Krea 2 vs Qwen-Image 2.1: which keeps the same face

Two image models dominate this week's downloads for people who build a recurring character, and they split the job in half. Krea 2 makes the better-looking photo. Qwen-Image 2.1 is built to take your reference pictures and keep her face while it edits. The catch sits in the licenses: one of them lets a small creator business sell what it makes, and the other, read literally, does not.

This comparison puts the two side by side on what matters for a feed: portrait realism, identity, hardware, speed, ComfyUI support and the terms. Where nobody has run a proper head-to-head, it says so instead of inventing a score. Every spec below comes from the model cards, the licenses or the ComfyUI announcement, and the one hands-on test we found is described with its limits.

The verdict first

• Choose Krea 2 Turbo if the character earns money and your company makes under USD 1 million a year: it is the more photographic of the two in the test we found, it needs 8 steps, and its license has a clear commercial path.
• Choose Qwen-Image 2.1 if you are prototyping, researching or building a look from reference photos and edits, and you can live with the license or get a commercial one from Alibaba. Its 10-reference editing is the strongest identity tool in this pair.
• Use both: Qwen to explore a face from references, a trained LoRA on Krea 2 for the daily feed once the face is settled. A LoRA belongs to the base model it was trained on, so pick the one you can legally ship before you train.

Side by side

Portrait realism: Krea 2 wins the one test we found

There is no published head-to-head built for AI influencers, and none ran identical settings across many generations of one face. The closest is a community test on one RTX 3090: five prompts with two seeds each at 1024 by 1024, Krea 2 Turbo at 8 steps against Qwen-Image 2.1 at 25, prompt enhancers off. Its author calls it "a sanity check, not a benchmark", and the ratings are subjective.

Within those limits the result is consistent: Krea scored three stars for portrait realism in both its fp8 and int8 builds, Qwen two, and two and a half with a community fix LoRA. The write-up says Krea "looks like an actual photo" where Qwen's subjects "skew younger and slightly plastic", and that base Qwen output looks flatter and more washed out. For a feed that lives on believable skin, that matters more than any spec.

The speed row has a hardware footnote: the test card is an Ampere GPU without FP8 tensor cores, which is why int8 was 1.78 times faster than fp8 for Krea there. On a newer card the fp8 build may close that gap. Qwen's own Turbo (8 steps, CFG 1) arrived on October 9, after this test, so its portraits and timings are not covered here.

Keeping the same face

This is where the models differ in kind. Qwen-Image 2.1 takes up to 10 reference images in the same model that generates, and its GitHub page says it can "preserve identity for people and products", with showcase examples and no measurements. Krea 2's model card describes a text-to-image model only, and the ComfyUI post splits it into a Raw base meant for fine-tuning and LoRA training and the distilled Turbo.

So the honest comparison is two routes to one face. With Qwen you feed her reference set into every edit. With Krea you either train a LoRA on Raw or Turbo (a card you do not own can be rented by the hour) or follow a community workflow that holds the face by prompt and a locked base character. One tutorial built on a creator's post in r/StableDiffusion describes that workflow, and its own summary admits it is a single creator's method that nobody independently confirmed.

Musetta studioDemo data

The licenses decide who can use which

Search summaries called Qwen-Image 2.1 Apache 2.0 within days of its release. It is not. The Qwen Research License, dated September 20, 2026, defines non-commercial as "for research or evaluation purposes only" and says you "shall not use the Materials for any commercial purpose without obtaining a separate commercial license". Its predecessors were Apache 2.0, which is why so many people assumed the same.

huggingface.coOpen the page

What the license does not say is whether the images you generate are covered by the same limit. The text grants no commercial rights to outputs, and commenters on the model page report that Qwen clarified on X that users keep their outputs, a statement that is not in the license file and that we could not read directly. That ambiguity is the reason to ask Alibaba in writing, at the contact address in the license, before a paid account depends on it. This is not legal advice.

The Krea 2 Community License, version 1 dated June 22, 2026, is clearer and stricter in other places. Commercial use of the model, derivatives or outputs is allowed only if your company, with affiliates, has less than USD 1,000,000 in total annual revenue over the trailing twelve months; above that you need an Enterprise license. You own your outputs and you are responsible for them. You must also run reasonable content filters in any deployment and disclose AI generation where a law or platform requires it.

Running them in ComfyUI

Both run in core ComfyUI, no custom node pack. Krea 2 needs version 0.26.0 or later and uses a Qwen3-VL text encoder with the Qwen Image VAE. Qwen-Image 2.1 arrived in ComfyUI 0.37.0 per our October news roundup. If a template opens with red nodes, update first.

On memory, the facts are thinner than the guides suggest. Krea's fp8 checkpoint is about 12.2 GB on disk in the RTX 3090 test, and several 16 GB card recipes report it fits because the text encoder is freed before sampling. For 12 GB the sources disagree: one recipe says the fp8 transformer alone leaves no room and recommends a GGUF quant instead. Qwen-Image 2.1's model card gives no VRAM figure at all and only suggests CPU offloading. Treat every number as one reporter's machine.

Which one should you pick

Our recommendation is the boring one: ship on the model whose license you can read in five minutes, and use the other to explore. If you want a tool that keeps her references in one place and lets you swap the model when a license or a result changes, that is what the Musetta studio is for, and it runs on your own ComfyUI.