On May 12, 2026, Krea introduced Krea 2 as its first foundation image model built from scratch. Three weeks later the product team shipped a speed-focused path as Krea 2 Turbo (June 3). Then, on June 22–23, 2026, the lab published open weights on Hugging Face, a technical report, and a public “train on Raw / run on Turbo” story that the open-source community immediately picked up.
This is not a recap of marketing slides. It is a sourced map of what actually shipped—and a practical path to try Krea 2 Turbo yourself on SupaImagine without standing up GPUs first (see the Try section below).
TLDR
Krea 2 Turbo is the distilled, few-step checkpoint of Krea’s Krea 2 foundation model family: a 12B-parameter diffusion transformer trained for aesthetic range, released as open weights under a community license, with hosted product/API surfaces that claim ~2-second generation. Use Turbo for fast text-to-image, Raw for LoRA / post-training. Product “Turbo” on Krea’s own UI is related but not identical to every third-party host; always check which endpoint you are calling.
Key Takeaways
- May 12, 2026: Krea announces Krea 2 as a from-scratch foundation image model (official intro).
- June 3, 2026: Product post claims high-quality images in just 2 seconds, with style references, moodboards, and LoRAs supported on Turbo (Introducing Krea 2 Turbo).
- June 22, 2026: Hugging Face model card lists Krea 2 v1.0, 12 billion parameters, text-to-image DiT architecture (
krea/Krea-2-Turbo). - June 23, 2026: Open-source page + technical report: Raw (undistilled base) + Turbo (8-step distilled checkpoint); architecture described as Qwen Image VAE + 12B dense DiT + Qwen3-VL text encoder (open-source hub, tech report).
- Official repo recipe: train LoRAs on Raw, run them on Turbo; Turbo inference 8 steps, CFG off (GitHub
krea-ai/krea-2). - Krea’s technical report places the family among the top 10 on Artificial Analysis text-to-image and 2nd among independent labs (their wording—not our re-benchmark) (tech report).
- You can try Turbo as browser text-to-image on SupaImagine without local setup (Try section below).
What Actually Shipped
1. A foundation model, not another fine-tune story
Krea’s May 12 product post frames Krea 2 as the lab’s first foundation image model built completely from scratch, aimed at aesthetics, style transfer, and creative control—not as a thin wrapper around someone else’s base weights.
The later technical report (June 23) expands that claim into a full research write-up: data curation (including a stated policy of no AI-generated images in pretraining), multi-stage training (pretrain → midtrain → SFT → preference optimization → RL), and architectural choices around a diffusion transformer stack.
2. Product Turbo before open weights
The June 3 “Introducing Krea 2 Turbo” post is short but specific:
- Turbo is a faster version of Krea 2 that tries to keep as much of the original capability as possible while running multiple times faster.
- Headline product claim: high-quality images in just 2 seconds.
- Compatibility claim: style references, moodboards, and LoRAs.
- Explicit trade-off: for strongest style adherence and final polish, Krea still points users at Krea 2 Medium or Krea 2 Large; Turbo is for rapid ideation and early creative loops.
That product framing matters: “Turbo” on Krea’s site is a product SKU as well as a checkpoint name. Third-party hosts may expose only the open Turbo weights, not Krea’s full Medium/Large product stack.
3. Open weights: Raw + Turbo as a paired system
The open-source landing page (schema date published 2026-06-23) ships two checkpoints:
| Checkpoint | Role (Krea’s wording) |
|---|---|
| Krea 2 Raw | Undistilled base — diverse, malleable; for fine-tuning, post-training, LoRA training |
| Krea 2 Turbo | 8-step distilled checkpoint for fast, high-quality text-to-image |
The recommended workflow is repeated across official surfaces: train on Raw, run on Turbo (open-source FAQ, GitHub README).
Hugging Face’s krea/Krea-2-Turbo model card records:
- Release date: June 22, 2026
- Architecture: Diffusion Transformer with 12 billion parameters
- License: Krea 2 Community License
- Recommended Turbo sample settings in the card’s official-code path: 8 steps, guidance_scale 0.0
4. Official inference code and practical settings
The official GitHub repository is the cleanest “how do I actually run this?” source:
- Raw: full sampler with CFG — example flags
--steps 52 --cfg 3.5; trained up to ~1K resolution in the README description - Turbo: few-step — example
--steps 8 --cfg 0.0 --mu 1.15, with generation described as 1K ~ 2K - Distillation narrative: Turbo is an 8-step distilled checkpoint built for fast high-quality text-to-image
- Ecosystem pointers: ComfyUI, fal, SGLang
5. Distillation method (research-level claim)
The technical report’s post-training section describes an optional timestep distillation stage after RL. Krea says they evaluated several methods and adopted Trajectory Distribution Matching (TDM) for flexible multi-step distillation (tech report; method paper context: TDM on arXiv). Independent coverage such as VentureBeat’s open-weights write-up also highlights the ~2-second / open-weights angle for enterprise readers.
Treat TDM as Krea’s published method name, not as a third-party verified latency guarantee on your hardware.
6. Hosted paths (try without local weights)
You do not need to download safetensors to evaluate Turbo:
- SupaImagine (recommended for this guide): browser text-to-image with library + sibling models in one workspace—full steps in the Try section below.
- Krea’s own product and research hosts (e.g. fal’s Turbo endpoint as a documented inference surface) also expose the model family—useful as evidence that Turbo is a real, widely hosted checkpoint, not a one-off demo.
7. Community announcement channel
Krea’s team also posted the open release in r/StableDiffusion, summarizing Raw vs Turbo, natural-language prompting tips (wrap in-image text in quotes), and links to weights, the technical report, and a Hugging Face Space. Useful as a contemporaneous primary post—not as a benchmark.
Family positioning: where Turbo sits
Think of the Krea 2 family as three related ideas that people casually collapse into one name:
- Research / open-weights family — Raw + Turbo checkpoints on Hugging Face
- Krea product family — Turbo for speed; Medium / Large for polish (per product Turbo post)
- Third-party hosted Turbo — open Turbo weights behind someone else’s API or UI
Turbo’s job, in Krea’s own words, is the fast creative loop: prompt tests, mood exploration, early concepts. It is not marketed as the always-best final still when style adherence is the bottleneck.
Architecture (open-source FAQ): Qwen Image VAE, 12B dense DiT, Qwen3-VL text encoder with multi-layer feature aggregation (open-source hub).
Try Krea 2 Turbo on SupaImagine
If you came here to see what Turbo looks like on your prompts, skip the local install:
- Open the Krea 2 Turbo AI image generator.
- Write a natural-language prompt (people, animals, and photographic scenes are a fair first test of this model’s stated strengths).
- Pick an aspect ratio and generate.
- Keep winners in your library; swap to a sibling model in the same workspace when you want a second opinion on the same prompt.
On SupaImagine, Turbo is text-to-image only (prompt + aspect ratio)—not an upload-to-edit tool. That matches the open Turbo materials’ emphasis on prompt→image, and keeps the first evaluation loop simple.
Krea 2 Turbo vs Krea 2 Raw: What the Signal Says
| Dimension | Turbo | Raw |
|---|---|---|
| Primary use | Fast inference | Training / research base |
| Steps (official examples) | 8, CFG off | Higher steps with CFG |
| Resolution guidance | 1K–2K in README | ~1K emphasized for base |
| Distillation | Yes (few-step student) | No (undistilled) |
| LoRA workflow | Run LoRAs trained on Raw | Train LoRAs here |
If your goal is “generate a polished still from a prompt in a product UI,” start with Turbo. If your goal is “own a style with LoRAs or post-train,” start with Raw, then deploy Turbo for inference—exactly the pairing Krea documents on GitHub and the open-source page.
Versus non-Krea models (Flux-class, Google’s image stack, OpenAI image models, etc.): public head-to-head numbers depend on the host and the date. Prefer live leaderboards and your own prompt set over any single blog’s ranking claim. Krea’s own Artificial Analysis statement is best read as self-reported standing in their technical report, not as a perpetual scoreboard.
What We Know vs. What We Don’t
What we know (sourced):
- Krea 2 is positioned as a from-scratch foundation model (May 12 intro).
- Product Turbo claims ~2-second generations and workflow compatibility with style refs / moodboards / LoRAs (June 3 post).
- Open Turbo is an 8-step distilled checkpoint; Raw is the malleable base (open-source hub).
- Model card: 12B DiT, release 2026-06-22 (HF Turbo card).
- Official recipe: train Raw → run Turbo (GitHub).
- Hosted Turbo inference surfaces exist beyond Krea’s app (e.g. fal’s Turbo endpoint as a documented host).
- License is not pure public-domain free-for-all—community + enterprise paths (licensing).
What we don’t know (or cannot honestly claim from public docs alone):
- Exact wall-clock latency on your GPU / region / batch size (product “2 seconds” is Krea’s claim, not a universal SLA).
- Whether every third-party “Krea 2 Turbo” endpoint is bit-identical to HF
krea/Krea-2-Turbo(quantization, safety filters, and samplers differ). - Independent, locked-prompt wins vs every competing 2026 frontier model on your production distribution.
- Full internal training compute budget and dataset sizes beyond what the technical report discloses.
Why This Matters for Builders
Three practical consequences:
- Open aesthetic models moved again. A lab with a real product surface also dropped open weights and a long technical report—rare combination, and it compresses the gap between “SaaS only” and “self-host + fine-tune.”
- The Raw/Turbo split is an ops pattern. Treating train and serve as one checkpoint is the old Flux-era habit; Krea is standardizing a two-checkpoint pipeline. Design your storage and CI around that.
- Name collision risk is real. “Krea 2,” “Krea 2 Turbo,” “Krea 2 Medium,” and “Krea 2 Large” are not interchangeable SKUs. When you write an internal RFC or a customer FAQ, pin the endpoint ID (HF repo, fal model id, or product route).
For product teams, the interesting question is simpler: does Turbo’s look and iteration speed beat your current default on the prompts that matter? That is a one-afternoon A/B on SupaImagine—not a paper-reading project.
How to Evaluate It Yourself
Primary path (browser, no GPU setup): follow the Try section above on SupaImagine’s Krea 2 Turbo AI image generator.
- Fix a short prompt suite you already care about (people, animals, product packs, brand style).
- Run the same prompts on Turbo, then on one sibling model in the same workspace for a second opinion.
- Score only what you can see: prompt adherence, skin/fur realism, artifact rate, style consistency across a short series.
- Time the loop end-to-end (queue + result + save), not just marketing latency claims.
- Keep winners in your library and decide from your eyes, not from a leaderboard screenshot.
Optional local path (research / self-host only): follow the GitHub setup, download Turbo weights from Hugging Face, run the 8-step recipe, and re-read the community license before commercial use.
What to Watch Next
- License enforcement and enterprise terms as more studios adopt open weights in production (licensing hub).
- LoRA ecosystem depth on Turbo after community training on Raw (HF adapter graphs grow quickly after open releases).
- Leaderboard movement on Artificial Analysis and similar boards—treat any single snapshot as perishable.
- Product vs open-weight feature parity (style reference / moodboard systems described in the tech report may not all ship on every third-party Turbo endpoint).
- Sibling model pressure from other 2026 open and closed image stacks—re-run your prompt suite monthly, not once.
FAQ
What is Krea 2 Turbo?
It is the speed-optimized, few-step checkpoint of Krea’s Krea 2 foundation image model family—described by Krea as an 8-step distilled text-to-image model for fast, high-quality generation (open-source hub).
When was Krea 2 Turbo released?
Product Turbo was introduced on June 3, 2026 (product post). Open weights and the technical report followed around June 22–23, 2026 (HF card, tech report).
How is Turbo different from Krea 2 Raw?
Raw is the undistilled base for training and research. Turbo is the distilled inference checkpoint. Official guidance: train LoRAs on Raw, run them on Turbo (GitHub FAQ).
How fast is Krea 2 Turbo?
Krea’s product marketing claims about 2 seconds per generation on their experience (Introducing Krea 2 Turbo). Local and third-party speeds vary with hardware, resolution, and queueing.
How many parameters does Krea 2 use?
The Hugging Face model card lists a 12-billion-parameter diffusion transformer (Turbo model card).
Is Krea 2 Turbo open source?
Open weights and inference code are public under the Krea 2 Community License, with community and enterprise licensing paths (open-source page, licensing). Read the license before commercial deployment—do not assume “open weights” means unrestricted enterprise use.
Does Turbo support image editing or image-to-image?
Official open Turbo materials emphasize text-to-image. Product surfaces may add more controls; third-party “edit” demos are not automatically the base model. On SupaImagine’s hosted Turbo path, generation is prompt → image (text-to-image), not an upload-to-edit tool.
How do I try Krea 2 Turbo on SupaImagine?
Follow the Try section above: open the generator, sign in if needed, write a prompt, pick an aspect ratio, and generate. New accounts get starter credits to evaluate models; paid plans top up when you want more volume.
Where can I try Krea 2 Turbo without setting up GPUs?
Start on SupaImagine (Try section above). That is this guide’s evaluation path so you can A/B models in one library. Vendor product and research hosts also exist; we do not route the primary CTA off-site.
Is Krea 2 the best open image model?
Krea’s technical report claims a strong Artificial Analysis standing (top 10 overall / 2nd among independent labs in their text). Rankings move. Run your own prompts on SupaImagine and decide from the images.
What should I use Turbo for first?
Rapid ideation: portrait and animal photoreal tests, mood exploration, and short A/Bs against your current default—exactly the “faster creative loop” Krea describes (product post). Run that loop in-browser on SupaImagine first.
Try it next: open Krea 2 Turbo on SupaImagine, run three of your real prompts, and keep the takes that look less like “default AI” and more like the photographic direction this model was built for.
About Mara Ellison
Mara is an AI Image Model Analyst. She tracks image-model releases and capability claims so builders don’t have to, and writes the numbers-first breakdowns on SupaImagine.
