GPAI Ledger › GPAI Training Transparency tracker (AI Accountability Lab (AIAL)) › Capture 12 Sep 2026

ByteDance_Seedeam_5_0_2026_09_08 — capture 20260912T062129Z

Filed under AI Accountability Lab (AIAL) — GPAI Training Transparency tracker, the source this project captured it from; the document itself is the filing of the model named above.

Verify: sha256sum bbfe07316d3bfeb78f67e43c8280e887102aa2cb4926db18e1a3c9fa25fa1e7e.pdf must equal the hash above (the filename IS the expected hash); ots verify bbfe07316d3bfeb78f67e43c8280e887102aa2cb4926db18e1a3c9fa25fa1e7e.pdf.20260912T062129Z.ots -f bbfe07316d3bfeb78f67e43c8280e887102aa2cb4926db18e1a3c9fa25fa1e7e.pdf (opentimestamps.org) proves the bytes existed no later than the attestation time — an upper bound on the capture time; the fetch time above is the archive's own record (a freshly captured proof reports 'pending' here: the calendars anchor within hours, but this archive only upgrades the stored proof to its anchor on a later run, so expect a day or two). ots verify needs a local Bitcoin Core node (a pruned one is fine); without one, ots info on the proof prints the attesting block height and merkle path to check on any block explorer.

Extracted text

Machine-extracted text (layout may be lost; the authoritative content is the stored file above).

Public
Summary
of
Training
Data
Content

for
Seedream
5.0
Pro


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):
 The
Seedream
5.0
Pro
family
of
models

Model
dependencies:
 The
Seedream
5.0
Pro
family
includes
SeeDream
5.0
Pro
and
Seedream
5.0

Lite.
Both
models
are
based
on
the
same
architecture.

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



More
than
10
trillion
tokens


Seedream
5.0
Pro's
training
dataset

comprises
paired
image-text
datasets

and
interweaved
image-text
data,

including
images
with
corresponding

textual
descriptions
and
documents

with
interweaved
images
and
text.


Image



More
than
1
billion
images


Seedream
5.0
Pro's
training
dataset

comprises
paired
image-text
datasets

and
interweaved
image-text
data,

including
images
with
corresponding

textual
descriptions
and
documents

with
interweaved
images
and
text.


Audio
 N/A



N/A

Video

 N/A
 N/A

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.






Other
relevant
characteristics

of
the
overall
training
data:

Seedream
5.0
Pro's
training
dataset
comprises
paired
image-text

datasets
and
interweaved
image-text
data,
including
images
with

corresponding
textual
descriptions
and
documents
with
interweaved

images
and
text.


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



List
of
large
publicly
available

datasets:


The
training
data
corpus
comprises
a
mix
of
publicly
available
and

licensed
data.


This
may
include
image
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



Image


If
yes,
specify
the
modality(ies)
of
the
content
covered
by

the
datasets
concerned:


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

obtained
from
providers
of
private
databases,

or
data
intermediaries?

Yes

If
yes,
specify
the
modality(ies)
of
the
content

covered
by
the
datasets
concerned:


Image

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
primarily
comprise

content
across
text
and
image
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:
 Image

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
and
image

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
image

generation
models,
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

Have
data
sources
other
than
those
described
in

Sections
2.1
to
2.5
been
used
to
train
the
model?


No

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