GPAI Ledger › Seedance 2.5 (ByteDance) › Capture 13 Sep 2026

Seedance 2.5 — capture 20260913T115303Z

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Public
Summary
of
Training
Data
Content

for
Seedance
2.5

Version
of
the
Summary:

 1.0

Last
update:

 31
July
2026

1. General
information

1.1 Provider
identification


Provider
name
and
contact

details:


ByteDance
Nexus
AI
Pte.
Ltd
(ByteDance)

Authorised
representative

name
and
contact
details:

Mikros
Information
Technology
Ireland
Limited



1.2 Model
identification


Versioned
model
name(s):
 Seedance
2.5

Model
dependencies:
 N/A

Date
of
placement
of
the

model
on
the
Union

market:


July
2026

1.3 Modalities,
overall
training
data
size
and
other
characteristics

Modality



Training
data
size



Types
of
content


Text



1
billion
to
10
trillion
tokens
 The
model
was
pre-trained
using
a

large-scale,
diverse
collection
of
data

spanning
a
wide-range
of
domains


and
modalities,
including
textual

captions.


Image
 More
than
1
billion
images The
model
was
pre-trained
using
a

large-scale,
diverse
collection
of
data

spanning
a
wide-range
of
domains

and
modalities,
including

infographics
and
imagery.


Audio
 More
than
1
million
hours
 Most
audio
content
originates
from

the
associated
video
files,
and
only
a

limited
subset
consists
of
standalone

audio.

Video

 More
than
1
million
hours
 Seedance
2.5's
training
dataset

comprised
video
clips.

Other
 N/A
 N/A

Latest
date
of
data

acquisition/collection
for

model
training:

June
2026



Description
of
the
linguistic

characteristics
of
the
overall

training
data:


No
specific
geographic
region
was
intentionally
excluded
from
the

data
collection
process.
Video
data
contains
a
diverse
range
of
global

languages,
including
languages
from
within
Europe.



Other
relevant
characteristics

of
the
overall
training
data:

Seedance
2.5's
training
dataset
comprised
video
clips.

For
model
training
purposes,
ByteDance
uses
a
large-scale
mixture
of

publicly
available
data,
licensed
data,
and
synthetic
data,
designed
to

ensure
broad
coverage
of
domains
and
contexts.


The
dataset
has
been
curated
with
the
objective
of
maximizing

representativeness
and
robustness,
while
applying
filtering

mechanisms
to
remove
low-quality
or
harmful
content
where

appropriate.

Additional
comments

(optional):

N/A

2. List
of
Data
Sources

2.1 Publicly
available
datasets





Have
you
used
publicly
available

datasets
to
train
the
model?



Yes



If
yes,
specify
the
modality(ies)

of
the
content
covered
by
the

datasets
concerned:

Image
and
video



List
of
large
publicly
available

datasets:


The
training
data
corpus
comprises
a
mix
of
publicly
available
and

licensed
data.


This
may
include
image
and
video
datasets
made
available
by
third

parties
through
public
repositories
and
online
platforms.
As

described
above,
such
datasets
are
curated
and
pre-processed
with

the
objective
of
maximizing
representativeness
and
robustness,

while
applying
filtering
mechanisms
to
remove
low-quality
or

harmful
content
where
appropriate.


General
description
of
other

publicly
available
datasets
not

listed
above:

See
the
description
of
linguistic
characteristics
and
other
relevant

characteristics
of
overall
training
data
in
Section
1.


Additional
comments
(optional):
 N/A

2.2 Private
non-publicly
available
datasets
obtained
from
third

parties

2.2.1 Datasets
commercially
licensed
by
rightsholders
or
their

representatives

Have
you
concluded
transactional

commercial
licensing
agreement(s)
with

rightsholder(s)
or
with
their

representatives?

Yes



If
yes,
specify
the
modality(ies)
of
the

content
covered
by
the
datasets

concerned:


Image,
video
and
audio

2.2.2 Private
datasets
obtained
from
other
third
parties

Have
you
obtained
private
datasets
from

third
parties
that
are
not
licensed
as

described
in
Section
2.2.1,
such
as
data

Yes


obtained
from
providers
of
private

databases,
or
data
intermediaries?

If
yes,
specify
the
modality(ies)
of
the

content
covered
by
the
datasets

concerned:


Image,
video
and
audio

If
publicly
known,
list
private
datasets

obtained
from
other
third
parties:


N/A

General
description
of
non-publicly

known
private
datasets
obtained
from

third
parties


We
have
obtained
datasets
from
third-parties
on
a
licensed

basis.
Such
datasets
comprise
content
across
image,
video

and
audio
modalities.

Additional
comments
(optional):
 N/A

2.3 Data
crawled
and
scraped
from
online
sources

Were
crawlers
used
by
the
provider
or

on
behalf
of?

Yes

If
yes,
specify
crawler

name(s)/identifier(s):

Bytespider

Purposes
of
the
crawler(s):
 In
the
context
of
ByteDance's
AI
model
development,

Bytespider
is
used
to
collect
publicly
accessible
image,
video

and
audio
data
to
support
training,
testing,
and
validation
of

the
AI
model.


General
description
of
crawler

behaviour:


ByteDance's
crawler
is
configured
to
avoid
overloading

websites
and
to
operate
in
a
manner
consistent
with
ethical

web
scraping
practices.
Our
crawler
is
designed
to
respect

robots.txt
instructions,
and
not
to
circumvent
control

measures
such
as
paywalls
or
to
access
password-protected

content.



Period
of
data
collection:
 Up
to
June
2026

Comprehensive
description
of
the
type

of
content
and
online
sources
crawled:

As
above,
we
may
use
our
crawler
to
collect
publicly

accessible
data.
The
crawled
content
primarily
consists
of

videos,
images
and
audio.


Type
of
modality
covered:
 Video,
images
and
audio.

Summary
of
the
most
relevant
domain

names
crawled:

The
most
relevant
domains
crawled
includes
resources

spanning
natural
scenes,
objects,
people
and
diagrams.


Additional
comments
(optional):
 N/A

2.4 User
data

Was
data
from
user
interactions
with
the
AI
model

(e.g.
user
input
and
prompts)
used
to
train
the

model?

No

Was
data
collected
from
user
interactions
with
the

provider’s
other
services
or
products
used
to
train

the
model?


No

If
yes,
provide
a
general
description
of
the

provider’s
services
or
products
that
were
used
to

collect
the
user
data:

N/A

Type
of
modality
covered:
 N/A

Additional
comments
(optional):
 N/A

2.5 Synthetic
data

Was
synthetic
AI-generated
data
created
by
the

provider
or
on
their
behalf
to
train
the
model?



Yes

If
yes,
modality
of
the
synthetic
data:
 Text
(description
of
videos
and
images)

If
yes,
specify
the
general-purpose
AI
model(s)
used

to
generate
the
synthetic
data
if
available
on
the

market:

Synthetic
data
was
generated
using
a
range
of

general-purpose
AI
models,
including
vision

language
models
and
large
language
models.



Information
about
other
AI
models,
including

provider’s
own
AI
model(s)
not
available
on
the

market,
used
to
generate
synthetic
data
to
train
the

model
to
which
this
Summary
applies:

We
may
use
fine-tuned
versions
of
internal

models
to
generate
synthetic
data.

Additional
comments
(optional):
 N/A

2.6 Other
sources
of
data

No


Have
data
sources
other
than
those
described
in

Sections
2.1
to
2.5
been
used
to
train
the
model?


If
yes,
provide
a
narrative
description
of
these
data

sources
and
the
data:



N/A

Additional
comments
(optional):
 N/A

3. Data
processing
aspects

3.1 Respect
of
reservation
of
rights
from
text
and
data
mining

exception
or
limitation


Are
you
a
Signatory
to
the
Code
of
Practice
for
general-
purpose
AI
models
that
includes
commitments
to
respect

reservations
of
rights
from
the
TDM
exception
or
limitation?


No



Describe
the
measures
implemented
before
model
training

to
respect
reservations
of
rights
from
the
TDM
exception
or

limitation
before
and
during
data
collection,
including
the

opt-out
protocols
and
solutions
honoured
by
the
provider

or,
as
applicable,
by
third
parties
from
which
datasets
have

been
obtained:



ByteDance's
crawler
is
configured
to
avoid

overloading
websites
and
to
operate
in
a

manner
consistent
with
ethical
web

scraping
practices.
Our
crawler
is

designed
to
respect
robots.txt

instructions,
and
not
to
circumvent

control
measures
such
as
paywalls
or
to

access
password-protected
content.


Additional
comments
(optional):
 N/A

3.2 Removal
of
illegal
content

General
description
of
measures
taken:
 As
mentioned,
we
applied
preprocessing
and
filtering

methods,
including
filtering
models,
to
exclude
unsafe
or

harmful
content.


3.3 Other
information
(optional)

Other
relevant
information
about
data

processing
(optional):

N/A