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Krea 2 Turbo Int8 Row ConvRot HQ

Updated: Jul 18, 2026

base modelkrea 2krea2

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int8

Krea2_Turbo_INT8_Row_ConvRot_HQ.safetensors

int8 precision • 13.16 GB

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Type
Checkpoint Trained
Stats

142

Reviews
Published

Jul 18, 2026

Base Model

Krea 2

Hash
AutoV2
ED963F042B
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Krea2.png

This is HQ Int8 Row ConvRot of Krea 2 Turbo (fast 8 steps and High Quality) 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