Stable Diffusion v2.1 using Optimum-Intel OpenVINO and multiple Intel Hardware#

This Jupyter notebook can be launched after a local installation only.


This notebook will provide you a way to see different precision models performing in different hardware. This notebook was done for showing case the use of Optimum-Intel-OpenVINO and it is not optimized for running multiple times.


Table of contents:#

Optimum Intel is the interface between the Transformers and Diffusers libraries and the different tools and libraries provided by Intel to accelerate end-to-end pipelines on Intel architectures. More details in this repository.

Note: We suggest you to create a different environment and run the following installation command there.

%pip install -q "optimum-intel[openvino,diffusers]@git+" "ipywidgets" "transformers>=4.33.0" "torch>=2.1" --extra-index-url

Stable Diffusion pipeline should brings 6 elements together, a text encoder model with a tokenizer, a UNet model with and scheduler, and an Autoencoder with Decoder and Encoder models.



The base model used for this example is the stabilityai/stable-diffusion-2-1-base. This model was converted to OpenVINO format, for accelerated inference on CPU or Intel GPU with OpenVINO’s integration into Optimum.

import warnings


Showing Info Available Devices#

The available_devices property shows the available devices in your system. The “FULL_DEVICE_NAME” option to ie.get_property() shows the name of the device. Check what is the ID name for the discrete GPU, if you have integrated GPU (iGPU) and discrete GPU (dGPU), it will show device_name="GPU.0" for iGPU and device_name="GPU.1" for dGPU. If you just have either an iGPU or dGPU that will be assigned to "GPU"

import openvino as ov

core = ov.Core()
devices = core.available_devices

for device in devices:
    device_name = core.get_property(device, "FULL_DEVICE_NAME")
    print(f"{device}: {device_name}")
CPU: Intel(R) Core(TM) Ultra 7 155H
GPU: Intel(R) Arc(TM) Graphics (iGPU)
NPU: Intel(R) AI Boost

Configure Inference Pipeline#

Select device from dropdown list for running inference using OpenVINO.

import ipywidgets as widgets

device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],

Dropdown(description='Device:', index=1, options=('CPU', 'GPU', 'NPU', 'AUTO'), value='GPU')

Using full precision model in choice device with OVStableDiffusionPipeline#

from import OVStableDiffusionPipeline

# download the pre-converted SD v2.1 model from Hugging Face Hub
name = "helenai/stabilityai-stable-diffusion-2-1-base-ov"
ov_pipe = OVStableDiffusionPipeline.from_pretrained(name, compile=False)
ov_pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
import gc

# Generate an image.
prompt = "red car in snowy forest, epic vista, beautiful landscape, 4k, 8k"
output_ov = ov_pipe(prompt, num_inference_steps=17, output_type="pil").images[0]"image.png")
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del ov_pipe