4.4

Interpolation

We can interpolate source images x0, x(cid:48)0 ∼ q(x0) in latent space using q as a stochastic encoder,
xt, x(cid:48)t ∼ q(xt|x0), then decoding the linearly interpolated latent ¯xt = (1 − λ)x0 + λx(cid:48)0 into image
space by the reverse process, ¯x0 ∼ p(x0|¯xt). In effect, we use the reverse process to remove
artifacts from linearly interpolating corrupted versions of the source images, as depicted in Fig. 8
(left). We ﬁxed the noise for different values of λ so xt and x(cid:48)t remain the same. Fig. 8 (right)
shows interpolations and reconstructions of original CelebA-HQ 256 × 256 images (t = 500). The
reverse process produces high-quality reconstructions, and plausible interpolations that smoothly
vary attributes such as pose, skin tone, hairstyle, expression and background, but not eyewear. Larger
t results in coarser and more varied interpolations, with novel samples at t = 1000 (Appendix Fig. 9).

5 Related Work
Denoising Diffusion Probabilistic Models