Stable Diffusion on AMD GPUs on Windows using DirectML

Stable_Diffusion.md

Stable Diffusion for AMD GPUs on Windows using DirectML

UPDATE: A faster (20x) approach for running Stable Diffusion using MLIR/Vulkan/IREE is available on Windows:

https://github.com/nod-ai/SHARK/blob/main/shark/examples/shark_inference/stable_diffusion/stable_diffusion_amd.md

Install 🤗 diffusers

conda create --name sd39 python=3.9 -y
conda activate sd39
pip install diffusers==0.3.0
pip install transformers
pip install onnxruntime
pip install onnx

Install DirectML latest release

You can download the nightly onnxruntime-directml release from the link below

• https://aiinfra.visualstudio.com/PublicPackages/_artifacts/feed/ORT-Nightly/PyPI/ort-nightly-directml/overview/1.13.0.dev20220908001

Run python --version to find out, which whl file to download.

• If you are on Python3.7, download the file that ends with **-cp37-cp37m-win_amd64.whl.
• If you are on Python3.8, download the file that ends with **-cp38-cp38m-win_amd64.whl
• and likewise

pip install ort_nightly_directml-1.13.0.dev20220908001-cp39-cp39-win_amd64.whl --force-reinstall

Convert Stable Diffusion model to ONNX format

This apporach is faster than downloading the onnx models files.

• Download diffusers/scripts/convert_stable_diffusion_checkpoint_to_onnx.py to your working directory. You can try the command below to download the script.

wget https://raw.githubusercontent.com/huggingface/diffusers/main/scripts/convert_stable_diffusion_checkpoint_to_onnx.py

• Run huggingface-cli.exe login and provide huggingface access token.
• Convert the model using the command below. Models are stored in stable_diffusion_onnx folder.

python convert_stable_diffusion_checkpoint_to_onnx.py --model_path="CompVis/stable-diffusion-v1-4" --output_path="./stable_diffusion_onnx"

Run Stable Diffusion on AMD GPUs

Here is an example python code for stable diffusion pipeline using huggingface diffusers.

from diffusers import StableDiffusionOnnxPipeline
pipe = StableDiffusionOnnxPipeline.from_pretrained("./stable_diffusion_onnx", provider="DmlExecutionProvider")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0] 
image.save("astronaut_rides_horse.png")

hi, the exe doesnt work for me following your little guide (using the MLIR driver on 6900XT), im getting errors:

ill try the manual guide

For me the Advanced Installation worked

Time generation ?