Slide 22
Slide 22 text
# Initialization
xt = random.normal(0, 1, (3,H,W))
# Reverse diffusion process
for t in range(T, 0, -1):
# estimate mean
mu = estimate_mean(model, xt, t)
# use fixed sigma
sigma = beta[t] ** 0.5
# sample x_{t-1}
xt = mu + sigma * random.normal(0, 1, (3,H,W))
# return x_0
return xt