Table 1 shows Inception scores, FID scores, and negative log likelihoods (lossless codelengths) on
CIFAR10. With our FID score of 3.17, our unconditional model achieves better sample quality than
most models in the literature, including class conditional models. Our FID score is computed with
respect to the training set, as is standard practice; when we compute it with respect to the test set, the
score is 5.24, which is still better than many of the training set FID scores in the literature.

5

Figure 3: LSUN Church samples. FID=7.89

Figure 4: LSUN Bedroom samples. FID=4.90

Algorithm 3 Sending x0
1: Send xT ∼ q(xT|x0) using p(xT )
2: for t = T − 1, . . . , 2, 1 do
3: Send xt ∼ q(xt|xt+1, x0) using pθ(xt|xt+1)
4: end for
5: Send x0 using pθ(x0|x1)

Algorithm 4 Receiving
1: Receive xT using p(xT )
2: for t = T − 1, . . . , 1, 0 do
3: Receive xt using pθ(xt|xt+1)
4: end for
5: return x0
Denoising Diffusion Probabilistic Models