GPAI Ledger › FLUX.3 (Black Forest Labs) › Capture 17 Aug 2026

FLUX.3 — capture 20260817T084108Z

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Template  for  the  Public  Summary  of  Training  Content  for
General-Purpose

AI

models

 This  template  is  provided  by  the  European  Commission  and  required  to  be  filled  in  by  providers  of  general-purpose  AI  models  prior  to  their  placing  on  the  Union  market  in  order  to  comply  with  their  obligation  under  Article  53  (1)(d)  of  Regulation  (EU)  2024/1689  (AI  Act).   For  more  information  and  guidance  see  Commission’s  Explanatory  Notice  and  Template  for  the  Public  Summary  of  Training  Content  for  general-purpose  AI  models  |  Shaping  Europe’s  digital  future.
 Version  of  the  Summary:   1.0   Last  update:   1  August  2026   General  information
1.  General  information  1.1.  Provider  identification
Provider  name  and  contact  details:
Black  Forest  Labs  Inc.,  2261  Market  Street,  Suite  22997,  San  Francisco,  CA  94114,  USA  (“BFL”)
Authorised  representative  name  and  contact  details:
BFL  GmbH, Ingeborg-Krummer-Schroth-Str.  18A,  4OG  79106  Freiburg,  Germany    1.2.  Model  identification   Versioned  model  name(s):  FLUX  3
Model  dependencies:
None   Date  of  placement  of  the  model  on  the  Union  market:  16  July  2026
1.3  Modalities,  overall  training  data  size  and  other  characteristics  This  Section  requires  general  information  about  the  overall  training  data  after  pre-processing  and  before  the  training  of  the  model.    Modality  Select  the  modalities  present  in  the  training  data,  to  the  extent  that  they  are  identifiable
Training  data  size  For  each  selected  modality,  select  the  range  within  which  the  estimated  total  training  data  size  for  that  modality  falls.  Dynamic  datasets  may  be  excluded  from  the  estimation.
Types  of  content  For  each  selected  modality,  provide  a  general  description  of  the  type  of  content  that  has  been  included  in  the  training  data.
 x   Text
☐  Less  than  1  billion  tokens  x  1billion  to  10  trillions  tokens  ☐  More  than  10  trillions  tokens

 The  FLUX  3  text  training  corpus  comprises  primarily  multi-domain,  supervised  fine-tuned  image-text  or  video-text  caption  dataset  collections  curated  for  quality,  together  with  deidentified,  aggregated  text-based  information  derived  from  user’s  interactions  with  Black  Forest  Labs  services  as  further  set  out  in  Section  2.4.
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x  Image
☐  Less  than  1  million  images  ☐  1Million  to1  billion  images  x  More  than  1  billion  images
The  FLUX  3  image  training  corpus  comprises  primarily  diverse,  acquired,  open,  safety-filtered,  supervised  fine-tuned  and  synthetic  image  datasets.  It  includes  bespoke  images  created  by  vendors  for  specific  use  cases,  as  well  as  human  created  labelling  of  images.
x  Audio
 1

☐  Less  than  10  000  hours  x10  000  to  1  million  hours   ☐  More  than  1  million  hours
 The  FLUX  3  audio  training  corpus  comprises  primarily  diverse,  acquired,  open,  safety-filtered,  and  supervised  fine-tuned  audio  datasets.  The  datasets  include  sound  effects,  background  noise,  and  speech.
x  Video
☐  Less  than  10  000  hours  ☐  10  000  to1  million  hours   x  More  than  1  million  hours
The  FLUX  3  video  training  corpus  comprises  primarily  diverse,  acquired,  open,  safety-filtered,  supervised  fine-tuned  and  synthetic  video  datasets.  It  includes  bespoke  videos  created  by  vendors  for  specific  use  cases,  as  well  as  human  created  labelling  of  videos.
☐  Other
Specify  the  modality  and  for  each  one  indicate  approximate  size  and  unit  of  measurement
 N/A

Latest  date  of  data  acquisition/collection  for  model  training:
The  data  used  to  train  the  model  is  composed  of  different  datasets  with  varying  publication  and  cutoff  dates.  Datasets  were  acquired  as  recently  as  June  2026  for  model  training.  The  model  will  not  be  trained  on  new  data  while  in  production,  but  subsequent  versions  may  undergo  distillation  or  fine-tuning  and  be  released  as  later  versions  as  part  of  the  same  family  of  models.  Description  of  the  linguistic  characteristics  of  the  overall  training  data:
To  the  extent  there  are  identifiable  linguistic  characteristics  of  the  datasets,  the  coverage  is  multilingual,  including  EU  official  languages  with  strong  English  language  representations.
Other  relevant  characteristics  of  the  overall  training  data:
The  overall  training  corpus  is  designed  for  a  video,  image  and  action  prediction  output  model  with  multimodal  understanding  capabilities.    Additional  comments  (optional):

2.  List  of  data  sources
2.  List  of  data  sou r c es
1
 Excluding  audio  that  is  part  of  video,  as  this  should  be  reported  under  the  “video”  modality  instead.  Furthermore,  the  Commission
understands

the

modality

of

‘audio’

to

include

‘speech’.

2

This  Section  requires  information  about  specific  sources  of  data  used  to  train  the  general-purpose  AI  model.  In  this  section  “dataset”  should  be  understood  as  a  single,  pre-packaged  collection  of  data.  The  filtering  and  pre-processing  of  data  collected  from  the  same  pre-packaged  collection  should  not  be  considered  a  new  dataset  to  be  disclosed  separately  in  the  sections  below .  If  a  particular  dataset  can  be  assigned  to  more  than  one  of  the  categories  below,  providers  should  select  the  most  relevant  category  and  only  report  the  dataset  in
that

category,

except

in

the

case

of

synthetic

data

(see

Section

2.5).

  2.1.  Publicly  available  datasets    This  Section  requires  information  about  datasets  that  were  used  to  train  the  model  and  which  have  been  compiled  by  a  third  party,  are  made  available  publicly  for  free,  and  are  readily  downloadable  as  a  whole  or  in  predefined  chunks,  such  as  datasets  and  collections  available  on  public  repositories  and  online  platforms,  specialised  websites,  or  snapshots  of  common  crawl.  The  public  availability  of  the  datasets  for  free  does  not  mean  that  the  content  at  issue  is  necessarily  free  of  rights  since  it  may  be  subject  to  licensing  arrangements  or  conditions  of  use  (e.g.,  certain  free  and/or  open  licenses  may  determine  the  scope  of  the  uses,  including  prohibiting  uses  relating  to  model  training).   A  dataset  is  considered  to  be  “large”  if  the  total  data  size  for  any  one  of  the  modalities  contained  in  the  dataset  exceeds  3%  of  the  size  of  all  publicly  available  datasets  for  that  modality  used  for  training.  The  size  of  the  dataset  should  be  based  on  its  size  after  pre-processing  (for  example  filtering),  and  without  splitting  the  dataset  to  prevent  reporting  circumvention.       Have  you  used  publicly  available  datasets  to  train  the  model?    x  Yes      ☐  No  If  yes,  specify  the  modality(ies)  of  the  content  covered  by  the  datasets  concerned:
x  Text    x  Image    x  Video   ☐  Audio  ☐  Other
If

so,

please

specify…
 List  of  large  publicly  available  datasets:
N/a
General  description  of  other  publicly  available  datasets  not  listed  above:
Training  data  includes  captioning,  images,  videos,  action  prediction,  text-image  and  text-video  pairs  from  open  source  and  publicly  available  scientific  research,  technical  and  educational  repositories,  and  specialised  collections,  for  example,  Egocentric-100K  made  available  by  Build  AI  published  on  Hugging  Face  under  an  Apache  2.0  licence.  These  datasets  are  multilingual,  and  subject  to  preprocessing  such  as  quality  filtering,  deduplication,  and  safety  filtering  before  training.  We  use  data  filtering  processes  to  reduce  personally  identifiable  information  from  training  data.
Additional  comments  (optional):
BFL   uses  the  U.S.  Trade  Representative  (USTR)  Notorious  Markets  for  Counterfeiting  and  Piracy  list  as  a  signal  when  deciding  to  exclude  data  from  certain  websites  that  have  been  recognized  as  persistently  and  repeatedly  infringing  copyright.
2.2  Private  non-publicly  available  datasets  obtained  from  third  parties  This  Section  requires  information  about  private  non-publicly  available  datasets  of  third  parties  that  are  not  publicly  available  and  not  disclosed  under  Section  2.1.  These  include:  1)  datasets  for  which  transactional  commercial  licensing  agreements  were  concluded  between  the  provider  and  the  rightsholders  or  their  representatives,  including  by  collective  management  organisations  and  legitimate  content  aggregators  who  have  the  right  to  collectively  license  works  on  behalf  of  rightsholders  (Section  2.2.1);
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2)  other  private  datasets  obtained  through  data  intermediaries,  non-publicly  available  databases  and  datasets  of  third  parties  for  which  transactional  commercial  licenses  have  not  been  concluded  with  rightsholders  or  their  representatives  (Section  2.2.2).
 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?
x  Yes  ,  BFL  has  entered  into  data  access  agreements     ☐  No
If  yes,  specify  the  modality(ies)  of  the  content  covered  by  the  datasets  concerned:
☐  Text    x  Image    x  Video    x  Audio  ☐  Other
If

so,

please

specify…
 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?
x  Yes,   we  have  entered  into  data  access  agreements      ☐  No
If  yes,  specify  the  modality(ies)  of  the  content  covered  by  the  datasets  concerned:
x  Text    x  Image    x  Video   ☐  Audio  ☐  Other  If  so,  please  specify…  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
Data  acquired  from  third  party  providers  through  confidential  commercial  agreements  and  data  access  partnerships.  Data  is  collected  consistent  with  applicable  law.   Additional  comments  (optional):  N/a
2.3  Data  crawled  and  scraped  from  online  sources This  Section  requires  information  about  crawled,  scraped  data,  or  otherwise  compiled  from  online  sources  directly  by  the  provider  of  the  model  or  on  their  behalf  (i.e.  excluding  publicly  available  datasets  already  compiled  by  third  parties  and  made  available  on  platforms  such  as  common  crawl  that  are  covered  under  Section  2.1).
Were  crawlers  used  by  the  provider  or  on  behalf  of?
☐ Yes      x  No

2.4  User  data  This  Section  requires  information  about  user  data  collected  by  all  services  and  products  of  the  provider,  including  through  mail  services,  social  media  platforms,  content  platforms  or  interaction  with  the  providers’  AI  models  and/or  systems.  This  does  not  cover  data  licensed  by  users  based  on  commercial  transactional  agreements  described  in  Section  2.2.1.,  or  customer  data  to  fine-tune  models  for  specific  purposes.   Was  data  from  user  interactions  with  the  AI  model  (e.g.  user  input  and  prompts)  used  to  train  the  model?
 x  Yes      ☐  No
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Was  data  collected  from  user  interactions  with  the  provider’s  other  services  or  products  used  to  train  the  model?
 x  Yes      ☐  No
If  yes,  provide  a  general  description  of  the  provider’s  services  or  products  that  were  used  to  collect  the  user  data:
 Subject  to  user  opt-out,  data  from  user’s  interactions  with  FLUX  models  accessed  via  an  API  pursuant  to  an  agreement  may  have  been  used  to  improve  the  quality  and  capabilities  of  the  FLUX  3  model.  BFL  uses  data  filtering  and  minimisation  techniques  to  reduce  personally  identifiable  information  from  training  data.   Type  of  modality  covered:   x  Text    x  Image    ☐  Video   ☐  Audio   ☐  Other
If

so,

please

specify…
 Additional  comments  (optional):
2.5  Synthetic  data  This  Section  requires  information  about  synthetic  data  created  by  or  on  behalf  of  the  provider  for  training  the  model  directly  on  the  outputs  of  another  AI  model,  in  particular  through  model  distillation  or  model  alignment  (e.g.  AI  feedback  through  reinforcement  learning).  This  does  not  include  the  use  of  AI  models  to  clean  or  enrich  data  (e.g.  AI-generated  metadata  to  enrich  or  modify  a  dataset,  such  as  creating  depth  maps  or  text  descriptions  of  images).  In  case  this  concerns  publicly  available  datasets  as  described  in  Section  2.1,  these  should  be  reported  in  that  Section  of  the  Template.  In  case  this  concerns  synthetic  datasets  created  by  third  parties  on  behalf  of  the  provider,  these  should  be  reported  in  this  Section  of  the  Template  instead  of  in  Section  2.2.2.    Was  synthetic  AI-generated  data  created  by  the  provider  or  on  their  behalf  to  train  the  model?
 x  Yes      ☐  No
If  yes,  modality  of  the  synthetic  data:
 ☐  Text    x  Image    x  Video   ☐  Audio        ☐  Other
If

so,

please

specify…
  If  yes,  specify  the  general-purpose  AI  model(s)  used  to  generate  the  synthetic  data  if  available  on  the  market:
BFL’s  proprietary  publicly  available  FLUX.1  and  FLUX.2  suite  of  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:
Output  from  BFL  proprietary,  internal  FLUX  models  were  used  selectively  as  part  of  the   training  data  mix.  Synthetic  data  is  used  to  support  various  training  objectives  including  fine  tuning,  safety  research  and  capability  development.  Additional  comments  (optional):  N/a     2.6  Other  sources  of  data  This  Section  requires  information  about  data  that  does  not  fall  under  any  of  the  categories  in  the  previous  Sections,  for  example  data  collected  from  offline  sources,  self-digitised  media  (e.g.,  digitised  analog  text  context,  images),  datasets  labelled  by  humans  commissioned  by  the  provider,  or  human  generated  data  through  reinforcement  learning.

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Have  data  sources  other  than  those  described  in  Sections  2.1  to  2.5  been  used  to  train  the  model?   ☐ Yes      x  No

1.  Data  processing  aspects  3.  Data  processing  aspects  3.1.  Respect  of  reservation  of  rights  from  text  and  data  mining  exception  or
limitation

 This  Section  concerns  measures  implemented  by  the  provider  to  identify  and  comply  with  the  reservation  of  rights  from  the  text  and  data  mining  (TDM)  exception  or  limitation  expressed  pursuant  to  Article  4(3)  of  Directive  (EU)  2019/790,  as  outlined  in  the  copyright  policy  put  in  place  by  the  provider  in  accordance  with  Article  53(1)(c)  AI  Act.     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?
 x  Yes      ☐  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:
  Third  parties  from  which  datasets  have  been  obtained  implement  a  variety  of  measures  to  comply  with  applicable  laws.  Additional  comments  (optional):   N/a   3.2  Removal  of  illegal  content  This  Section  concerns  measures  taken  to  avoid  or  remove  illegal  content  under  Union  law  from  the  training  data  (such  as  blacklists,  keywords,  and  model-based  classifiers),  without  requiring  disclosure  of  specific  details  about  the  provider’s  internal  business  practices  or  trade  secrets.  Such  measures  are  advisable  if  the  training  data  is  likely  to  include  illegal  or  unlawful  content  under  Union  law,  in  particular  child  sexual  abuse  material  and  terrorist  content  and  the  non-authorised  use  of  material  protected  by  intellectual  property  rights.  Such  measures  do  not  include  data  selection  practices,  for  example  to  increase  the  capability  of  the  model.
General  description  of  measures  taken:
BFL  follows  applicable  laws  and  best  practices  to  remove  illegal  or  harmful  content  from  its  training  corpus  by  way  of  preprocessing,  deduplication  and  filtering.  To  learn  more  about  our  practices,  please  refer  to  our  Responsible  AI  Development  Policy  (https://bfl.ai/legal/responsible-ai-development-policy).

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