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OpenVINO
OpenVINO is an open-source toolkit for optimizing and deploying deep learning models.
- Compiles models for your hardware.
- Supports Linux and Windows
- Supports CPU/GPU/GNA/NPU.
- Supports AMD GPUs on Windows with FP16 support. (Double performance over DirectML.)
- Supports INTEL GPUs including iGPUs.
- Supports NVIDIA GPUs.
- Supports CPUs with FP16, INT8 and INT4 support. (Double performance over PyTorch.)
- Supports multiple devices at the same time using Hetero Device.
It is basically a TensorRT / Olive competitor that works with any hardware.
Currently, INT8 and INT4 is CPU only. Enable Compress Model weights with NNCF
option in Compute Settings to use it.
Select a 4 bit mode from OpenVINO compress mode for NNCF
to use INT4.
Use the OpenVINO devices to use
option in Compute Settings
if you want to specify a device.
Selecting multiple devices will use multiple devices as a HETERO
device.
Using --device-id
cli argument with the WebUI will use a GPU with the specified Device ID.
Using --use-cpu openvino
cli argument with the WebUI will use the CPU.
OpenVINO will save compiled models to cache folder so you won't have to compile them again.
OpenVINO disable model caching
option in Compute Settings will disable caching.
Directory for OpenVINO cache
option in System Paths will set a new location for saving OpenVINO caches.
Same limitations with TensorRT / Olive applies here too.
Compilation takes a few minutes and any change to Resolution / Batch Size / LoRa will trigger recompilation.
Attention Slicing and HyperTile will not work.
OpenVINO will lock you in the Diffusers backend.
- Install the drivers for your device.
- Install
git
andpython
. - Open CMD in a folder you want to install SD.Next.
Install SD.Next from Github:
git clone https://github.com/vladmandic/automatic
Then enter into the automatic folder:
cd automatic
Then start WebUI with this command:
Windows:
.\webui.bat --use-openvino
Linux:
./webui.sh --use-openvino