Instructions to use rockerBOO/krea2-raw-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use rockerBOO/krea2-raw-nvfp4 with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Krea 2 Raw β NVFP4 quant for ComfyUI
Quantized version of Krea 2 Raw for ComfyUI, using NVFP4 for the main transformer blocks with ComfyUI-compatible metadata.
Files
| File | Size | Status |
|---|---|---|
krea2_raw_nvfp4.safetensors |
8.3 GB | Uploaded, tested in ComfyUI (text-to-image) |
Requirements
Blackwell GPU (SM >= 10.0/12.0) required for inference β RTX 50-series, B100/B200.
BF16 vs NVFP4 comparison
Same seed/workflow/LoRA, only the base model swapped. Generated in ComfyUI.
License
Krea 2 Community License, inherited from krea/Krea-2-Raw.
Quantization method
Quantized with silveroxides/convert_to_quant
(ctq):
ctq -i krea2_raw_bf16.safetensors -o krea2_raw_nvfp4.safetensors \
--nvfp4 --comfy_quant --save-quant-metadata --low-memory --krea2
--krea2 keeps the text-fusion branch (txtfusion.*) and a handful of small first/last layers
(first, last.linear, tmlp.*, txtmlp.*, tproj.1) at full precision; all 28 main transformer
blocks (attention + MLP, 224 of 264 weight tensors, ~95% of weight parameters) are quantized to
NVFP4 with learned-rounding optimization (SVD-based, the tool's default β not RTN/--simple).
An earlier build of this file used --simple (RTN, no learned rounding); this one replaces it.
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Model tree for rockerBOO/krea2-raw-nvfp4
Base model
krea/Krea-2-Raw


