# Professional outfits for existing adult identities

**Snapshot: 16/4,640 sprites uploaded, 16 generated.** Planned:464 existing adult human identities,19–95 years old,each with ten unique professional outfits. These images reuse personality IDs and do not add new identities. The entire character dataset target becomes **12,140 images across840 identities**.

Every adult receives one job from each of ten families. Each of the100 professions has46 or47 images (60 professions have46;40 have47). Exactly equal counts are arithmetically impossible for464×10 images divided among100 jobs. Within every age/gender cell,each job's count also differs by at most1. Job allocation is independent of intended heritage and does not restrict occupations by gender. Age/heritage/gender describe intended prompt labels,not inferred appearance or real employment. All teen/child and special-creature categories are excluded.

## All100 jobs

| ID | Beruf | English title | Family | Planned sprites |
|---|---|---|---|---:|
| `job-001` | Arzt/Ärztin | physician | `health` | 47 |
| `job-002` | Chirurg/Chirurgin | surgeon | `health` | 47 |
| `job-003` | Pflegefachkraft | nurse | `health` | 46 |
| `job-004` | Rettungssanitäter/in | paramedic | `health` | 46 |
| `job-005` | Apotheker/in | pharmacist | `health` | 46 |
| `job-006` | Zahnarzt/Zahnärztin | dentist | `health` | 46 |
| `job-007` | Medizinische Laborfachkraft | medical laboratory technician | `health` | 47 |
| `job-008` | Radiologie-Fachkraft | radiographer | `health` | 46 |
| `job-009` | Physiotherapeut/in | physiotherapist | `health` | 47 |
| `job-010` | Psychologe/Psychologin | psychologist | `health` | 46 |
| `job-011` | Feuerwehrkraft | firefighter | `emergency` | 47 |
| `job-012` | Polizeikraft | police officer | `emergency` | 47 |
| `job-013` | Kriminalermittler/in | detective | `emergency` | 46 |
| `job-014` | Sicherheitskraft | security guard | `emergency` | 46 |
| `job-015` | Küstenwache | coast guard officer | `emergency` | 47 |
| `job-016` | Rettungsschwimmer/in | lifeguard | `emergency` | 47 |
| `job-017` | Bergungs- und Suchrettungskraft | search and rescue worker | `emergency` | 46 |
| `job-018` | Katastrophenschutzkraft | civil defense worker | `emergency` | 46 |
| `job-019` | Park-Ranger/in | park ranger | `emergency` | 46 |
| `job-020` | Justizvollzugskraft | corrections officer | `emergency` | 46 |
| `job-021` | Kassierer/in | cashier | `retail_hospitality` | 47 |
| `job-022` | Supermarktmitarbeiter/in | supermarket worker | `retail_hospitality` | 46 |
| `job-023` | Bäcker/in | baker | `retail_hospitality` | 46 |
| `job-024` | Fleischer/in | butcher | `retail_hospitality` | 46 |
| `job-025` | Fischhändler/in | fishmonger | `retail_hospitality` | 46 |
| `job-026` | Florist/in | florist | `retail_hospitality` | 47 |
| `job-027` | Servicekraft im Restaurant | restaurant server | `retail_hospitality` | 46 |
| `job-028` | Barmann/Barfrau | bartender | `retail_hospitality` | 47 |
| `job-029` | Barista | barista | `retail_hospitality` | 46 |
| `job-030` | Hotel-Rezeptionist/in | hotel receptionist | `retail_hospitality` | 47 |
| `job-031` | Elektriker/in | electrician | `construction` | 46 |
| `job-032` | Installateur/in | plumber | `construction` | 46 |
| `job-033` | Zimmerer/Zimmerin | carpenter | `construction` | 47 |
| `job-034` | Maurer/in | bricklayer | `construction` | 46 |
| `job-035` | Maler/in | painter | `construction` | 46 |
| `job-036` | Dachdecker/in | roofer | `construction` | 47 |
| `job-037` | Fliesenleger/in | tiler | `construction` | 47 |
| `job-038` | Glaser/in | glazier | `construction` | 47 |
| `job-039` | Schlosser/in | locksmith | `construction` | 46 |
| `job-040` | Bauleiter/in | construction foreman | `construction` | 46 |
| `job-041` | Zerspanungsmechaniker/in | machinist | `industrial_logistics` | 46 |
| `job-042` | Schweißer/in | welder | `industrial_logistics` | 46 |
| `job-043` | Kfz-Mechaniker/in | auto mechanic | `industrial_logistics` | 47 |
| `job-044` | Klima- und Heizungstechniker/in | HVAC technician | `industrial_logistics` | 46 |
| `job-045` | Lagerfachkraft | warehouse worker | `industrial_logistics` | 47 |
| `job-046` | Gabelstaplerfahrer/in | forklift operator | `industrial_logistics` | 46 |
| `job-047` | Lkw-Fahrer/in | truck driver | `industrial_logistics` | 47 |
| `job-048` | Paketzusteller/in | delivery courier | `industrial_logistics` | 47 |
| `job-049` | Abfallwirtschaftskraft | sanitation worker | `industrial_logistics` | 46 |
| `job-050` | Bahn-Instandhaltungskraft | railway maintenance mechanic | `industrial_logistics` | 46 |
| `job-051` | Postzusteller/in | postal worker | `civic_transport` | 46 |
| `job-052` | Busfahrer/in | bus driver | `civic_transport` | 46 |
| `job-053` | Lokführer/in | train driver | `civic_transport` | 46 |
| `job-054` | Taxifahrer/in | taxi driver | `civic_transport` | 46 |
| `job-055` | Flugbegleiter/in | flight attendant | `civic_transport` | 47 |
| `job-056` | Linienpilot/in | airline pilot | `civic_transport` | 47 |
| `job-057` | Kommunale Instandhaltungskraft | municipal maintenance worker | `civic_transport` | 47 |
| `job-058` | Straßenreinigungskraft | street cleaner | `civic_transport` | 47 |
| `job-059` | Verkehrsüberwachungskraft | traffic warden | `civic_transport` | 46 |
| `job-060` | Verwaltungsangestellte/r | municipal clerk | `civic_transport` | 46 |
| `job-061` | Bankangestellte/r | bank teller | `office_finance` | 46 |
| `job-062` | Buchhalter/in | accountant | `office_finance` | 47 |
| `job-063` | Geldeintreiber/in | debt collection agent | `office_finance` | 47 |
| `job-064` | Versicherungsberater/in | insurance agent | `office_finance` | 47 |
| `job-065` | Immobilienmakler/in | real estate agent | `office_finance` | 46 |
| `job-066` | Rechtsanwalt/Rechtsanwältin | lawyer | `office_finance` | 47 |
| `job-067` | Richter/in | judge | `office_finance` | 46 |
| `job-068` | Finanzberater/in | financial adviser | `office_finance` | 46 |
| `job-069` | Büroleiter/in | office manager | `office_finance` | 46 |
| `job-070` | Übersetzer/in | translator | `office_finance` | 46 |
| `job-071` | Landwirt/in | farmer | `outdoors_animals` | 47 |
| `job-072` | Gärtner/in | gardener | `outdoors_animals` | 46 |
| `job-073` | Forstwirt/in | forester | `outdoors_animals` | 47 |
| `job-074` | Fischereifachkraft | fishery worker | `outdoors_animals` | 46 |
| `job-075` | Tierpfleger/in | animal caretaker | `outdoors_animals` | 46 |
| `job-076` | Tierarzt/Tierärztin | veterinarian | `outdoors_animals` | 46 |
| `job-077` | Landschaftsgärtner/in | landscape contractor | `outdoors_animals` | 47 |
| `job-078` | Winzer/in | viticulturist | `outdoors_animals` | 46 |
| `job-079` | Imker/in | beekeeper | `outdoors_animals` | 46 |
| `job-080` | Meeresbiologe/Meeresbiologin | marine biologist | `outdoors_animals` | 47 |
| `job-081` | Lehrer/in | school teacher | `science_education` | 46 |
| `job-082` | Hochschuldozent/in | university lecturer | `science_education` | 47 |
| `job-083` | Forschungswissenschaftler/in | research scientist | `science_education` | 47 |
| `job-084` | Astronom/in | astronomer | `science_education` | 46 |
| `job-085` | Archäologe/Archäologin | archaeologist | `science_education` | 46 |
| `job-086` | Vermessungstechniker/in | surveyor | `science_education` | 46 |
| `job-087` | Architekt/in | architect | `science_education` | 46 |
| `job-088` | Softwareentwickler/in | software developer | `science_education` | 47 |
| `job-089` | Journalist/in | journalist | `science_education` | 47 |
| `job-090` | Bibliothekar/in | librarian | `science_education` | 46 |
| `job-091` | Koch/Köchin | chef | `creative_culture` | 47 |
| `job-092` | Friseur/in | hairdresser | `creative_culture` | 47 |
| `job-093` | Barbier/in | barber | `creative_culture` | 47 |
| `job-094` | Schneider/in | tailor | `creative_culture` | 46 |
| `job-095` | Schuhmacher/in | shoe repairer | `creative_culture` | 46 |
| `job-096` | Fotograf/in | photographer | `creative_culture` | 46 |
| `job-097` | Tontechniker/in | sound technician | `creative_culture` | 46 |
| `job-098` | Bühnentechniker/in | stage technician | `creative_culture` | 46 |
| `job-099` | Museumskurator/in | museum curator | `creative_culture` | 47 |
| `job-100` | Musiklehrer/in | music teacher | `creative_culture` | 46 |


The [job CSV](metadata/job-outfits/jobs.csv),[full outfit allocation CSV](metadata/job-outfits/outfits.csv),[allocation checks](metadata/job-outfits/allocation.json) and [exact prompt/seed plan](metadata/job-outfits/image-plan.json) document every assignment. Clothing can suggest a profession but does not visually prove it; several civilian office roles naturally share similar clothes. Uniforms are fictional and unbranded,without weapons or readable insignia. Debt collection is represented by ordinary fully clothed civilian clothing and a calm expression,not violent conduct. Hair and face stay visible; protective headgear is intentionally omitted in these portrait assets.

## Identity and inference

Each outfit edit independently references the same person's original neutral default PNG. The full-size reference checksum and source TAR/member are recorded; actual conditioning uses416×624 PIL BICUBIC. Only clothes change,with face,hair,skin tone,body proportions,pose,framing,lighting and a neutral slightly friendly expression explicitly preserved. Small model-dependent changes can remain. All garments are opaque and fully covering; greens and teals are avoided for chroma keying. Outputs use the selected warm-painted Ghibli-inspired style,832×1,248 RGB,50 real-CFG4 steps,BF16,the undistilled [FLUX.2-klein-base-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B) and the linked [rank64 character LoRA](https://huggingface.co/laion/FLUX.2-klein-base-9B-vivarium-loras).

New TARs live in `job-data/jobs-gN-NNNNN.tar`,typically20 images with smaller pilot/final shards. Each stable sample ID `<personality_id>-job-NNN` has adjacent `.png`,`.json` and `.txt` members. JSON includes profession,base/planned outfit,identity,prompt/negative prompt,seed,reference SHA256/source location,actual inference parameters and timing. `jobs` isolates this family after the first upload; `default` includes all uploaded families. Identity-based splits must keep every emotion/outfit of the same personality ID together.

```python
from datasets import load_dataset
ds=load_dataset("laion/Vivarium-Greenscreen-Characters-FLUX-9B","jobs",split="train",streaming=True)
sample=next(iter(ds))
print(sample["json"]["personality_id"],sample["json"]["job_title_en"])
```

Portable inference keeps reference bytes in memory and writes a TAR:

```bash
python tools/infer_job_outfit_tar.py --id person-0281-job-001 --output physician.tar
```

Use the original pinned dependencies and model setup. The generator can load the neutral reference from the public source TAR using the checksum/range index or accept `--reference` with its PNG. Progress,shard manifests,inference records,review and integrity reports are published under `metadata/job-outfits/`. The first16 shared-GPU/CPU-offload pilot timings are separated from dedicated-worker throughput.