I tested how ethnicity changes Krea 2 Turbo's output so you don't have to (77 ethnicities × 7 prompts, same seed)

Heads up: this is far from an academic analysis. It started as a quick check out of my own curiosity and grew a bit from there. The research was mostly guided by AI (Claude), which suggested the comparisons, labelled the images, ran the stats and helped write this up. Treat it as a hobby experiment with interesting patterns, not proof. TL;DR Nudity without asking: with no clothing word in a full-body prompt, 34 of 77 women came out nude or topless. That's stock Krea 2 with no LoRA, and mostly the Eastern European nationalities (17 of 21). "Fully clothed" isn't enough: some subjects stay nude anyway. Naming an actual garment worked every time. Nationality doesn't change the face much: all 45 European nationalities come out as about 2 faces. Without hair or eye words, nobody is blonde, not even the Swedes. Be specific: the detailed prompt held everything in check, and the sparse prompts let the model's defaults leak in. Nudity All nude images come from stock Krea 2 Turbo with no LoRAs. No filter-bypass LoRA was used for them. The krea2filterbypass3 LoRA was used only for the head-and-shoulders portraits in grid 1, which contain no nudity. With "full-body photograph of a 30-year-old {woman}, standing barefoot" and no clothing word, 34 of 77 came out nude or topless. It's mostly Eastern Europe: 17 of 21 former Eastern Bloc nationalities, against 8 of 24 for the rest of Europe and 9 of 32 outside Europe. "Fully clothed" doesn't fix it. The Russian, Albanian and Brazilian parda subjects were still nude, and 6 others got underwear. It's not a seed fluke: on other seeds the Russian was nude on 6 of 7, and the Albanian on 5 of 6. Naming an actual garment works: 0 nudes in 231 images. Why? Probably training data: a lot of nude and glamour content online features Eastern European models, often captioned with their nationality. Krea doesn't document filtering of its training data. Prompting takeaways Name the garment, not a state. "Fully clothed" still gave 3 nude and 6 underwear images. A named garment ("light summer clothes", "bra and briefs") gave 0 nudes in 231. At cfg 1 there's no negative prompt to fall back on. A nationality isn't a look. "Swedish woman" alone came out with brown hair. If you want blonde or blue-eyed, write it out explicitly, along with skin tone. Same seed, same face. Swapping nationalities on one seed doesn't give you a cast of different people. Likely fix (untested): vary the seed and describe the face itself. Always state the skin tone. Without skin words, most subjects drifted darker, but South Asians came out lighter. Watch for words with double meanings. "Maltese" added a Maltese dog and white hair. Likely fix (untested): "woman from Malta". Spell out mixed heritage. "Japanese-Nigerian" rendered as fully one parent each time, never mixed. Likely fix (untested): describe the mixed features you want. Specific prompts suppress the priors. The detailed portrait prompt (grid 1) got outfit, backdrop and expression right on 77 of 77. Every deviation came from the sparse prompts. Know the house defaults. Whatever you leave out, Krea fills in the same way: Outfit: "dressed" gives a gray striped tee with black trousers. Background: none specified gives a plain studio wall or white seamless. Hair: in portraits with no hair instruction, it's pulled back on all 77 women (grid 4). "Hair loose and natural" fixed that (grid 1). Scene logic beats "barefoot". It's obeyed in the studio, but every outdoor summer image has sandals. If footwear matters, set the scene to match. Europe = one face Full descriptor (grid 1): the 45 European nationalities are about 2 faces, a "northern" one and a "southern/Balkan" one. Hair and eye colour from the prompt are most of what tells them apart. A face-recognition model confirms the split. Name only (grid 4): every European has brown hair pulled back, and there's not a single blonde, Swedes and Finns included. Nationality survives only as light eyes on about 11 Nordic and Baltic women. Non-European names keep a distinct, recognisable face across prompts, except in the Middle East: Lebanese and Egyptian are the most similar pair of all 77, and with the name only, the 5 MENA women are even more alike than the Europeans. In short: "blonde hair, blue eyes" in the prompt does almost all the work, and "Swedish" barely does anything. "Dressed" = Western casual "Dressed" gives 43 of 45 Europeans the same gray striped tee. Traditional dress and headwear appear only outside Europe: all 3 South Asians, hijabs, headwraps, a Quechua hat. Caveats One seed per subject. Groupings like "Eastern Bloc" were spotted first and tested afterwards. Clothing, age, build and setting were labelled by an AI model (Claude) with no human double-check. NudeNet and a face-recognition model are the independent checks. Krea's training data is undocumented, so any "why" is a guess. Setup Generation (ComfyUI): Diffusion model: Krea 2 Turbo (krea2_turbo_fp8_scaled) Text encoder: Qwen3-VL 4B (qwen3vl_4b_fp8_scaled) VAE: Wan 2.1 (Wan2_1_VAE_fp32) LoRA: none for grids 2–7, including every nude image. krea2filterbypass3 at 1.0 was used in grid 1 only (portraits). Sampling: 8 steps, cfg 1, euler/beta, 1024×1536 Analysis: Nudity check: NudeNet 3.4 Faces: YuNet (detection) + SFace (face recognition), from OpenCV Zoo Labelling clothing, age, build and setting: Claude Seed: one fixed seed for every image. Only the woman's nationality or ethnicity changes. Subjects: 77 women, 45 European and 32 from everywhere else. Prompts: Grids 1–3: full descriptor ("Polish woman, fair skin, dark blonde hair, gray-green eyes"). Grids 4–7: name only ("Polish woman"). Gallery: one grid per prompt, split into Europe and the rest. Each grid's header shows the exact prompt. submitted by /u/matcheal [link] [comments]

Source: r/StableDiffusion | 2026-10-15

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