Diffusion Model Quantization using Quark#

This topic provides examples of FP8 weight-activation quantization and INT8 weight-only quantization using Quark, along with instructions for exporting the quantized models. Supported models include SDXL, SDXL-Turbo, SD1.5, SDXL-Controlnet, and SD1.5-Controlnet. To incorporate additional diffusion models, adjust the pipeline when loading the model, as demonstrated in quantize_diffusers.py.

Third-party Dependencies#

The example relies on torchvision. Users need to install the version of torchvision that is compatible with their specific version of PyTorch.

export DIFFUSERS_ROOT=$PWD
git clone https://github.com/mlcommons/inference.git
cd inference
git checkout 87ba8cb8a6a4f6525f26255fa513d902b17ab060
cd ./text_to_image/tools/
sh ./download-coco-2014.sh --num-workers 5
sh ./download-coco-2014-calibration.sh -n 5
cd ${DIFFUSERS_ROOT}
export PYTHONPATH="${DIFFUSERS_ROOT}/inference/text_to_image/:$PYTHONPATH"

Dataset Files#

The calibration dataset file will be downloaded to ${DIFFUSERS_ROOT}/inference/text_to_image/coco2014/calibration/captions.tsv.
The test dataset file will be downloaded to ${DIFFUSERS_ROOT}/inference/text_to_image/coco2014/captions/captions_source.tsv.

Quantization & Export Scripts#

You can run the following Python scripts in the examples/torch/diffusers path.

Run Diffusion Model Without Quantization#

Run SDXL:

python quantize_diffusers.py --model_id stabilityai/stable-diffusion-xl-base-1.0 --float

Run SD1.5 Controlnet:

python quantize_diffusers.py --model_id runwayml/stable-diffusion-v1-5 --controlnet_id lllyasviel/control_v11p_sd15_canny --input_image {your input image for guidence in controlnet} --float

Calibration and Export SafeTensor#

Run Calibration and Export:

python quantize_diffusers.py --model_id {your diffusion model} --controlnet_id {your controlnet if used} --input_image {guidence image if controlnet is used} --quant_scheme {'w_fp8_a_fp8', 'w_int8_per_tensor_sym'} --calib_prompts {your calibration dataset file path} --export --saved_path {output path for your quantized model} --calib_size {number of calibration prompts, default 500}

Load SafeTensor and Test#

Load and Test:

python quantize_diffusers.py --model_id {your diffusion model} --controlnet_id {your controlnet if used} --input_image {guidence image if controlnet is used} --quant_scheme {'w_fp8_a_fp8', 'w_int8_per_tensor_sym'}  --test_prompts {your test dataset file path} --load --saved_path {the path for your quantized model} --test --test_size {number of test prompts, default 5000}

Load SafeTensor and Run with a Prompt#

Load and Run:

python quantize_diffusers.py --model_id {your diffusion model} --controlnet_id {your controlnet if used} --input_image {guidence image if controlnet is used} --quant_scheme {'w_fp8_a_fp8', 'w_int8_per_tensor_sym'} --load --saved_path {the path for your quantized model} --prompt "A city at night with people walking around."

Benchmark#

MI210 GPU, diffusers==0.21.2

Model Name	Quant Config	CLIP score	FID score
SDXL base 1.0	FP16	31.74845	23.56758
	W-FP8-A-FP8	31.83954	23.61475
	W-INT8	31.77445	23.34854
SD 1.5	FP16	29.53386	41.85444
	W-FP8-A-FP8	29.44639	45.26559
	W-INT8	29.53238	42.34745

Diffusion Model Quantization using Quark

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