Krea 2 Tested Across Multiple Precision Formats
y3kdhmbdb2ch2fc6vpm2 · reddit · 2026-07-11
The author tested Krea 2 across various weight/quantization formats, covering BF16, FP8, INT8, GGUF, MXFP8, and NVFP4.
Experimental Setup
- Generated 120 images in total, resulting in 20 comparison groups
- Used the Krea 2 Turbo model from the official ComfyUI repo, alongside a GGUF version
- Hardware: RTX 5070 Ti 16GB + 32GB DDR5 + NVMe
- Settings: 1024×1024 resolution, 8 steps, cfg 1.0, default PyTorch attention
- Full software stack listed: ComfyUI 0.27.0, Python 3.13.12, PyTorch 2.12.0+cu130, CUDA 13.0, etc.
Results & Timings
The author shared generation times for each format. Quick takeaways:
- INT8convrot is the fastest: approx. 10.26s / 6.16s / 5.87s
- NVFP4 is also very fast: approx. 9.47s / 7.78s / 8.30s
- BF16 is relatively slower: approx. 19.44s / 13.13s / 12.60s
- GGUFQ8 is particularly slow on the first run: approx. 22.81s / 24.22s / 14.28s
- MXFP8 and FP8scaled fall somewhere in the middle
Questions for Discussion
- Which format is "best overall"?
- Which one closest matches BF16's quality?
- Is BF16 always the optimal choice?
Overall, this is an engineering-focused comparison of local generation speed vs. quality, highlighting the practical trade-offs between different precision formats.
Related event: Krea 2 Precision Tests Show Small Visual Gaps(3 posts)→
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