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")

@cpietsch It doesn't work for Linux very well. The Linux-exclusive ROCM only properly support their workstation GPUs and support for consumer GPUs is lagging. You'd have to follow weird workarounds to get them working on the recent cards. And for slightly older cards like GFX803, it turns out to be impossible.

Oh sorry about that. It worked out of the box for my Radeon VII and I thought that that this was the same for the rest.

Hello everyone. As Christian mentioned, we have added a new pipeline for AMD GPUs using MLIR/IREE. This approach significantly boosts the performance of running Stable Diffusion in Windows and avoids the current ONNX/DirectML approach.

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

Please reach out to us on the discord link on the instructions page or create GitHub issues if something does not work for you.

Thanks!

@averad, Could you please give it a try and update your instructions too? You can reach us on the discord channel if you have any questions, Thanks!

@harishanand95 I will give it a try and update the Instructions.

@harishanand95 I wasn't able to test the process as IREE doesn't have support for RX 500 series cards - GCNv3

I've suggested adding def VK_TTA_RGCNv3 : I32EnumAttrCase<"AMD_RGCNv3", 103, "rgcn3">; and am working on compiling IREE with my suggested changes for testing.

I am getting 3.85 it/s on my 6900xt on SHARK (vulkan), that is 13 seconds for 50 iterations

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 ?