stable_diffusion.openvino
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bes-dev
ONNX models
First of all, thank you for this, tried it out on CPU and there was some speed boost compared to pytroch.
However i am wondering if you would be willing to share the possible onnx-versions of the models? I don't have a lot of experience with openvino, but to my understanding converting to onnx is usually a step in converting to the openvino format? Having the onnx ones may also, with some luck help amd users to run it eventually.
@simona198710 thanks for you feedback. Actually, openvino works on amd hardware pretty well. So, you can use our project for AMD hardware too. What about .onnx, I have onnx ir for the stable diffusion, but currently, I have a little problems with hosting of checkpoints (I used gdrive to store checkpoints, but google bans they due too many downloads). I'll share .onnx soon when I resolve issues related to hosting.
@bes-dev Sorry, by amd hardware i was actually referring to amd gpus, Since they cannot use cuda. The onnxruntime have support for DirectML which enables amd gpus to run the models, provided the opset version is 15 or lower. If hosting is a problem, maybe just the script for converting from pytorch to onnx (and ir) may be enough.
@simona198710 sounds good for me 👍 I used opset=14 for all models. Would you like to contribute AMD InferenceEngine to our project? We can refactor our code to support different backends.
@bes-dev Sure, if i am able to get them to run on a amd-gpu using onnx i would certainly be willing to open a pull request for it. Regarding hosting, would not putting the models https://huggingface.co/ work?
@simona198710 I'll try to share onnx today later. I'll let you know when it will be ready.
Thanks for nice idea about huggingface hub)
@simona198710 you can found .onnx here: https://huggingface.co/bes-dev/stable-diffusion-v1-4-onnx
@bes-dev Thank you, the models loads when using onnx with cpu.
Unfortunately it gives:
RUNTIME_EXCEPTION : Exception during initialization: ... The parameter is incorrect.
When loading the vae and unet models on amd using DirectML.
Hoping the cause is the same as: https://github.com/microsoft/onnxruntime/issues/12677
The text-encoder works fine to interface using DirectML on the gpu however.
Do you think it would be possible to share the steps or scripts you used to convert the models from pytorch to onnx? It would probably be helpful for people using it with openvino as well. Especially since there are pruned variants of the original models available, that people may want to convert for cpu usage.
@simona198710 Hmm, it looks like a problem on the DirectML side.
Our code to convert models from pytorch to onnx is really ugly and tricky. But we have plans to prepare it for publication soon.
Hi @bes-dev,
Thank you so much for sharing the onnx model.
It will be really great if you can share the onnx conversion script for the unet model.
Did you make any changes while converting? I am getting opset unsupported error, while conversion.
@kamalkraj This script works for converting the unet. https://github.com/harishanand95/diffusers/blob/main/examples/inference/save_onnx.py
@simona198710 Does that really work for you? I'm getting RuntimeError: Encountering a dict at the output of the tracer might cause the trace to be incorrect with nothing changed in the script.
@epi-morphism You probably did not include his edit of transformers that earlier also was part of that repo. Seems like its removed now, and a new one is here without it. https://github.com/harishanand95/diffusers/tree/dml Probably better to open an issue relating to this on that repo than here, since this one is about cpu.
Also relevant discussion here: https://github.com/huggingface/diffusers/issues/284