city96-ComfyUI-GGUF/tools

STEP 1 (Patch files with Unix (LF) line endings

Solution to fix lines endings of the patch files from Windows (CRLF) to Unix (LF)

python fix_lines_ending.py

STEP 2 (Clone llama.cpp version of gguf-py)

Git clone llama.cpp into the current folder. You may also install gguf-py from the llama.cpp repo directly, though the one specified in requirements.txt should also work on recent versions.

git clone https://github.com/ggerganov/llama.cpp
pip install llama.cpp/gguf-py

STEP 3 (Convert to FP16 or BF16)

To convert your initial source model to FP16 (or BF16), run the following command:

python convert.py --src E:\models\unet\flux1-dev.safetensors

STEP 4 (Patch llama.cpp)

• To quantize the model, first apply the provided patch to the llama.cpp repo you've just cloned.

cd llama.cpp
git checkout tags/b3600
git apply ..\lcpp.patch

• To quantize SD3 or AuraFlow models, you should use the patch lcpp_sd3.patch and target tags/b3962 instead.
• There is a WIP PR for other model architectures

cd llama.cpp
git checkout tags/b3962
git apply ..\lcpp_sd3.patch

STEP 5 (Compile llama-quantize binary)

Then, compile the llama-quantize binary. This example uses cmake, on linux you can just use make.

mkdir build
cd build
cmake ..
cmake --build . --config Debug -j10 --target llama-quantize
cd ..
cd ..

STEP 6 (Quantization)

Now you can use the newly build binary to quantize your model to the desired format:

llama.cpp\build\bin\Debug\llama-quantize.exe E:\models\unet\flux1-dev-BF16.gguf E:\models\unet\flux1-dev-Q4_K_S.gguf Q4_K_S

You can extract the patch again with git diff src\llama.cpp > lcpp.patch if you wish to change something and contribute back.

Warning

Do not use the diffusers UNET for flux, it won't work, use the default/reference checkpoint format. This is due to q/k/v being merged into one qkv key. You can convert it by loading it in ComfyUI and saving it using the built-in "ModelSave" node.

Warning

Do not quantize SDXL / SD1 / other Conv2D heavy models. There's little to no benefit with these models. If you do, make sure to extract the UNET model first. This should be obvious, but also don't use the resulting llama-quantize binary with LLMs.