We train the super-resolution stages on a dataset of 64M
high-quality text-video pairs using the masked modeling
objective of MAGVIT [74], with token factorization into
k = 2 groups [75]. During inference, we use the sam-
pling algorithm of MAGVIT-v2 [75] with 24 sampling steps
for each stage and classiﬁer-free guidance scale [7, 31]
of 4.0/8.0 for the text condition and 1.0/2.0 for the low-
resolution condition, in the ﬁrst/second stage.
A.3. Comparison of 1B and 8B models
In Figure 14, we show outputs of 1B and 8B parameter mod-
els on the same prompts. Four frames from the best video
output of each model in a batch of four text-to-video sam-
ples were selected to represent the model. In the ﬁrst row,
the 1B model is unstable with large changes to the subject
over time and misses elements from the complex prompt.
This prompt was originally used for scaling comparisons
in [73], and compared to a dedicated image-only model, our
VideoPoet