ONNX Runtime
SD.Next includes experimental support for ONNX Runtime.
You should switch branch to olive.
Change Execution backend to diffusers and Diffusers pipeline to ONNX Stable Diffusion or ONNX Stable Diffusion with Olive on System tab.
The performance depends on the execution provider.
Olive is an easy-to-use hardware-aware model optimization tool that composes industry-leading techniques across model compression, optimization, and compilation. (from pypi)
Currently, SDXL is not supported.
This feature is EXPERIMENTAL. If you run this, your existing installation may be broken. Run it in a new installation or in a new virtual environment.
You should switch branch to olive.
Go to System tab → Diffusers Settings and set Diffusers pipeline to ONNX Stable Diffusion with Olive.
Guide on YouTube:
Model optimization occurs automatically before generation.
Target models can be .safetensors, .ckpt, Diffusers and the optimization 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).
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.
There's an optimized version of runwayml/stable-diffusion-v1-5.
Guide on YouTube:
| Property | Value |
|---|---|
| Prompt | a castle, best quality |
| Negative Prompt | worst quality |
| Sampler | Euler |
| Sampling Steps | 20 |
| Device | RX 7900 XTX 24GB |
| Version | olive-ai(0.3.3) onnxruntime-directml(1.16.1) ROCm(5.6) torch(olive: 1.13.1, 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 | ROCm |
|---|---|
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- The generation is faster.
- Uses less graphics memory.
- Optimization is required for every models and image sizes.
- Some features are unavailable.
Run this command and try again:
(venv) $ pip uninstall onnxruntime onnxruntime-... -y