Single step image generation using SDXL-turbo and OpenVINO#

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


SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. SDXL-Turbo is a distilled version of SDXL 1.0, trained for real-time synthesis. SDXL Turbo is based on a novel distillation technique called Adversarial Diffusion Distillation (ADD), which enables the model to synthesize image outputs in a single step and generate real-time text-to-image outputs while maintaining high sampling fidelity. More details about this distillation approach can be found in technical report. More details about model can be found in Stability AI blog post.

Previously, we already discussed how to launch Stable Diffusion XL model using OpenVINO in the following notebook, in this tutorial we will focus on the SDXL-turbo version. Additionally, to improve image decoding speed, we will use Tiny Autoencoder, which is useful for real-time previewing of the SDXL generation process.

We will use a pre-trained model from the Hugging Face Diffusers library. To simplify the user experience, the Hugging Face Optimum Intel library is used to convert the models to OpenVINO™ IR format.

Table of contents:#


%pip install -q --extra-index-url\
"torch>=2.1" transformers diffusers "git+" "gradio>=4.19" "peft==0.6.2" "openvino>=2023.3.0"

Convert model to OpenVINO format#

sdxl-turbo is available for downloading via the HuggingFace hub. We will use optimum-cli interface for exporting it into OpenVINO Intermediate Representation (IR) format.

Optimum CLI interface for converting models supports export to OpenVINO (supported starting optimum-intel 1.12 version). 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. Available tasks depend on the model, for sdxl should be selected stable-diffusion-xl

You can find a mapping between tasks and model classes in Optimum TaskManager documentation.

Additionally, you can specify weights compression --fp16 for the compression model to FP16 and --int8 for the compression model to INT8. Please note, that for INT8, it is necessary to install nncf.

Full list of supported arguments available via --help For more details and examples of usage, please check optimum documentation.

For Tiny Autoencoder, we will use ov.convert_model function for obtaining ov.Model and save it using ov.save_model. Model consists of 2 parts that used in pipeline separately: vae_encoder for encoding input image in latent space in image-to-image generation task and vae_decoder that responsible for decoding diffusion result back to image format.

from pathlib import Path

model_dir = Path("./model")
sdxl_model_id = "stabilityai/sdxl-turbo"
tae_id = "madebyollin/taesdxl"
skip_convert_model = model_dir.exists()
import torch
import openvino as ov
from diffusers import AutoencoderTiny
import gc

class VAEEncoder(torch.nn.Module):
    def __init__(self, vae):
        self.vae = vae

    def forward(self, sample):
        return self.vae.encode(sample)

class VAEDecoder(torch.nn.Module):
    def __init__(self, vae):
        self.vae = vae

    def forward(self, latent_sample):
        return self.vae.decode(latent_sample)

def convert_tiny_vae(model_id, output_path):
    tiny_vae = AutoencoderTiny.from_pretrained(model_id)
    vae_encoder = VAEEncoder(tiny_vae)
    ov_model = ov.convert_model(vae_encoder, example_input=torch.zeros((1, 3, 512, 512)))
    ov.save_model(ov_model, output_path / "vae_encoder/openvino_model.xml")
    tiny_vae.save_config(output_path / "vae_encoder")
    vae_decoder = VAEDecoder(tiny_vae)
    ov_model = ov.convert_model(vae_decoder, example_input=torch.zeros((1, 4, 64, 64)))
    ov.save_model(ov_model, output_path / "vae_decoder/openvino_model.xml")
    tiny_vae.save_config(output_path / "vae_decoder")

if not skip_convert_model:
    !optimum-cli export openvino --model $sdxl_model_id --task stable-diffusion-xl $model_dir --fp16
    convert_tiny_vae(tae_id, model_dir)

Text-to-image generation#

Text-to-image generation lets you create images using text description. To start generating images, we need to load models first. To load an OpenVINO model and run an inference with Optimum and OpenVINO Runtime, you need to replace diffusers StableDiffusionXLPipeline with Optimum OVStableDiffusionXLPipeline. Pipeline initialization starts with using from_pretrained method, where a directory with OpenVINO models should be passed. Additionally, you can specify an inference device.

Select inference device for text-to-image generation#

import ipywidgets as widgets

core = ov.Core()

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

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

text2image_pipe = OVStableDiffusionXLPipeline.from_pretrained(model_dir, device=device.value)
INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, onnx, openvino
/home/ea/work/genai_env/lib/python3.8/site-packages/torch/cuda/ UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 11080). Please update your GPU driver by downloading and installing a new version from the URL: Alternatively, go to: to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)
  return torch._C._cuda_getDeviceCount() > 0
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
Compiling the vae_decoder to AUTO ...
Compiling the unet to AUTO ...
Compiling the text_encoder to AUTO ...
Compiling the text_encoder_2 to AUTO ...
Compiling the vae_encoder to AUTO ...

The pipeline interface is similar to original StableDiffusionXLPipeline. We should provide text prompt. The default number of steps is 50, while sdxl-turbo required only 1 step. According to the information provided in model card, model does not use negative prompt and guidance scale and this parameters should be disabled using guidance_scale = 0

import numpy as np

prompt = "cute cat"
image = text2image_pipe(
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del text2image_pipe

Image-to-Image generation#

Image-to-image generation lets you transform images to match the characteristics provided in the text description. We can reuse the already converted model for running the Image2Image generation pipeline. For that, we should replace OVStableDiffusionXLPipeline with OVStableDiffusionXLImage2ImagePipeline.

from import OVStableDiffusionXLImg2ImgPipeline

image2image_pipe = OVStableDiffusionXLImg2ImgPipeline.from_pretrained(model_dir, device=device.value)
Compiling the vae_decoder to AUTO ...
Compiling the unet to AUTO ...
Compiling the text_encoder_2 to AUTO ...
Compiling the vae_encoder to AUTO ...
Compiling the text_encoder to AUTO ...
photo_prompt = "a cute cat with bow tie"

strength parameter is important for the image-to-image generation pipeline. It is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. Values that approach 1.0 enable lots of variations but will also produce images that are not semantically consistent with the input, then close to 0, less noise will be added and the target image will preserve source image content. strength has an impact not only on a number of noise but also the number of generation steps. The number of denoising iterations in the image-to-image generation pipeline is calculated as int(num_inference_steps * strength). With sdxl-turbo we should be careful with selecting num_inference_steps and strength to produce the correct result and make sure that the number of steps used in pipeline >= 1 after applying strength multiplication. e.g. in example below, we will use num_inference_steps=2 and stength=0.5, finally, we get 0.5 * 2.0 = 1 step in our pipeline.

photo_image = image2image_pipe(
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del image2image_pipe


NNCF enables post-training quantization by adding quantization layers into model graph and then using a subset of the training dataset to initialize the parameters of these additional quantization layers. Quantized operations are executed in INT8 instead of FP32/FP16 making model inference faster.

According to SDXL-Turbo Model structure, the UNet model takes up significant portion of the overall pipeline execution time. Now we will show you how to optimize the UNet part using NNCF to reduce computation cost and speed up the pipeline. Quantizing the rest of the SDXL pipeline does not significantly improve inference performance but can lead to a substantial degradation of accuracy.

The optimization process contains the following steps:

  1. Create a calibration dataset for quantization.

  2. Run nncf.quantize() to obtain quantized model.

  3. Save the INT8 model using openvino.save_model() function.

Please select below whether you would like to run quantization to improve model inference speed.

skip_for_device = "GPU" in device.value
to_quantize = widgets.Checkbox(value=not skip_for_device, description="Quantization", disabled=skip_for_device)
Checkbox(value=True, description='Quantization')
# Fetch `skip_kernel_extension` module
import requests

r = requests.get(
open("", "w").write(r.text)

int8_pipe = None

%load_ext skip_kernel_extension

Prepare calibration dataset#

We use a portion of conceptual_captions dataset from Hugging Face as calibration data. To collect intermediate model inputs for calibration we should customize CompiledModel.

UNET_INT8_OV_PATH = model_dir / "optimized_unet" / "openvino_model.xml"

def disable_progress_bar(pipeline, disable=True):
    if not hasattr(pipeline, "_progress_bar_config"):
        pipeline._progress_bar_config = {"disable": disable}
        pipeline._progress_bar_config["disable"] = disable
%%skip not $to_quantize.value

import datasets
import numpy as np
from tqdm.notebook import tqdm
from transformers import set_seed
from typing import Any, Dict, List


class CompiledModelDecorator(ov.CompiledModel):
    def __init__(self, compiled_model: ov.CompiledModel, data_cache: List[Any] = None):
        self.data_cache = data_cache if data_cache else []

    def __call__(self, *args, **kwargs):
        return super().__call__(*args, **kwargs)

def collect_calibration_data(pipe, subset_size: int) -> List[Dict]:
    original_unet = pipe.unet.request
    pipe.unet.request = CompiledModelDecorator(original_unet)

    dataset = datasets.load_dataset("conceptual_captions", split="train").shuffle(seed=42)

    # Run inference for data collection
    pbar = tqdm(total=subset_size)
    diff = 0
    for batch in dataset:
        prompt = batch["caption"]
        if len(prompt) > pipe.tokenizer.model_max_length:
        _ = pipe(
        collected_subset_size = len(pipe.unet.request.data_cache)
        if collected_subset_size >= subset_size:
            pbar.update(subset_size - pbar.n)
        pbar.update(collected_subset_size - diff)
        diff = collected_subset_size

    calibration_dataset = pipe.unet.request.data_cache
    disable_progress_bar(pipe, disable=False)
    pipe.unet.request = original_unet
    return calibration_dataset
%%skip not $to_quantize.value

if not UNET_INT8_OV_PATH.exists():
    text2image_pipe = OVStableDiffusionXLPipeline.from_pretrained(model_dir, device=device.value)
    unet_calibration_data = collect_calibration_data(text2image_pipe, subset_size=200)

Run quantization#

Create a quantized model from the pre-trained converted OpenVINO model. Quantization of the first and last Convolution layers impacts the generation results. We recommend using IgnoredScope to keep accuracy sensitive Convolution layers in FP16 precision.

NOTE: Quantization is time and memory consuming operation. Running quantization code below may take some time.

%%skip not $to_quantize.value

import nncf
from nncf.scopes import IgnoredScope

UNET_OV_PATH = model_dir / "unet" / "openvino_model.xml"
if not UNET_INT8_OV_PATH.exists():
    unet = core.read_model(UNET_OV_PATH)
    quantized_unet = nncf.quantize(
    ov.save_model(quantized_unet, UNET_INT8_OV_PATH)

Let us check predictions with the quantized UNet using the same input data.

%%skip not $to_quantize.value

from IPython.display import display

int8_text2image_pipe = OVStableDiffusionXLPipeline.from_pretrained(model_dir, device=device.value, compile=False)
int8_text2image_pipe.unet.model = core.read_model(UNET_INT8_OV_PATH)
int8_text2image_pipe.unet.request = None

prompt = "cute cat"
image = int8_text2image_pipe(prompt, num_inference_steps=1, height=512, width=512, guidance_scale=0.0, generator=np.random.RandomState(987)).images[0]
Compiling the text_encoder to AUTO ...
Compiling the text_encoder_2 to AUTO ...
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Compiling the unet to AUTO ...
Compiling the vae_decoder to AUTO ...
%%skip not $to_quantize.value

int8_image2image_pipe = OVStableDiffusionXLImg2ImgPipeline.from_pretrained(model_dir, device=device.value, compile=False)
int8_image2image_pipe.unet.model = core.read_model(UNET_INT8_OV_PATH)
int8_image2image_pipe.unet.request = None

photo_prompt = "a cute cat with bow tie"
photo_image = int8_image2image_pipe(photo_prompt, image=image, num_inference_steps=2, generator=np.random.RandomState(511), guidance_scale=0.0, strength=0.5).images[0]
Compiling the text_encoder to AUTO ...
Compiling the text_encoder_2 to AUTO ...
Compiling the vae_encoder to AUTO ...
0%|          | 0/1 [00:00<?, ?it/s]
Compiling the unet to AUTO ...
Compiling the vae_decoder to AUTO ...

Compare UNet file size#

%%skip not $to_quantize.value

fp16_ir_model_size = UNET_OV_PATH.with_suffix(".bin").stat().st_size / 1024
quantized_model_size = UNET_INT8_OV_PATH.with_suffix(".bin").stat().st_size / 1024

print(f"FP16 model size: {fp16_ir_model_size:.2f} KB")
print(f"INT8 model size: {quantized_model_size:.2f} KB")
print(f"Model compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")
FP16 model size: 5014578.27 KB
INT8 model size: 2513541.44 KB
Model compression rate: 1.995

To measure the inference performance of the FP16 and INT8 pipelines, we use median inference time on calibration subset.

NOTE: For the most accurate performance estimation, it is recommended to run benchmark_app in a terminal/command prompt after closing other applications.

%%skip not $to_quantize.value

import time

validation_size = 7
calibration_dataset = datasets.load_dataset("conceptual_captions", split="train")
validation_data = []
for batch in calibration_dataset:
    prompt = batch["caption"]

def calculate_inference_time(pipe, dataset):
    inference_time = []

    for idx, prompt in enumerate(dataset):
        start = time.perf_counter()
        image = pipe(
        end = time.perf_counter()
        delta = end - start
        if idx >= validation_size:
    disable_progress_bar(pipe, disable=False)
    return np.median(inference_time)
%%skip not $to_quantize.value

int8_latency = calculate_inference_time(int8_text2image_pipe, validation_data)
text2image_pipe = OVStableDiffusionXLPipeline.from_pretrained(model_dir, device=device.value)
fp_latency = calculate_inference_time(text2image_pipe, validation_data)
print(f"FP16 pipeline latency: {fp_latency:.3f}")
print(f"INT8 pipeline latency: {int8_latency:.3f}")
print(f"Text-to-Image generation speed up: {fp_latency / int8_latency:.3f}")
Compiling the vae_decoder to AUTO ...
Compiling the unet to AUTO ...
Compiling the text_encoder_2 to AUTO ...
Compiling the text_encoder to AUTO ...
Compiling the vae_encoder to AUTO ...
FP16 pipeline latency: 1.391
INT8 pipeline latency: 0.781
Text-to-Image generation speed up: 1.780

Interactive Demo#

Now, you can check model work using own text descriptions. Provide text prompt in the text box and launch generation using Run button. Additionally you can control generation with additional parameters: * Seed - random seed for initialization * Steps - number of generation steps * Height and Width - size of generated image

Please note that increasing image size may require to increasing number of steps for accurate result. We recommend running 104x1024 resolution image generation using 4 steps.

Please select below whether you would like to use the quantized model to launch the interactive demo.

quantized_model_present = UNET_INT8_OV_PATH.exists()

use_quantized_model = widgets.Checkbox(
    value=True if quantized_model_present else False,
    description="Use quantized model",

Checkbox(value=True, description='Use quantized model')
import gradio as gr

text2image_pipe = OVStableDiffusionXLPipeline.from_pretrained(model_dir, device=device.value)
if use_quantized_model.value:
    if not quantized_model_present:
        raise RuntimeError("Quantized model not found.")
    text2image_pipe.unet.model = core.read_model(UNET_INT8_OV_PATH)
    text2image_pipe.unet.request = core.compile_model(text2image_pipe.unet.model, device.value)

def generate_from_text(text, seed, num_steps, height, width):
    result = text2image_pipe(
    return result

with gr.Blocks() as demo:
    with gr.Column():
        positive_input = gr.Textbox(label="Text prompt")
        with gr.Row():
            seed_input = gr.Number(precision=0, label="Seed", value=42, minimum=0)
            steps_input = gr.Slider(label="Steps", value=1, minimum=1, maximum=4, step=1)
            height_input = gr.Slider(label="Height", value=512, minimum=256, maximum=1024, step=32)
            width_input = gr.Slider(label="Width", value=512, minimum=256, maximum=1024, step=32)
            btn = gr.Button()
        out = gr.Image(
            label=("Result (Quantized)" if use_quantized_model.value else "Result (Original)"),
            [positive_input, seed_input, steps_input, height_input, width_input],
                ["cute cat", 999],
                    "underwater world coral reef, colorful jellyfish, 35mm, cinematic lighting, shallow depth of field,  ultra quality, masterpiece, realistic",
                    "a photo realistic happy white poodle dog ​​playing in the grass, extremely detailed, high res, 8k, masterpiece, dynamic angle",
                    "Astronaut on Mars watching sunset, best quality, cinematic effects,",
                    "Black and white street photography of a rainy night in New York, reflections on wet pavement",
            [positive_input, seed_input],

# if you are launching remotely, specify server_name and server_port
# demo.launch(server_name='your server name', server_port='server port in int')
# Read more in the docs:
# if you want create public link for sharing demo, please add share=True
except Exception:
    demo.launch(share=True, debug=False)