openvino/docs/notebooks/225-stable-diffusion-text-to-image-with-output.rst

Text-to-Image Generation with Stable Diffusion and OpenVINO™
============================================================

.. _top:

Stable Diffusion is a text-to-image latent diffusion model created by
the researchers and engineers from
`CompVis <https://github.com/CompVis>`__, `Stability
AI <https://stability.ai/>`__ and `LAION <https://laion.ai/>`__. It is
trained on 512x512 images from a subset of the
`LAION-5B <https://laion.ai/blog/laion-5b/>`__ database. This model uses
a frozen CLIP ViT-L/14 text encoder to condition the model on text
prompts. With its 860M UNet and 123M text encoder. See the `model
card <https://huggingface.co/CompVis/stable-diffusion>`__ for more
information.

General diffusion models are machine learning systems that are trained
to denoise random gaussian noise step by step, to get to a sample of
interest, such as an image. Diffusion models have shown to achieve
state-of-the-art results for generating image data. But one downside of
diffusion models is that the reverse denoising process is slow. In
addition, these models consume a lot of memory because they operate in
pixel space, which becomes unreasonably expensive when generating
high-resolution images. Therefore, it is challenging to train these
models and also use them for inference. OpenVINO brings capabilities to
run model inference on Intel hardware and opens the door to the
fantastic world of diffusion models for everyone!

Model capabilities are not limited text-to-image only, it also is able
solve additional tasks, for example text-guided image-to-image
generation and inpainting. This tutorial also considers how to run
text-guided image-to-image generation using Stable Diffusion.

This notebook demonstrates how to convert and run stable diffusion model
using OpenVINO.

Notebook contains the following steps:

1. Convert PyTorch models to ONNX format.
2. Convert ONNX models to OpenVINO IR format, using model conversion
   API.
3. Run Stable Diffusion pipeline with OpenVINO.

**Table of contents**:

- `Prerequisites <#prerequisites>`__
- `Create PyTorch Models pipeline <#create-pytorch-models-pipeline>`__
- `Convert models to OpenVINO Intermediate representation (IR) format <#convert-models-to-openvino-intermediate-representation-ir-format>`__

  - `Text Encoder <#text-encoder>`__
  - `U-net <#u-net>`__
  - `VAE <#vae>`__

- `Prepare Inference Pipeline <#prepare-inference-pipeline>`__
- `Configure Inference Pipeline <#configure-inference-pipeline>`__

  - `Text-to-Image generation <#text-to-image-generation>`__
  - `Image-to-Image generation <#image-to-image-generation>`__

Prerequisites `⇑ <#top>`__
###############################################################################################################################


**The following is needed only if you want to use the original model. If
not, you do not have to do anything. Just run the notebook.**

   **Note**: The original model (for example, ``stable-diffusion-v1-4``)
   requires you to accept the model license before downloading or using
   its weights. Visit the `stable-diffusion-v1-4
   card <https://huggingface.co/CompVis/stable-diffusion-v1-4>`__ to
   read and accept the license before you proceed. To use this diffusion
   model, you must be a registered user in 🤗 Hugging Face Hub. 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 <https://huggingface.co/docs/hub/security-tokens>`__.
   You can login on Hugging Face Hub in notebook environment, using
   following code:

.. code:: python


   ## 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()

This tutorial uses a Stable Diffusion model, fine-tuned using images
from Midjourney v4 (another popular solution for text to image
generation). You can find more details about this model on the `model
card <https://huggingface.co/prompthero/openjourney>`__. The same steps
for conversion and running the pipeline are applicable to other
solutions based on Stable Diffusion.

.. code:: ipython3

    !pip install -q "diffusers[torch]>=0.9.0"
    !pip install -q "huggingface-hub>=0.9.1"


Create PyTorch Models pipeline `⇑ <#top>`__
###############################################################################################################################

``StableDiffusionPipeline`` is an end-to-end inference pipeline that you can use to generate images
from text with just a few lines of code.

First, load the pre-trained weights of all components of the model.

.. code:: ipython3

    from diffusers import StableDiffusionPipeline
    
    pipe = StableDiffusionPipeline.from_pretrained("prompthero/openjourney").to("cpu")
    text_encoder = pipe.text_encoder
    text_encoder.eval()
    unet = pipe.unet
    unet.eval()
    vae = pipe.vae
    vae.eval()
    
    del pipe



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Convert models to OpenVINO Intermediate representation (IR) format. `⇑ <#top>`__
###############################################################################################################################

OpenVINO supports PyTorch through export to the ONNX format. You will
use ``torch.onnx.export`` function for obtaining ONNX model. You can
learn more in the `PyTorch
documentation <https://pytorch.org/docs/stable/onnx.html>`__. You need
to provide a model object, input data for model tracing and a path for
saving the model. Optionally, you can provide the target onnx opset for
conversion and other parameters specified in documentation (for example,
input and output names or dynamic shapes).

While ONNX models are directly supported by OpenVINO™ runtime, it can be
useful to convert them to IR format to take advantage of advanced
OpenVINO optimization tools and features. For converting the model to IR
format and compressing weights to ``FP16`` format, you will use model
conversion API.

The model consists of three important parts:

-  Text Encoder for creation condition to generate image from text
   prompt.
-  Unet for step by step denoising latent image representation.
-  Autoencoder (VAE) for encoding input image to latent space (if
   required) and decoding latent space to image back after generation.

Let us convert each part.

Text Encoder `⇑ <#top>`__
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


The text-encoder is responsible for transforming the input prompt, for
example, “a photo of an astronaut riding a horse” into an embedding
space that can be understood by the U-Net. It is usually a simple
transformer-based encoder that maps a sequence of input tokens to a
sequence of latent text embeddings.

Input of the text encoder is the tensor ``input_ids`` which contains
indexes of tokens from text processed by tokenizer and padded to maximum
length accepted by model. Model outputs are two tensors:
``last_hidden_state`` - hidden state from the last MultiHeadAttention
layer in the model and ``pooler_out`` - Pooled output for whole model
hidden states. You will use ``opset_version=14``, because model contains
``triu`` operation, supported in ONNX only starting from this opset.

.. code:: ipython3

    import gc
    from pathlib import Path
    import torch
    
    TEXT_ENCODER_ONNX_PATH = Path('text_encoder.onnx')
    TEXT_ENCODER_OV_PATH = TEXT_ENCODER_ONNX_PATH.with_suffix('.xml')
    
    
    def convert_encoder_onnx(xtext_encoder: StableDiffusionPipeline, onnx_path:Path):
        """
        Convert Text Encoder model to ONNX. 
        Function accepts pipeline, prepares example inputs for ONNX conversion via torch.export, 
        Parameters: 
            pipe (StableDiffusionPipeline): Stable Diffusion pipeline
            onnx_path (Path): File for storing onnx model
        Returns:
            None
        """
        if not onnx_path.exists():
            input_ids = torch.ones((1, 77), dtype=torch.long)
            # switch model to inference mode
            text_encoder.eval()
    
            # disable gradients calculation for reducing memory consumption
            with torch.no_grad():
                # infer model, just to make sure that it works
                text_encoder(input_ids)
                # export model to ONNX format
                torch.onnx.export(
                    text_encoder,  # model instance
                    input_ids,  # inputs for model tracing
                    onnx_path,  # output file for saving result
                    input_names=['tokens'],  # model input name for onnx representation
                    output_names=['last_hidden_state', 'pooler_out'],  # model output names for onnx representation
                    opset_version=14  # onnx opset version for export
                )
            print('Text Encoder successfully converted to ONNX')
        
    
    if not TEXT_ENCODER_OV_PATH.exists():
        convert_encoder_onnx(text_encoder, TEXT_ENCODER_ONNX_PATH)
        !mo --input_model $TEXT_ENCODER_ONNX_PATH --compress_to_fp16
        print('Text Encoder successfully converted to IR')
    else:
        print(f"Text encoder will be loaded from {TEXT_ENCODER_OV_PATH}")
    
    del text_encoder
    gc.collect()


.. parsed-literal::

    Text encoder will be loaded from text_encoder.xml




.. parsed-literal::

    13



U-net `⇑ <#top>`__
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


Unet model has three inputs:

- ``sample`` - latent image sample from previous step. Generation
   process has not been started yet, so you will use random noise.
- ``timestep`` - current scheduler step.
- ``encoder_hidden_state`` - hidden state of text encoder.

Model predicts the ``sample`` state for the next step.

.. code:: ipython3

    import numpy as np
    
    UNET_ONNX_PATH = Path('unet/unet.onnx')
    UNET_OV_PATH = UNET_ONNX_PATH.parents[1] / 'unet.xml'
    
    
    def convert_unet_onnx(unet:StableDiffusionPipeline, onnx_path:Path):
        """
        Convert Unet model to ONNX, then IR format. 
        Function accepts pipeline, prepares example inputs for ONNX conversion via torch.export, 
        Parameters: 
            pipe (StableDiffusionPipeline): Stable Diffusion pipeline
            onnx_path (Path): File for storing onnx model
        Returns:
            None
        """
        if not onnx_path.exists():
            # prepare inputs
            encoder_hidden_state = torch.ones((2, 77, 768))
            latents_shape = (2, 4, 512 // 8, 512 // 8)
            latents = torch.randn(latents_shape)
            t = torch.from_numpy(np.array(1, dtype=float))
    
            # model size > 2Gb, it will be represented as onnx with external data files, you will store it in separated directory for avoid a lot of files in current directory
            onnx_path.parent.mkdir(exist_ok=True, parents=True)
            unet.eval()
    
            with torch.no_grad():
                torch.onnx.export(
                    unet, 
                    (latents, t, encoder_hidden_state), str(onnx_path),
                    input_names=['latent_model_input', 't', 'encoder_hidden_states'],
                    output_names=['out_sample']
                )
            print('Unet successfully converted to ONNX')
    
    
    if not UNET_OV_PATH.exists():
        convert_unet_onnx(unet, UNET_ONNX_PATH)
        del unet
        gc.collect()
        !mo --input_model $UNET_ONNX_PATH --compress_to_fp16
        print('Unet successfully converted to IR')
    else:
        del unet
        print(f"Unet will be loaded from {UNET_OV_PATH}")
    gc.collect()


.. parsed-literal::

    Unet will be loaded from unet.xml




.. parsed-literal::

    0



VAE `⇑ <#top>`__
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


The VAE model has two parts, an encoder and a decoder. The encoder is
used to convert the image into a low dimensional latent representation,
which will serve as the input to the U-Net model. The decoder,
conversely, transforms the latent representation back into an image.

During latent diffusion training, the encoder is used to get the latent
representations (latents) of the images for the forward diffusion
process, which applies more and more noise at each step. During
inference, the denoised latents generated by the reverse diffusion
process are converted back into images using the VAE decoder. When you
run inference for text-to-image, there is no initial image as a starting
point. You can skip this step and directly generate initial random
noise.

As the encoder and the decoder are used independently in different parts
of the pipeline, it will be better to convert them to separate models.

.. code:: ipython3

    VAE_ENCODER_ONNX_PATH = Path('vae_encoder.onnx')
    VAE_ENCODER_OV_PATH = VAE_ENCODER_ONNX_PATH.with_suffix('.xml')
    
    
    def convert_vae_encoder_onnx(vae: StableDiffusionPipeline, onnx_path: Path):
        """
        Convert VAE model to ONNX, then IR format. 
        Function accepts pipeline, creates wrapper class for export only necessary for inference part, 
        prepares example inputs for ONNX conversion via torch.export, 
        Parameters: 
            pipe (StableDiffusionInstructPix2PixPipeline): InstrcutPix2Pix pipeline
            onnx_path (Path): File for storing onnx model
        Returns:
            None
        """
        class VAEEncoderWrapper(torch.nn.Module):
            def __init__(self, vae):
                super().__init__()
                self.vae = vae
    
            def forward(self, image):
                h = self.vae.encoder(image)
                moments = self.vae.quant_conv(h)
                return moments
    
        if not onnx_path.exists():
            vae_encoder = VAEEncoderWrapper(vae)
            vae_encoder.eval()
            image = torch.zeros((1, 3, 512, 512))
            with torch.no_grad():
                torch.onnx.export(vae_encoder, image, onnx_path, input_names=[
                                  'init_image'], output_names=['image_latent'])
            print('VAE encoder successfully converted to ONNX')
    
    
    if not VAE_ENCODER_OV_PATH.exists():
        convert_vae_encoder_onnx(vae, VAE_ENCODER_ONNX_PATH)
        !mo --input_model $VAE_ENCODER_ONNX_PATH --compress_to_fp16
        print('VAE encoder successfully converted to IR')
    else:
        print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH}")
    
    VAE_DECODER_ONNX_PATH = Path('vae_decoder.onnx')
    VAE_DECODER_OV_PATH = VAE_DECODER_ONNX_PATH.with_suffix('.xml')
    
    
    def convert_vae_decoder_onnx(vae: StableDiffusionPipeline, onnx_path: Path):
        """
        Convert VAE model to ONNX, then IR format. 
        Function accepts pipeline, creates wrapper class for export only necessary for inference part, 
        prepares example inputs for ONNX conversion via torch.export, 
        Parameters: 
            pipe (StableDiffusionInstructPix2PixPipeline): InstrcutPix2Pix pipeline
            onnx_path (Path): File for storing onnx model
        Returns:
            None
        """
        class VAEDecoderWrapper(torch.nn.Module):
            def __init__(self, vae):
                super().__init__()
                self.vae = vae
    
            def forward(self, latents):
                latents = 1 / 0.18215 * latents 
                return self.vae.decode(latents)
    
        if not onnx_path.exists():
            vae_decoder = VAEDecoderWrapper(vae)
            latents = torch.zeros((1, 4, 64, 64))
    
            vae_decoder.eval()
            with torch.no_grad():
                torch.onnx.export(vae_decoder, latents, onnx_path, input_names=[
                                  'latents'], output_names=['sample'])
            print('VAE decoder successfully converted to ONNX')
    
    
    if not VAE_DECODER_OV_PATH.exists():
        convert_vae_decoder_onnx(vae, VAE_DECODER_ONNX_PATH)
        !mo --input_model $VAE_DECODER_ONNX_PATH --compress_to_fp16
        print('VAE decoder successfully converted to IR')
    else:
        print(f"VAE decoder will be loaded from {VAE_DECODER_OV_PATH}")
    
    del vae


.. parsed-literal::

    VAE encoder will be loaded from vae_encoder.xml
    VAE decoder will be loaded from vae_decoder.xml


Prepare Inference Pipeline `⇑ <#top>`__
###############################################################################################################################


Putting it all together, let us now take a closer look at how the model
works in inference by illustrating the logical flow.

.. figure:: https://user-images.githubusercontent.com/29454499/216378932-7a9be39f-cc86-43e4-b072-66372a35d6bd.png
   :alt: sd-pipeline

   sd-pipeline

As you can see from the diagram, the only difference between
Text-to-Image and text-guided Image-to-Image generation in approach is
how initial latent state is generated. In case of Image-to-Image
generation, you additionally have an image encoded by VAE encoder mixed
with the noise produced by using latent seed, while in Text-to-Image you
use only noise as initial latent state. The stable diffusion model takes
both a latent image representation of size :math:`64 \times 64` and a
text prompt is transformed to text embeddings of size
:math:`77 \times 768` via CLIP’s text encoder as an input.

Next, the U-Net iteratively *denoises* the random latent image
representations while being conditioned on the text embeddings. The
output of the U-Net, being the noise residual, is used to compute a
denoised latent image representation via a scheduler algorithm. Many
different scheduler algorithms can be used for this computation, each
having its pros and cons. For Stable Diffusion, it is recommended to use
one of:

-  `PNDM
   scheduler <https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py>`__
-  `DDIM
   scheduler <https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py>`__
-  `K-LMS
   scheduler <https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_lms_discrete.py>`__\ (you
   will use it in your pipeline)

Theory on how the scheduler algorithm function works is out of scope for
this notebook. Nonetheless, in short, you should remember that you
compute the predicted denoised image representation from the previous
noise representation and the predicted noise residual. For more
information, refer to the recommended `Elucidating the Design Space of
Diffusion-Based Generative Models <https://arxiv.org/abs/2206.00364>`__

The *denoising* process is repeated given number of times (by default
50) to step-by-step retrieve better latent image representations. When
complete, the latent image representation is decoded by the decoder part
of the variational auto encoder.

.. code:: ipython3

    import inspect
    from typing import List, Optional, Union, Dict
    
    import PIL
    import cv2
    
    from transformers import CLIPTokenizer
    from diffusers.pipeline_utils import DiffusionPipeline
    from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
    from openvino.runtime import Model
    
    
    def scale_fit_to_window(dst_width:int, dst_height:int, image_width:int, image_height:int):
        """
        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio 
        and fitting image to specific window size
        
        Parameters:
          dst_width (int): destination window width
          dst_height (int): destination window height
          image_width (int): source image width
          image_height (int): source image height
        Returns:
          result_width (int): calculated width for resize
          result_height (int): calculated height for resize
        """
        im_scale = min(dst_height / image_height, dst_width / image_width)
        return int(im_scale * image_width), int(im_scale * image_height)
    
    
    def preprocess(image: PIL.Image.Image):
        """
        Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
        then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
        converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
        The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
        
        Parameters:
          image (PIL.Image.Image): input image
        Returns:
           image (np.ndarray): preprocessed image tensor
           meta (Dict): dictionary with preprocessing metadata info
        """
        src_width, src_height = image.size
        dst_width, dst_height = scale_fit_to_window(
            512, 512, src_width, src_height)
        image = np.array(image.resize((dst_width, dst_height),
                         resample=PIL.Image.Resampling.LANCZOS))[None, :]
        pad_width = 512 - dst_width
        pad_height = 512 - dst_height
        pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
        image = np.pad(image, pad, mode="constant")
        image = image.astype(np.float32) / 255.0
        image = 2.0 * image - 1.0
        image = image.transpose(0, 3, 1, 2)
        return image, {"padding": pad, "src_width": src_width, "src_height": src_height}
    
    
    class OVStableDiffusionPipeline(DiffusionPipeline):
        def __init__(
            self,
            vae_decoder: Model,
            text_encoder: Model,
            tokenizer: CLIPTokenizer,
            unet: Model,
            scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
            vae_encoder: Model = None,
        ):
            """
            Pipeline for text-to-image generation using Stable Diffusion.
            Parameters:
                vae (Model):
                    Variational Auto-Encoder (VAE) Model to decode images to and from latent representations.
                text_encoder (Model):
                    Frozen text-encoder. Stable Diffusion uses the text portion of
                    [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
                    the clip-vit-large-patch14(https://huggingface.co/openai/clip-vit-large-patch14) variant.
                tokenizer (CLIPTokenizer):
                    Tokenizer of class CLIPTokenizer(https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
                unet (Model): Conditional U-Net architecture to denoise the encoded image latents.
                scheduler (SchedulerMixin):
                    A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of
                    DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
            """
            super().__init__()
            self.scheduler = scheduler
            self.vae_decoder = vae_decoder
            self.vae_encoder = vae_encoder
            self.text_encoder = text_encoder
            self.unet = unet
            self._text_encoder_output = text_encoder.output(0)
            self._unet_output = unet.output(0)
            self._vae_d_output = vae_decoder.output(0)
            self._vae_e_output = vae_encoder.output(0) if vae_encoder is not None else None
            self.height = self.unet.input(0).shape[2] * 8
            self.width = self.unet.input(0).shape[3] * 8
            self.tokenizer = tokenizer
    
        def __call__(
            self,
            prompt: Union[str, List[str]],
            image: PIL.Image.Image = None,
            num_inference_steps: Optional[int] = 50,
            guidance_scale: Optional[float] = 7.5,
            eta: Optional[float] = 0.0,
            output_type: Optional[str] = "pil",
            seed: Optional[int] = None,
            strength: float = 1.0,
            gif: Optional[bool] = False,
            **kwargs,
        ):
            """
            Function invoked when calling the pipeline for generation.
            Parameters:
                prompt (str or List[str]):
                    The prompt or prompts to guide the image generation.
                image (PIL.Image.Image, *optional*, None):
                     Intinal image for generation.
                num_inference_steps (int, *optional*, defaults to 50):
                    The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                    expense of slower inference.
                guidance_scale (float, *optional*, defaults to 7.5):
                    Guidance scale as defined in Classifier-Free Diffusion Guidance(https://arxiv.org/abs/2207.12598).
                    guidance_scale is defined as `w` of equation 2.
                    Higher guidance scale encourages to generate images that are closely linked to the text prompt,
                    usually at the expense of lower image quality.
                eta (float, *optional*, defaults to 0.0):
                    Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                    [DDIMScheduler], will be ignored for others.
                output_type (`str`, *optional*, defaults to "pil"):
                    The output format of the generate image. Choose between
                    [PIL](https://pillow.readthedocs.io/en/stable/): PIL.Image.Image or np.array.
                seed (int, *optional*, None):
                    Seed for random generator state initialization.
                gif (bool, *optional*, False):
                    Flag for storing all steps results or not.
            Returns:
                Dictionary with keys: 
                    sample - the last generated image PIL.Image.Image or np.array
                    iterations - *optional* (if gif=True) images for all diffusion steps, List of PIL.Image.Image or np.array.
            """
            if seed is not None:
                np.random.seed(seed)
    
            if isinstance(prompt, str):
                batch_size = 1
            elif isinstance(prompt, list):
                batch_size = len(prompt)
            else:
                raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
    
            img_buffer = []
            # get prompt text embeddings
            text_input = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="np",
            )
            text_embeddings = self.text_encoder(text_input.input_ids)[self._text_encoder_output]
            # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
            # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
            # corresponds to doing no classifier free guidance.
            do_classifier_free_guidance = guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                max_length = text_input.input_ids.shape[-1]
                uncond_input = self.tokenizer(
                    [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
                )
                uncond_embeddings = self.text_encoder(uncond_input.input_ids)[self._text_encoder_output]
    
                # For classifier free guidance, you need to do two forward passes.
                # Here you concatenate the unconditional and text embeddings into a single batch
                # to avoid doing two forward passes
                text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
            
            # set timesteps
            accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
            extra_set_kwargs = {}
            if accepts_offset:
                extra_set_kwargs["offset"] = 1
    
            self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
            timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
            latent_timestep = timesteps[:1]
    
            # get the initial random noise unless the user supplied it
            latents, meta = self.prepare_latents(image, latent_timestep)
    
            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
            extra_step_kwargs = {}
            if accepts_eta:
                extra_step_kwargs["eta"] = eta
    
            for i, t in enumerate(self.progress_bar(timesteps)):
                # expand the latents if you are doing classifier free guidance
                latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
    
                # predict the noise residual
                noise_pred = self.unet([latent_model_input, t, text_embeddings])[self._unet_output]
                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
    
                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs)["prev_sample"].numpy()
                if gif:
                    image = self.vae_decoder(latents)[self._vae_d_output]
                    image = self.postprocess_image(image, meta, output_type)
                    img_buffer.extend(image)
    
            # scale and decode the image latents with vae
            image = self.vae_decoder(latents)[self._vae_d_output]
    
            image = self.postprocess_image(image, meta, output_type)
            return {"sample": image, 'iterations': img_buffer}
    
        def prepare_latents(self, image:PIL.Image.Image = None, latent_timestep:torch.Tensor = None):
            """
            Function for getting initial latents for starting generation
            
            Parameters:
                image (PIL.Image.Image, *optional*, None):
                    Input image for generation, if not provided randon noise will be used as starting point
                latent_timestep (torch.Tensor, *optional*, None):
                    Predicted by scheduler initial step for image generation, required for latent image mixing with nosie
            Returns:
                latents (np.ndarray):
                    Image encoded in latent space
            """
            latents_shape = (1, 4, self.height // 8, self.width // 8)
            noise = np.random.randn(*latents_shape).astype(np.float32)
            if image is None:
                # if you use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
                if isinstance(self.scheduler, LMSDiscreteScheduler):
                    noise = noise * self.scheduler.sigmas[0].numpy()
                    return noise, {}
            input_image, meta = preprocess(image)
            moments = self.vae_encoder(input_image)[self._vae_e_output]
            mean, logvar = np.split(moments, 2, axis=1) 
            std = np.exp(logvar * 0.5)
            latents = (mean + std * np.random.randn(*mean.shape)) * 0.18215
            latents = self.scheduler.add_noise(torch.from_numpy(latents), torch.from_numpy(noise), latent_timestep).numpy()
            return latents, meta
    
        def postprocess_image(self, image:np.ndarray, meta:Dict, output_type:str = "pil"):
            """
            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required), 
            normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
            
            Parameters:
                image (np.ndarray):
                    Generated image
                meta (Dict):
                    Metadata obtained on latents preparing step, can be empty
                output_type (str, *optional*, pil):
                    Output format for result, can be pil or numpy
            Returns:
                image (List of np.ndarray or PIL.Image.Image):
                    Postprocessed images
            """
            if "padding" in meta:
                pad = meta["padding"]
                (_, end_h), (_, end_w) = pad[1:3]
                h, w = image.shape[2:]
                unpad_h = h - end_h
                unpad_w = w - end_w
                image = image[:, :, :unpad_h, :unpad_w]
            image = np.clip(image / 2 + 0.5, 0, 1)
            image = np.transpose(image, (0, 2, 3, 1))
            # 9. Convert to PIL
            if output_type == "pil":
                image = self.numpy_to_pil(image)
                if "src_height" in meta:
                    orig_height, orig_width = meta["src_height"], meta["src_width"]
                    image = [img.resize((orig_width, orig_height),
                                        PIL.Image.Resampling.LANCZOS) for img in image]
            else:
                if "src_height" in meta:
                    orig_height, orig_width = meta["src_height"], meta["src_width"]
                    image = [cv2.resize(img, (orig_width, orig_width))
                             for img in image]
            return image
    
        def get_timesteps(self, num_inference_steps:int, strength:float):
            """
            Helper function for getting scheduler timesteps for generation
            In case of image-to-image generation, it updates number of steps according to strength
            
            Parameters:
               num_inference_steps (int):
                  number of inference steps for generation
               strength (float):
                   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.
            """
            # get the original timestep using init_timestep
            init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
    
            t_start = max(num_inference_steps - init_timestep, 0)
            timesteps = self.scheduler.timesteps[t_start:]
    
            return timesteps, num_inference_steps - t_start 

Configure Inference Pipeline `⇑ <#top>`__
###############################################################################################################################


First, you should create instances of OpenVINO Model.

.. code:: ipython3

    from openvino.runtime import Core
    core = Core()

Select device from dropdown list for running inference using OpenVINO.

.. code:: ipython3

    import ipywidgets as widgets
    
    device = widgets.Dropdown(
        options=core.available_devices + ["AUTO"],
        value='AUTO',
        description='Device:',
        disabled=False,
    )
    
    device

.. code:: ipython3

    
    text_enc = core.compile_model(TEXT_ENCODER_OV_PATH, device.value)

.. code:: ipython3

    unet_model = core.compile_model(UNET_OV_PATH, device.value)

.. code:: ipython3

    vae_decoder = core.compile_model(VAE_DECODER_OV_PATH, device.value)
    vae_encoder = core.compile_model(VAE_ENCODER_OV_PATH, device.value)

Model tokenizer and scheduler are also important parts of the pipeline.
Let us define them and put all components together

.. code:: ipython3

    from transformers import CLIPTokenizer
    from diffusers.schedulers import LMSDiscreteScheduler
    
    lms = LMSDiscreteScheduler(
        beta_start=0.00085, 
        beta_end=0.012, 
        beta_schedule="scaled_linear"
    )
    tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')
    
    ov_pipe = OVStableDiffusionPipeline(
        tokenizer=tokenizer,
        text_encoder=text_enc,
        unet=unet_model,
        vae_encoder=vae_encoder,
        vae_decoder=vae_decoder,
        scheduler=lms
    )

Text-to-Image generation `⇑ <#top>`__
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


Now, you can define a text prompt for image generation and run inference
pipeline. Optionally, you can also change the random generator seed for
latent state initialization and number of steps.

   **Note**: Consider increasing ``steps`` to get more precise results.
   A suggested value is ``50``, but it will take longer time to process.

.. code:: ipython3

    import ipywidgets as widgets
    
    text_prompt = widgets.Text(value='cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour cinematic lighting, epic composition. A golden daylight, hyper-realistic environment. Hyper and intricate detail, photo-realistic. Cinematic and volumetric light. Epic concept art. Octane render and Unreal Engine, trending on artstation', description='your text')
    num_steps = widgets.IntSlider(min=1, max=50, value=20, description='steps:')
    seed = widgets.IntSlider(min=0, max=10000000, description='seed: ', value=42)
    widgets.VBox([text_prompt, seed, num_steps])




.. parsed-literal::

    VBox(children=(Text(value='cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour ci…



.. code:: ipython3

    print('Pipeline settings')
    print(f'Input text: {text_prompt.value}')
    print(f'Seed: {seed.value}')
    print(f'Number of steps: {num_steps.value}')


.. parsed-literal::

    Pipeline settings
    Input text: cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour cinematic lighting, epic composition. A golden daylight, hyper-realistic environment. Hyper and intricate detail, photo-realistic. Cinematic and volumetric light. Epic concept art. Octane render and Unreal Engine, trending on artstation
    Seed: 42
    Number of steps: 20


.. code:: ipython3

    result = ov_pipe(text_prompt.value, num_inference_steps=num_steps.value, seed=seed.value)



.. parsed-literal::

      0%|          | 0/20 [00:00<?, ?it/s]


Finally, let us save generation results. The pipeline returns several
results: ``sample`` contains final generated image, ``iterations``
contains list of intermediate results for each step.

.. code:: ipython3

    final_image = result['sample'][0]
    if result['iterations']:
        all_frames = result['iterations']
        img = next(iter(all_frames))
        img.save(fp='result.gif', format='GIF', append_images=iter(all_frames), save_all=True, duration=len(all_frames) * 5, loop=0)
    final_image.save('result.png')

Now is show time!

.. code:: ipython3

    import ipywidgets as widgets
    
    text = '\n\t'.join(text_prompt.value.split('.'))
    print("Input text:")
    print("\t" + text)
    display(final_image)


.. parsed-literal::

    Input text:
    	cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour cinematic lighting, epic composition
    	 A golden daylight, hyper-realistic environment
    	 Hyper and intricate detail, photo-realistic
    	 Cinematic and volumetric light
    	 Epic concept art
    	 Octane render and Unreal Engine, trending on artstation



.. image:: 225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_33_1.png


Nice. As you can see, the picture has quite a high definition 🔥.

Image-to-Image generation `⇑ <#top>`__
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


Image-to-Image generation, additionally to text prompt, requires
providing initial image. Optionally, you can also change ``strength``
parameter, which 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.

.. code:: ipython3

    text_prompt_i2i = widgets.Text(value='amazing watercolor painting', description='your text')
    num_steps_i2i = widgets.IntSlider(min=1, max=50, value=10, description='steps:')
    seed_i2i = widgets.IntSlider(min=0, max=1024, description='seed: ', value=42)
    image_widget = widgets.FileUpload(
        accept='',
        multiple=False,
        description='Upload image',
    )
    strength = widgets.FloatSlider(min=0, max=1, description='strength: ', value=0.5)
    widgets.VBox([text_prompt_i2i, seed_i2i, num_steps_i2i, image_widget, strength])




.. parsed-literal::

    VBox(children=(Text(value='amazing watercolor painting', description='your text'), IntSlider(value=42, descrip…



.. code:: ipython3

    import io
    
    default_image_path = "../data/image/coco.jpg"
    # read uploaded image
    image = PIL.Image.open(io.BytesIO(image_widget.value[-1]['content']) if image_widget.value else default_image_path)
    print('Pipeline settings')
    print(f'Input text: {text_prompt_i2i.value}')
    print(f'Seed: {seed_i2i.value}')
    print(f'Number of steps: {num_steps_i2i.value}')
    print(f'Strength: {strength.value}')
    print("Input image:")
    display(image)
    processed_image = ov_pipe(text_prompt_i2i.value, image, num_inference_steps=num_steps_i2i.value, seed=seed_i2i.value, strength=strength.value)


.. parsed-literal::

    Pipeline settings
    Input text: amazing watercolor painting
    Seed: 42
    Number of steps: 10
    Strength: 0.5
    Input image:



.. image:: 225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_37_1.png



.. parsed-literal::

      0%|          | 0/5 [00:00<?, ?it/s]


.. code:: ipython3

    final_image_i2i = processed_image['sample'][0]
    if processed_image['iterations']:
        all_frames_i2i = processed_image['iterations']
        img = next(iter(all_frames_i2i))
        img.save(fp='result_i2i.gif', format='GIF', append_images=iter(all_frames_i2i), save_all=True, duration=len(all_frames_i2i) * 5, loop=0)
    final_image_i2i.save('result_i2i.png')

.. code:: ipython3

    text_i2i = '\n\t'.join(text_prompt_i2i.value.split('.'))
    print("Input text:")
    print("\t" + text_i2i)
    display(final_image_i2i)


.. parsed-literal::

    Input text:
    	amazing watercolor painting



.. image:: 225-stable-diffusion-text-to-image-with-output_files/225-stable-diffusion-text-to-image-with-output_39_1.png