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Krea 2 Anime UWU turbo

Updated: Sep 28, 2026

base model

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int8 SafeTensor

Krea 2 Anime Yuni UWU.safetensors

8-bit integer, smaller file • 12.57 GB

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Type
Checkpoint Trained
Stats

245

Reviews
Published

Sep 27, 2026

Base Model

Krea 2

Training
Steps: 9,000
Epochs: 45
Hash
AutoV2
73956F1343
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Followers - 216

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Krea2_turbo_00103_.png

krea 2 anime


The model was trained on a high-quality dataset of over 3,000 images; approximately 350 of these consist of my own drawings and AI-generated images that I edited using drawing software.


Qween VL annotation + manual corrections


I consider this model one of the strongest and most powerful I have ever released.

The download fee is nominal. The hardware required to train the Krea 2 model on a large dataset is expensive; even though I can afford to train it locally—my PC costs around $5,000–$6,000 (and I’m currently upgrading it)—it still places a massive strain on the system. I need the funds to enable generation using this model on Civitai and possibly to purchase some cosmetics on the platform. I have previously released a large number of models completely for free, and I hope you understand my decision in this instance.


I was really impressed by this model's performance—specifically its speed and flexibility. I had originally planned a large-scale fine-tuning project for Z-Image, but I changed my mind and decided to focus on Krea 2 instead. At the moment, I have a "raw" dataset of 350,000 images (about 20,000 have been processed so far; manual filtering weeds out roughly 60–70% of the material, leaving a final set of around 100,000 images), as well as a high-standard, carefully curated regularization set. Additionally, I plan to compile a supplementary set of about 20,000 images featuring game characters (primarily from HoYoverse titles) if I determine they are underrepresented in the main dataset, given that the base Krea 2 model doesn't recognize them very well—and is unfamiliar with many of them entirely.


int 8 model

euller\simple 8 step

Image resolution 1.0–1.3–1.5 MP