[CUDA] stable diffusion benchmark allows IO binding for optimum

[tianleiwu]

Description

Update stable diffusion benchmark:
(1) allow IO binding for optimum.
(2) do not use num_images_per_prompt across all engines for fair comparison.

Example to run benchmark of optimum on stable diffusion 1.5:

git clone https://github.com/tianleiwu/optimum
cd optimum
git checkout tlwu/diffusers-io-binding
pip install -e .

pip install -U onnxruntime-gpu
git clone https://github.com/microsoft/onnxruntime
cd onnxruntime/onnxruntime/python/tools/transformers/models/stable_diffusion
git checkout tlwu/benchmark_sd_optimum_io_binding
pip install -r requirements/cuda12/requirements.txt

optimum-cli export onnx --model runwayml/stable-diffusion-v1-5  --task text-to-image ./sd_onnx_fp32

python optimize_pipeline.py -i ./sd_onnx_fp32 -o ./sd_onnx_fp16 --float16
python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16
python benchmark.py -e optimum -r cuda -v 1.5 -p ./sd_onnx_fp16 --use_io_binding

Example output in H100_80GB_HBM3: 572 ms with IO Binding; 588 ms without IO Binding; IO binding gains 16ms, or 2.7%,

Motivation and Context

Optimum is working on enabling I/O binding: huggingface/optimum#2056. This could help testing the impact of I/O binding on the performance of the stable diffusion.

[github-actions]

You can commit the suggested changes from lintrunner.

[kunal-vaishnavi]

We should upgrade the Optimum version here once those changes are merged.

onnxruntime/onnxruntime/python/tools/transformers/models/stable_diffusion/requirements/requirements.txt

Line 15 in 3cccde4

optimum==1.20.0