[UPDATE]: The Automatic1111-directML branch now supports Microsoft Olive under the Automatic1111 WebUI interface, which allows for generating optimized models and running them all under the Automatic1111 WebUI, without a separate branch needed to optimize for AMD platforms. The updated blog to run Stable Diffusion Automatic1111 with Olive Optimizations is available here - UPDATED HOW-TO GUIDE
Prepared by Hisham Chowdhury (AMD), Lucas Neves (AMD), and Justin Stoecker (Microsoft)
Did you know you can enable Stable Diffusion with Microsoft Olive under Automatic1111(Xformer) to get a significant speedup via Microsoft DirectML on Windows? Microsoft and AMD have been working together to optimize the Olive path on AMD hardware, accelerated via the Microsoft DirectML platform API and the AMD User Mode Driver’s ML (Machine Learning) layer for DirectML allowing users access to the power of the AMD GPU’s AI (Artificial Intelligence) capabilities.
2. Overview of Microsoft Olive
Microsoft Olive is a Python tool that can be used to convert, optimize, quantize, and auto-tune models for optimal inference performance with ONNX Runtime execution providers like DirectML. Olive greatly simplifies model processing by providing a single toolchain to compose optimization techniques, which is especially important with more complex models like Stable Diffusion that are sensitive to the ordering of optimization techniques. The DirectML sample for Stable Diffusion applies the following techniques:
Combined, the above optimizations enable DirectML to leverage AMD GPUs for greatly improved performance when performing inference with transformer models like Stable Diffusion.
3. Generate Optimized Stable Diffusion Models using Microsoft Olive
Create Optimized Model
(Following the instruction from Olive, we can generate optimized Stable Diffusion model using Olive)
The optimized model will be stored at the following directory, keep this open for later: olive\examples\directml\stable_diffusion\models\optimized\runwayml. The model folder will be called “stable-diffusion-v1-5”. Use the following command to see what other models are supported: python stable_diffusion.py –help
To Test the Optimized Model
4. Install and Run Automatic1111 Stable Diffusion WebUI
Following the instructions here, install Automatic1111 Stable Diffusion WebUI without the optimized model. It will be using the default unoptimized PyTorch path. Enter the following commands sequentially into a new terminal window.
5. Enable Olive Optimized Path on AMD Radeon
Integrate the optimized model
Run the Automatic1111 WebUI with the Optimized Model
4. Pick “stable-diffusion-v1-5” from dropdown
Running on the default PyTorch path, the AMD Radeon RX 7900 XTX delivers 1.87 iterations/second.
Running on the optimized model with Microsoft Olive, the AMD Radeon RX 7900 XTX delivers 18.59 iterations/second.
End Result is up to 9.9X improvement to performance on AMD Radeon™ RX 7900 XTX.
7. Disclaimers & Footnotes
Links to third-party sites are provided for convenience and unless explicitly stated, AMD is not responsible for the contents of such linked sites, and no endorsement is implied. GD-98
Testing conducted by AMD as of August 15th, 2023, on a test system configured with a Ryzen9 7950X 3D(4.2GHz) CPU, 32GB DDR5, Radeon RX 7900XTX GPU, Windows 11 Pro, with AMD Software: Adrenalin Edition 23.7.2, using the application Stable Diffusion 1.5 with Microsoft Olive under Automatic 1111 vs. Default Automatic 1111.
Performance may vary. System manufacturers may vary configurations, yielding different results. RS-587
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