ONNX Runtime

ONNX Runtime

SD.Next includes support for ONNX Runtime.

How to

Currently, we can't use --use-directml because there's no release of torch-directml built with latest PyTorch. (this does not mean that you can't use DmlExecutionProvider)

Change Execution backend to diffusers and Diffusers pipeline to ONNX Stable Diffusion on the System tab.

Performance

The performance depends on the execution provider.

Execution Providers

Currently, CUDAExecutionProvider and DmlExecutionProvider are supported.

	ONNX	Olive	GPU	CPU
CPUExecutionProvider	✅	❌	❌	✅
DmlExecutionProvider	✅	✅	✅	❌
CUDAExecutionProvider	✅	✅	✅	❌
ROCMExecutionProvider	✅	🚧	✅	❌
OpenVINOExecutionProvider	✅	✅	✅	✅

CPUExecutionProvider

Not recommended.

Enabled by default.

DmlExecutionProvider

You can select DmlExecutionProvider by installing onnxruntime-directml.

DirectX 12 API is required. (Windows or WSL)

CUDAExecutionProvider

You can select CUDAExecutionProvider by installing onnxruntime-gpu. (may have been automatically installed)

🚧 ROCMExecutionProvider

Olive for ROCm is working in progress.

🚧 OpenVINOExecutionProvider

Under development.

Supported

• Models from huggingface
• Hires and second pass (without sdxl refiner)
• .safetensors VAE

Known issues

• SD Inpaint may not work.
• SD Upscale pipeline is not tested.
• SDXL Refiner does not work. (due to onnxruntime's issue)

FAQ

I'm getting OnnxStableDiffusionPipeline.__init__() missing 4 required positional arguments: 'vae_encoder', 'vae_decoder', 'text_encoder', and 'unet'.

It's due to the broken model cache which was previously generated by failed conversion or Olive run. Find one in models/ONNX/cache and remove it. You can also use ONNX tab on UI. (You should enable it on settings to make it show up)

Olive

Olive is an easy-to-use hardware-aware model optimization tool that composes industry-leading techniques across model compression, optimization, and compilation. (from pypi)

How to

As Olive optimizes the models in ONNX format, you should set up ONNX Runtime first.

1. Go to System tab → Compute Settings.
2. Select Model, Text Encoder and VAE in Compile Model.
3. Set Model compile backend to olive-ai.

Olive-specific settings are under Olive in Compute Settings.

How to switch to Olive from torch-directml

Run these commands using PowerShell.

.\venv\Scripts\activate
pip uninstall torch-directml
pip install torch torchvision --upgrade
pip install onnxruntime-directml
.\webui.bat

From checkpoint

Model optimization occurs automatically before generation.

Target models can be .safetensors, .ckpt, Diffusers pretrained model and the optimization progress takes time depending on your system and execution provider.

The optimized models are automatically cached and used later to create images of the same size (height and width).

From Huggingface

If your system memory is not enough to optimize model or you don't want to waste your time to optimize the model yourself, you can download optimized model from Huggingface.

Go to Models → Huggingface tab and download optimized model.

Advanced Usage

Customize Olive workflow

TBA

Performance

Property	Value
Prompt	a castle, best quality
Negative Prompt	worst quality
Sampler	Euler
Sampling Steps	20
Device	RX 7900 XTX 24GB
Version	olive-ai(0.4.0) onnxruntime-directml(1.16.3) ROCm(5.6) torch(olive: 2.1.2, rocm: 2.1.0)
Model	runwayml/stable-diffusion-v1-5 (ROCm), lshqqytiger/stable-diffusion-v1-5-olive (Olive)
Precision	fp16
Token Merging	Olive(0, not supported) ROCm(0.5)

Olive with DmlExecutionProvider	ROCm

Pros and Cons

Pros

• The generation is faster.
• Uses less graphics memory.

Cons

• Optimization is required for every models and image sizes.
• Some features are unavailable.

FAQ

My execution provider does not show up in my settings

After activating python venv, run this command and try again:

(venv) $ pip uninstall onnxruntime onnxruntime-... -y