Krea 2 Turbo community releases GGUF ports: RTX 3090 tests report 1.9x int8 speedups

• Community ports turned Krea 2 Turbo into a much more practical local model within a day: molbal's GGUF release targeted sub-8 GB cards, while TheLocalLab's ComfyUI post packaged FP8 and GGUF workflows around the same idea.
• The clearest speed datapoint came from a 3090 int8 benchmark, which reported 1.27 it/s for int8 versus 0.65 it/s for fp8, about 1.9x faster with similar same-seed outputs.
• Style range is the reason people are excited: hellorob's watercolor test said Krea 2 hit a full-page 1900s watercolor look other models missed, while fragilesleep's 100-style gallery pushed one scene across the official moodboards.
• Prompt adherence looks strong but not perfect. a costume-design comparison said Krea beat Z Base on apparel language, while kaigani's hands-on take said it can slip too easily into known IP.

You can pull the GGUF conversion, grab the ComfyUI GGUF loader, compare the 3090 int8 side-by-side, and browse Krea's own moodboard gallery. One of the more useful community add-ons was a reference-image text encoder, and one of the weirder early tells was hellorob's claim that Krea finally nailed a public-domain watercolor dataset Midjourney missed.

Krea 2's first useful community move was not another gallery post. It was shrinking the thing.

According to molbal's Reddit post, the BF16 source weights are 26.6 GB and FP8 is about half that, while the Q4_0 GGUF lands around 7.6 GB and is aimed at 8 GB GPUs. TheLocalLab's companion workflow post framed the same pitch more directly: 8 GB should work.

That matters because the open-weights release immediately became a packaging story. The community now has three practical local paths:

• BF16, for people with plenty of VRAM, per molbal's size breakdown
• FP8, for a lighter official-ish path, per TheLocalLab's FP8 workflow post
• GGUF, for smaller cards and CPU-offload setups, per molbal's GGUF release

The fastest early optimization came from a community quant, not the base drop.

WinResponsible9977's follow-up held sampler, seed, prompt, and resolution constant, then swapped only precision. The result was 0.65 it/s for fp8 and 1.27 it/s for int8 ConvRot on a 3090, which cut image time from 14.8 seconds to 7.7 seconds.

The same post also makes a useful correction to the usual low-bit story: on this setup, int8's win was speed, not memory. Peak VRAM was 18.8 GB for fp8 and 19.2 GB for int8, so the gain came from Ampere's INT8 tensor cores rather than a smaller footprint.

The reason creators kept posting Krea tests is simple: it appears unusually good at jumping styles without falling apart.

hellorob said Krea reproduced faded watercolor gradients and bleeding edges from an early-1900s illustration book, with better prompt adherence than the other models they tried. fragilesleep took a different angle, generating thousands of children's-book images from Krea's official moodboards and publishing a 100-plus-style gallery built from one base scene.

Other users kept finding the same pattern in narrower tests. a comic-cover experiment said the model knows many characters by name, and a costume-design comparison found stronger apparel-language adherence than Z Base, even though it missed requested background figures.

The best community tips were already getting pretty specific, which is usually a good sign a model has escaped pure novelty mode.

According to Different_Fix_2217's tip list, the most common stack was the raw model plus the turbo LoRA at 0.6 weight, around 12 steps, and a different VAE than the default. The same post also linked a reference-image text encoder, suggested more explicit naming for characters and series, and said Krea knows many artists by name.

Two prompt habits came up repeatedly in that thread:

• Specify the exact franchise or year, because generic names can drift to the wrong version, per the usage tips thread
• Use style phrases directly, such as artist names or stronger conditioning, per the same tips and fragilesleep's moodboard workflow

The last interesting reveal is that people were already treating Krea as more than a txt2img style toy.

lazyspock's img2img test reported workable denoise settings from 0.4 to 0.75 on a 12 GB 4070 using a GGUF build, while the usage tips thread pointed to a reference-image encoder for feeding visual inputs into the text side. That combination, img2img plus reference conditioning, is where these early local ports start looking less like a benchmark hobby and more like a real style workflow.