Method
SD v1.4
SD v1.5
LDM3D (ours)

FID↓
28.08
27.39
27.82

IS↑

34.17±0.76
34.02 ± 0.79
28.79 ± 0.49

CLIP↑

26.13 ± 2.81
26.13 ± 2.79
26.61±2.92

Table 1. Text-to-Image synthesis. Evaluation of text-conditional
image synthesis on the 512 x 512-sized MS-COCO [13] dataset
with 50 DDIM [27] steps. Our model is on par with the Stable
diffusion models with the same number of parameters (1.06B). IS
and CLIP similarity scores are averaged over 30k captions from
the MS-COCO dataset.

In addition, we investigate the relationship between key
hyperparameters and the quality of the generated images.
We plot the FID and IS scores against the classiﬁer-free
diffusion guidance scale factor (Fig. 4), the number of de-
noising steps (Fig. 5), and the training step (Fig. 7). Ad-
ditionally, we plotted the CLIP similarity score against the
classiﬁer-free diffusion guidance scale factor in see Fig. 6.
Fig. 4 indicates that the optimal classiﬁer-free diffusion
LDM3D- Latent Diffusion Model for 3D