Krea 2 Turbo - GGUF Quantizations
This repository contains GGUF format quantizations of Krea 2 Turbo, a state-of-the-art 12-billion parameter Diffusion Transformer (DiT) text-to-image model developed by Krea.ai.
GGUF weights allow for efficient, low-memory inference across consumer hardware, CPUs, and Apple Silicon.
📂 Available Files & Sizes
| File Name | Quantization Type | Size | Description |
|---|---|---|---|
Krea-2-Turbo-Q3_K_M.gguf | Q3_K_M | ~6.01 GB | Highly compressed, lowest memory usage. |
Krea-2-Turbo-Q3_K_S.gguf | Q3_K_S | ~6.01 GB | Small variation of 3-bit quantization. |
Krea-2-Turbo-Q4_K_M.gguf | Q4_K_M | ~7.49 GB | Balanced medium quantization, recommended baseline. |
Krea-2-Turbo-Q4_K_S.gguf | Q4_K_S | ~7.49 GB | Small variation of 4-bit quantization. |
Krea-2-Turbo-Q5_K_M.gguf | Q5_K_M | ~8.87 GB | High quality, great balance of speed and retention. |
Krea-2-Turbo-Q5_K_S.gguf | Q5_K_S | ~8.87 GB | Small variation of 5-bit quantization. |
Krea-2-Turbo-Q6_K.gguf | Q6_K | ~10.6 GB | Near-lossless representation of original weights. |
Krea-2-Turbo-Q8_0.gguf | Q8_0 | ~13.7 GB | Extremely high precision, closest to full 16-bit. |
🚀 Inference Guide
Using llama.cpp / stable-diffusion.cpp
You can run these GGUF text-to-image files directly using compatible implementations such as stable-diffusion.cpp.
# Example using stable-diffusion.cpp for a 1024x1024 image generation
./sd -m ./models/Krea-2-Turbo-Q4_K_M.gguf \
-p "a red fox sitting in fresh snow, golden hour, photorealistic" \
--steps 8 --cfg 0.0 -w 1024 -h 1024 -o output.png