Image and video tokenizer We represent images and
videos as a sequence of discrete tokens in a uniﬁed vocabu-
lary using the MAGVIT-v2 [75] tokenizer, which performs
favorably against other tokenizers [21, 64, 74] in our exper-
iments.

Speciﬁcally, a video clip is encoded and quantized into a
sequence of integers, with a decoder mapping them back to
the pixel space. As the bridge between the token and pixel
spaces, the performance of this visual tokenizer sets the
upper bound of the video generation quality. Meanwhile,
the compression ratio determines the sequence length of the
LLM for effective and efﬁcient task setups.
2.125-second
17-frame
128×128 resolution videos sampled at 8 fps to pro-
duce a latent shape of (5, 16, 16), which is then ﬂattened

MAGVIT-v2

tokenizes
VideoPoet