Krea 2
A solid pick for self-hosting and full data control.
Overview
Krea 2 is Krea AI's first in-house foundation model, announced May 12, 2026, then released as open weights in late June. It's a 12.9-billion-parameter Diffusion Transformer trained from scratch, with a deliberate stance: aesthetic diversity over the "default AI look" most generators converge toward. Notably, the training corpus completely excludes AI-generated images, filtered via in-house classifiers — the team found that even a small share of synthetic data caps model quality. The open-source release follows a "train on Raw, generate with Turbo" pattern: Krea 2 Raw is the undistilled checkpoint, ideal as a LoRA training base to capture a style or product; Krea 2 Turbo is the inference checkpoint, generating 2K images in about 2 seconds on consumer hardware. For a brand, that means training an adapter on your own assets and serving it locally, with no dependency on a closed API. Style control works through moodboards and reference images (palette, texture, lighting, composition) rather than endless prompts. Access via the Krea platform (free tier, then $9/month Basic, $35 Pro, $70 Max), the Krea API, or self-hosted via the Hugging Face weights under a community license (commercial use allowed for individuals and small teams, separate enterprise licensing).
Skill profile
Not disclosed
Strengths
- Has a free tier
- Open-source and self-hostable
Who is it for
- you want to control cost or self-host
- your data must stay in the EU
Ideal use cases
- Art direction and style exploration via moodboards
- Consistent brand visuals (LoRA fine-tuning on your assets)
- High-throughput generation (2K in ~2 seconds with Turbo)
Access & availability
Key specifications
What it costs per month
Estimate from sourced API prices and a hardware tier for self-hosting. Assumptions are editable.
Assumptions: 36-month amortisation, 0.25 €/kWh, 22 days/month, 1 $ = 0.92 €. Excludes labour, software licences, redundancy.
nAIvigate Trust Index
What the vendor offers and documents for professional use in Europe. An undocumented criterion counts as zero and stays visible: the score rises when the vendor publishes, not when we assume.
19/19 criteria documented · 100 % · 12 of them set by rule
See the criteria▾
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[](https://naivigate.com/en/model/krea-2)Data & compliance
What we know, line by line, with source and date. These lines feed the Data and Enterprise families of the Trust Index above. GDPR compliance depends on your processing, not on the model alone.
| EU data residency | Yes |
| Data processing agreement (DPA) | N/A· self-hosted: data never leaves |
| Transfer outside the EU | No |
| Training on your data | No |
| Configurable retention | N/A· self-hosted: data never leaves |
| Zero Data Retention | N/A· self-hosted: data never leaves |
| Self-hostable | Yes |
| Declared hosting | self |
Quality of this card’s data
This score measures the quality of the data on this card: price, context, Elo, licence, documented, sourced and fresh. It measures neither the model nor its compliance: for that, see the Trust Index above.
3/8 documented · 0/8 primary sources
| API pricingopen weights, no API pricing | missing | |
| Context window | nAIvigate assessment | |
| Arena Elo | missing | |
| Benchmarks (MMLU, GPQA, SWE-bench…) | missing | |
| Licence | nAIvigate assessment | |
| Data & complianceself-hosted: derived | derived | |
| Adoption (Hugging Face) | missing | |
| Hardware requirements (quantizations)computed from model size | missing |
Advanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |