Notebook

Image generation with Stable Diffusion v3 and OpenVINO¶

Stable Diffusion V3 is next generation of latent diffusion image Stable Diffusion models family that outperforms state-of-the-art text-to-image generation systems in typography and prompt adherence, based on human preference evaluations. In comparison with previous versions, it based on Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.

More details about model can be found in model card, research paper and Stability.AI blog post.

In this tutorial, we will consider how to convert Stable Diffusion v3 for running with OpenVINO. That notebook shows how to perform optimization with NNCF.

If you want to run previous Stable Diffusion versions, please check our other notebooks:

Installation Instructions¶

This is a self-contained example that relies solely on its own code.

We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. For details, please refer to Installation Guide.

Table of contents:¶

Prerequisites
Build PyTorch pipeline
Convert models with OpenVINO
- Convert model using Optimum Intel
- Compress model weights
Prepare OpenVINO inference pipeline
Run OpenVINO model
Interactive demo

Prerequisites¶

In [1]:

import platform

%pip install -q "gradio>=4.19" "diffusers>=0.30.0" "torch>=2.1"  "transformers[sentencepiece]" "nncf>=2.12.0" "datasets>=2.14.6" "opencv-python" "pillow" "peft>=0.7.0" --extra-index-url https://download.pytorch.org/whl/cpu
%pip install -q "git+https://github.com/huggingface/optimum-intel.git" --extra-index-url https://download.pytorch.org/whl/cpu
%pip install -qU "openvino>=2025.0.0" "openvino-tokenizers" "openvino-genai"

if platform.system() == "Darwin":
    %pip install "numpy<2.0.0"

In [2]:

import requests
from pathlib import Path

if not Path("sd3_helper.py").exists():
    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/stable-diffusion-v3/sd3_helper.py")
    open("sd3_helper.py", "w").write(r.text)

if not Path("gradio_helper.py").exists():
    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/stable-diffusion-v3/gradio_helper.py")
    open("gradio_helper.py", "w").write(r.text)

if not Path("notebook_utils.py").exists():
    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py")
    open("notebook_utils.py", "w").write(r.text)

if not Path("cmd_helper.py").exists():
    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/cmd_helper.py")
    open("cmd_helper.py", "w").write(r.text)

# Read more about telemetry collection at https://github.com/openvinotoolkit/openvino_notebooks?tab=readme-ov-file#-telemetry
from notebook_utils import collect_telemetry

collect_telemetry("stable-diffusion-v3.ipynb")

Build PyTorch pipeline¶

Note: run model with notebook, you will need to accept license agreement. You must be a registered user in 🤗 Hugging Face Hub. Please visit selected model card, e.g. stabilityai/stable-diffusion-3-medium-diffusers, carefully read terms of usage and click accept button. You will need to use an access token for the code below to run. For more information on access tokens, refer to this section of the documentation. You can login on Hugging Face Hub in notebook environment, using following code:

In [3]:

# uncomment these lines to login to huggingfacehub to get access to pretrained model

# from huggingface_hub import notebook_login, whoami

# try:
#     whoami()
#     print('Authorization token already provided')
# except OSError:
#     notebook_login()

We will use Diffusers library integration for running Stable Diffusion v3 model. You can find more details in Diffusers documentation. Additionally, we can apply optimization for pipeline performance and memory consumption:

Use turbo model version. Turbo model variant is distilled using Adversarial Diffusion Distillation (ADD) approach. This features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency, with a focus on fewer inference steps.
Remove T5 text encoder. Removing the memory-intensive 4.7B parameter T5-XXL text encoder during inference can significantly decrease the memory requirements for SD3 with only a slight loss in performance. If you want to use this model in pipeline, please set use t5 text encoder checkbox.

In [4]:

from sd3_helper import get_pipeline_options

pt_pipeline_options, model_selector, load_t5, to_compress = get_pipeline_options()

display(pt_pipeline_options)

VBox(children=(Dropdown(options=('tensorart/stable-diffusion-3.5-medium-turbo', 'stabilityai/stable-diffusion-…

Convert models with OpenVINO¶

Starting from 2023.0 release, OpenVINO supports PyTorch models directly via Model Conversion API. ov.convert_model function accepts instance of PyTorch model and example inputs for tracing and returns object of ov.Model class, ready to use or save on disk using ov.save_model function.

The pipeline consists of four important parts:

Clip and T5 Text Encoders to create condition to generate an image from a text prompt.
Transformer for step-by-step denoising latent image representation.
Autoencoder (VAE) for decoding latent space to image.

Convert model using Optimum Intel¶

For convenience, we will use OpenVINO integration with HuggingFace Optimum. 🤗 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.

Among other use cases, Optimum Intel provides a simple interface to optimize your Transformers and Diffusers models, convert them to the OpenVINO Intermediate Representation (IR) format and run inference using OpenVINO Runtime. optimum-cli provides command line interface for model conversion and optimization.

General command format:

optimum-cli export openvino --model <model_id_or_path> --task <task> <output_dir>

where task is task to export the model for, if not specified, the task will be auto-inferred based on the model. You can find a mapping between tasks and model classes in Optimum TaskManager documentation. Additionally, you can specify weights compression using --weight-format argument with one of following options: fp32, fp16, int8 and int4. Fro int8 and int4 nncf will be used for weight compression. More details about model export provided in Optimum Intel documentation.

Compress model weights¶

For reducing model memory consumption we will use weights compression. The Weights Compression algorithm is aimed at compressing the weights of the models and can be used to optimize the model footprint and performance of large models where the size of weights is relatively larger than the size of activations, for example, Large Language Models (LLM). Compared to INT8 compression, INT4 compression improves performance even more, but introduces a minor drop in prediction quality. We will use NNCF integration to optimum-cli tool for weight compression.

In [5]:

from cmd_helper import optimum_cli

model_id = model_selector.value
print(f"Selected {model_id} model")

base_model_path = Path(model_id.split("/")[-1])

model_path = base_model_path / ("FP16" if not to_compress.value else "INT4")

if not to_compress.value:
    additional_args = {"weight-format": "fp16"}
else:
    additional_args = {"weight-format": "int4", "group-size": "64", "ratio": "1.0"}


if not model_path.exists():
    optimum_cli(model_id, model_path)

Selected tensorart/stable-diffusion-3.5-medium-turbo model

Prepare OpenVINO inference pipeline¶

openvino_genai.Text2ImagePipeline represents inference pipeline for text-to-image generation. For creation pipeline instance, you should provide directory with converted to OpenVINO model and inference device.

In [6]:

import openvino_genai as ov_genai
from sd3_helper import init_pipeline_without_t5  # noqa: F401

Run OpenVINO model¶

In [7]:

from notebook_utils import device_widget

device = device_widget()

device

/home/ea/work/py311/lib/python3.11/site-packages/openvino/runtime/__init__.py:10: DeprecationWarning: The `openvino.runtime` module is deprecated and will be removed in the 2026.0 release. Please replace `openvino.runtime` with `openvino`.
  warnings.warn(

Out[7]:

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

In [8]:

if not load_t5.value:
    ov_pipe = init_pipeline_without_t5(model_path, device.value)
else:
    ov_pipe = ov_genai.Text2ImagePipeline(model_path, device.value)

In [9]:

from PIL import Image
from tqdm.notebook import tqdm
import sys

num_inference_steps = 28 if "turbo" not in model_id else 8

pbar = tqdm(total=num_inference_steps)


def callback(step, num_steps, latent):
    if num_steps != pbar.total:
        pbar.reset(num_steps)
    pbar.update(1)
    sys.stdout.flush()
    return False


image_tensor = ov_pipe.generate(
    "A raccoon trapped inside a glass jar full of colorful candies, the background is steamy with vivid colors",
    num_inference_steps=num_inference_steps,
    guidance_scale=5 if "turbo" not in model_id else 0.5,
    height=512,
    width=512,
    generator=ov_genai.TorchGenerator(141),
    callback=callback,
)

image = Image.fromarray(image_tensor.data[0])

  0%|          | 0/8 [00:00<?, ?it/s]

In [10]:

image

Out[10]:

Interactive demo¶

In [ ]:

from gradio_helper import make_demo

demo = make_demo(ov_pipe, "turbo" in model_id)

# if you are launching remotely, specify server_name and server_port
#  demo.launch(server_name='your server name', server_port='server port in int')
# if you have any issue to launch on your platform, you can pass share=True to launch method:
# demo.launch(share=True)
# it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
try:
    demo.launch(debug=True)
except Exception:
    demo.launch(debug=True, share=True)