.77
6.30
6.31

Table 3: Evaluation for image generation conditioned by
semantic segmentation. We report FID, CLIP text-image
score, and CLIP aesthetic scores for our method and other
baselines. We also report the performance of Stable Diffu-
sion without segmentation conditions. Methods marked with
“*” are trained from scratch.
AddingConditionalControltoText-to-ImageDiffusionModels