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Krea 2 Raw / Base Int8 Row ConvRot HQ

Updated: Jul 18, 2026

base modelkrea 2krea2

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int8 SafeTensor

Krea2_Raw_Base_INT8_Row_ConvRot_HQ.safetensors

int8 precision • 13.16 GB

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Type
Checkpoint Trained
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122

Reviews
Published

Jul 18, 2026

Base Model

Krea 2

Hash
AutoV2
CB81322759
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Krea2.png

This is HQ Int8 Row ConvRot of Krea 2 Raw (Base slow) model.

Made from official BF16 model with SECourses Musubi Trainer Quantization app

You can download and use Musubi Trainer app for both training and quantization from here : https://www.patreon.com/SECourses/posts/secourses-musubi-137551634

To be able to use this model with very best performance please use our Torch 2.13 CUDA 13 ComfyUI installer with ready presets : https://www.patreon.com/SECourses/posts/download-comfyui-installers-and-presets-105023709

I also recommend our SwarmUI installer with ready SwarmUI presets : https://www.patreon.com/posts/download-swarmui-installer-and-presets-114517862

Int8 ConvRot is 96.2% similar to BF16 meanwhile GGUF Q8 is only 90.0% and FP8 Scaled is 82.2% and NVFP4 is 63.7%

  • Moreover, Int8 ConvRot generates the output in 3.05 seconds, making it 1.82× faster than BF16, which takes 5.56 seconds.

  • NVFP4 takes 3.8 seconds and is 1.46× faster than BF16, whereas GGUF Q8 takes 6.06 seconds and is approximately 8.3% slower than BF16.

  • So Int8 ConvRot generated with our Musubi Trainer app at high quality is almost 100% faster and almost same quality as BF16

  • High quality generation takes few hours on RTX 5090

With our ComfyUI backend, Int8 Row ConvRot is able to generate faster than FP8 Scaled literally 100% faster on RTX 3000, 4000 and 5000 series GPUs

Model quantization is taking around 3-4 hours on RTX 5090 since we do training like quantization with prodigy optimizer

Check model screenshots to see and learn more