openvino/docs/notebooks/236-stable-diffusion-v2-infinite-zoom-with-output.rst

Infinite Zoom Stable Diffusion v2 and OpenVINO™
===============================================



Stable Diffusion v2 is the next generation of Stable Diffusion model a
Text-to-Image latent diffusion model created by the researchers and
engineers from `Stability AI <https://stability.ai/>`__ and
`LAION <https://laion.ai/>`__.

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!

In previous notebooks, we already discussed how to run `Text-to-Image
generation and Image-to-Image generation using Stable Diffusion
v1 <225-stable-diffusion-text-to-image-with-output.html>`__
and `controlling its generation process using
ControlNet <./235-controlnet-stable-diffusion/235-controlnet-stable-diffusion.ipynb>`__.
Now is turn of Stable Diffusion v2.

Stable Diffusion v2: What’s new?
--------------------------------

The new stable diffusion model offers a bunch of new features inspired
by the other models that have emerged since the introduction of the
first iteration. Some of the features that can be found in the new model
are:

-  The model comes with a new robust encoder, OpenCLIP, created by LAION
   and aided by Stability AI; this version v2 significantly enhances the
   produced photos over the V1 versions.
-  The model can now generate images in a 768x768 resolution, offering
   more information to be shown in the generated images.
-  The model finetuned with
   `v-objective <https://arxiv.org/abs/2202.00512>`__. The
   v-parameterization is particularly useful for numerical stability
   throughout the diffusion process to enable progressive distillation
   for models. For models that operate at higher resolution, it is also
   discovered that the v-parameterization avoids color shifting
   artifacts that are known to affect high resolution diffusion models,
   and in the video setting it avoids temporal color shifting that
   sometimes appears with epsilon-prediction used in Stable Diffusion
   v1.
-  The model also comes with a new diffusion model capable of running
   upscaling on the images generated. Upscaled images can be adjusted up
   to 4 times the original image. Provided as separated model, for more
   details please check
   `stable-diffusion-x4-upscaler <https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler>`__
-  The model comes with a new refined depth architecture capable of
   preserving context from prior generation layers in an image-to-image
   setting. This structure preservation helps generate images that
   preserving forms and shadow of objects, but with different content.
-  The model comes with an updated inpainting module built upon the
   previous model. This text-guided inpainting makes switching out parts
   in the image easier than before.

This notebook demonstrates how to convert and run Stable Diffusion v2
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 v2 inpainting pipeline for generation infinity
   zoom video

.. _top:

**Table of contents**:

- `Stable Diffusion v2 Infinite Zoom Showcase <#stable-diffusion-v2-infinite-zoom-showcase>`__

  - `Stable Diffusion Text guided Inpainting <#stable-diffusion-text-guided-inpainting>`__

- `Prerequisites <#prerequisites>`__

  - `Stable Diffusion in Diffusers library <#stable-diffusion-in-diffusers-library>`__
  - `Convert models to OpenVINO Intermediate representation (IR) format <#convert-models-to-openvino-intermediate-representation-ir-format>`__
  - `Prepare Inference pipeline <#prepare-inference-pipeline>`__
  - `Zoom Video Generation <#zoom-video-generation>`__
  - `Configure Inference Pipeline <#configure-inference-pipeline>`__
  - `Select inference device <#select-inference-device>`__
  - `Run Infinite Zoom video generation <#run-infinite-zoom-video-generation>`__

Stable Diffusion v2 Infinite Zoom Showcase `⇑ <#top>`__
###############################################################################################################################


In this tutorial we consider how to use Stable Diffusion v2 model for
generation sequence of images for infinite zoom video effect. To do
this, we will need
`stabilityai/stable-diffusion-2-inpainting <https://huggingface.co/stabilityai/stable-diffusion-2-inpainting>`__
model.

Stable Diffusion Text guided Inpainting `⇑ <#top>`__
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

In image editing, inpainting is a process of restoring missing parts of pictures. Most
commonly applied to reconstructing old deteriorated images, removing
cracks, scratches, dust spots, or red-eyes from photographs.

But with the power of AI and the Stable Diffusion model, inpainting can
be used to achieve more than that. For example, instead of just
restoring missing parts of an image, it can be used to render something
entirely new in any part of an existing picture. Only your imagination
limits it.

The workflow diagram explains how Stable Diffusion inpainting pipeline
for inpainting works:

.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/22090501/9ac6de45-186f-4a3c-aa20-825825a337eb
   :alt: sd2-inpainting

   sd2-inpainting

The pipeline has a lot of common with Text-to-Image generation pipeline
discussed in previous section. Additionally to text prompt, pipeline
accepts input source image and mask which provides an area of image
which should be modified. Masked image encoded by VAE encoder into
latent diffusion space and concatenated with randomly generated (on
initial step only) or produced by U-Net latent generated image
representation and used as input for next step denoising.

Using this inpainting feature, decreasing image by certain margin and
masking this border for every new frame we can create interesting Zoom
Out video based on our prompt.

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

Install required packages:

.. code:: ipython3

    !pip install -q "diffusers>=0.14.0" openvino-dev "transformers >= 4.25.1" gradio


.. parsed-literal::

    
    [notice] A new release of pip is available: 23.1.2 -> 23.2
    [notice] To update, run: pip install --upgrade pip


Stable Diffusion in Diffusers library `⇑ <#top>`__
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

To work with Stable Diffusion v2, we will use Hugging Face
`Diffusers <https://github.com/huggingface/diffusers>`__ library. To
experiment with Stable Diffusion models for Inpainting use case,
Diffusers exposes the
`StableDiffusionInpaintPipeline <https://huggingface.co/docs/diffusers/using-diffusers/conditional_image_generation>`__
similar to the `other Diffusers
pipelines <https://huggingface.co/docs/diffusers/api/pipelines/overview>`__.
The code below demonstrates how to create
``StableDiffusionInpaintPipeline`` using
``stable-diffusion-2-inpainting``:

.. code:: ipython3

    from diffusers import StableDiffusionInpaintPipeline, DPMSolverMultistepScheduler
    
    model_id_inpaint = "stabilityai/stable-diffusion-2-inpainting"
    
    pipe_inpaint = StableDiffusionInpaintPipeline.from_pretrained(model_id_inpaint)
    scheduler_inpaint = DPMSolverMultistepScheduler.from_config(pipe_inpaint.scheduler.config)


.. parsed-literal::

    2023-07-16 15:45:16.540634: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
    2023-07-16 15:45:16.577870: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
    2023-07-16 15:45:17.175991: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT



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.. code:: ipython3

    import gc
    
    text_encoder_inpaint = pipe_inpaint.text_encoder
    text_encoder_inpaint.eval()
    unet_inpaint = pipe_inpaint.unet
    unet_inpaint.eval()
    vae_inpaint = pipe_inpaint.vae
    vae_inpaint.eval()
    
    del pipe_inpaint
    gc.collect();

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

Conversion part of model stayed remain as in `Text-to-Image generation
notebook <./236-stable-diffusion-v2-text-to-image.ipynb>`__. Except
U-Net now has 9 channels, which now calculated like 4 for U-Net
generated latents channels + 4 for latent representation of masked image
+ 1 channel resized mask.

.. code:: ipython3

    from pathlib import Path
    import torch
    import numpy as np
    
    sd2_inpainting_model_dir = Path("sd2_inpainting")
    sd2_inpainting_model_dir.mkdir(exist_ok=True)

.. code:: ipython3

    def convert_encoder_onnx(text_encoder: torch.nn.Module, onnx_path:Path):
        """
        Convert Text Encoder model to ONNX. 
        Function accepts pipeline, prepares example inputs for ONNX conversion via torch.export, 
        Parameters: 
            text_encoder (torch.nn.Module): text encoder PyTorch model
            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():
                # 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,
                    onnx_shape_inference=False
                )
            print('Text Encoder successfully converted to ONNX')
    
            
    def convert_unet_onnx(unet:torch.nn.Module, onnx_path:Path, num_channels:int = 4, width:int = 64, height:int = 64):
        """
        Convert Unet model to ONNX, then IR format. 
        Function accepts pipeline, prepares example inputs for ONNX conversion via torch.export, 
        Parameters: 
            unet (torch.nn.Module): UNet PyTorch model
            onnx_path (Path): File for storing onnx model
            num_channels (int, optional, 4): number of input channels
            width (int, optional, 64): input width
            height (int, optional, 64): input height
        Returns:
            None
        """
        if not onnx_path.exists():
            # prepare inputs
            encoder_hidden_state = torch.ones((2, 77, 1024))
            latents_shape = (2, num_channels, width, height)
            latents = torch.randn(latents_shape)
            t = torch.from_numpy(np.array(1, dtype=np.float32))
    
            # model size > 2Gb, it will be represented as onnx with external data files, we 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'],
                    onnx_shape_inference=False
                )
            print('U-Net successfully converted to ONNX')
    
    
    def convert_vae_encoder_onnx(vae: torch.nn.Module, onnx_path: Path, width:int = 512, height:int = 512):
        """
        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: 
            vae (torch.nn.Module): VAE PyTorch model
            onnx_path (Path): File for storing onnx model
            width (int, optional, 512): input width
            height (int, optional, 512): input height
        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, width, height))
            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')
    
    
    def convert_vae_decoder_onnx(vae: torch.nn.Module, onnx_path: Path, width:int = 64, height:int = 64):
        """
        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: 
            vae: 
            onnx_path (Path): File for storing onnx model
            width (int, optional, 64): input width
            height (int, optional, 64): input height
        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, width, height))
    
            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')

.. code:: ipython3

    TEXT_ENCODER_ONNX_PATH_INPAINT = sd2_inpainting_model_dir / "text_encoder.onnx"
    TEXT_ENCODER_OV_PATH_INPAINT = TEXT_ENCODER_ONNX_PATH_INPAINT.with_suffix('.xml')
    
    if not TEXT_ENCODER_OV_PATH_INPAINT.exists():
        convert_encoder_onnx(text_encoder_inpaint, TEXT_ENCODER_ONNX_PATH_INPAINT)
        !mo --input_model $TEXT_ENCODER_ONNX_PATH_INPAINT --output_dir $sd2_inpainting_model_dir
        print('Text Encoder successfully converted to IR')
    else:
        print(f"Text encoder will be loaded from {TEXT_ENCODER_OV_PATH_INPAINT}")
    
    del text_encoder_inpaint
    gc.collect();


.. parsed-literal::

    /tmp/ipykernel_1181138/3505677505.py:19: FutureWarning: 'torch.onnx._export' is deprecated in version 1.12.0 and will be removed in version 1.14. Please use `torch.onnx.export` instead.
      torch.onnx._export(
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:684: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
      mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:284: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
      if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:292: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
      if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:324: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
      if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/torch/onnx/symbolic_helper.py:710: UserWarning: Type cannot be inferred, which might cause exported graph to produce incorrect results.
      warnings.warn(
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/torch/onnx/symbolic_opset9.py:5408: UserWarning: Exporting aten::index operator of advanced indexing in opset 14 is achieved by combination of multiple ONNX operators, including Reshape, Transpose, Concat, and Gather. If indices include negative values, the exported graph will produce incorrect results.
      warnings.warn(


.. parsed-literal::

    Text Encoder successfully converted to ONNX
    [ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
    Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.1/openvino_2_0_transition_guide.html
    [ SUCCESS ] Generated IR version 11 model.
    [ SUCCESS ] XML file: /home/ea/work/openvino_notebooks/notebooks/236-stable-diffusion-v2/sd2_inpainting/text_encoder.xml
    [ SUCCESS ] BIN file: /home/ea/work/openvino_notebooks/notebooks/236-stable-diffusion-v2/sd2_inpainting/text_encoder.bin
    Text Encoder successfully converted to IR


.. code:: ipython3

    UNET_ONNX_PATH_INPAINT = sd2_inpainting_model_dir / 'unet/unet.onnx'
    UNET_OV_PATH_INPAINT = UNET_ONNX_PATH_INPAINT.parents[1] / 'unet.xml'
    if not UNET_OV_PATH_INPAINT.exists():
        convert_unet_onnx(unet_inpaint, UNET_ONNX_PATH_INPAINT, num_channels=9, width=64, height=64)
        del unet_inpaint
        gc.collect()
        !mo --input_model $UNET_ONNX_PATH_INPAINT --output_dir $sd2_inpainting_model_dir
        print('U-Net successfully converted to IR')
    else:
        del unet_inpaint
        print(f"U-Net will be loaded from {UNET_OV_PATH_INPAINT}")
    gc.collect();


.. parsed-literal::

    /tmp/ipykernel_1181138/3505677505.py:56: FutureWarning: 'torch.onnx._export' is deprecated in version 1.12.0 and will be removed in version 1.14. Please use `torch.onnx.export` instead.
      torch.onnx._export(
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/diffusers/models/unet_2d_condition.py:752: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
      if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/diffusers/models/resnet.py:214: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
      assert hidden_states.shape[1] == self.channels
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/diffusers/models/resnet.py:219: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
      assert hidden_states.shape[1] == self.channels
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/diffusers/models/resnet.py:138: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
      assert hidden_states.shape[1] == self.channels
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/diffusers/models/resnet.py:151: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
      if hidden_states.shape[0] >= 64:
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/diffusers/models/unet_2d_condition.py:977: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
      if not return_dict:


.. parsed-literal::

    U-Net successfully converted to ONNX
    [ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
    Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.1/openvino_2_0_transition_guide.html
    [ SUCCESS ] Generated IR version 11 model.
    [ SUCCESS ] XML file: /home/ea/work/openvino_notebooks/notebooks/236-stable-diffusion-v2/sd2_inpainting/unet.xml
    [ SUCCESS ] BIN file: /home/ea/work/openvino_notebooks/notebooks/236-stable-diffusion-v2/sd2_inpainting/unet.bin
    U-Net successfully converted to IR


.. code:: ipython3

    VAE_ENCODER_ONNX_PATH_INPAINT = sd2_inpainting_model_dir / 'vae_encoder.onnx'
    VAE_ENCODER_OV_PATH_INPAINT = VAE_ENCODER_ONNX_PATH_INPAINT.with_suffix('.xml')
    
    if not VAE_ENCODER_OV_PATH_INPAINT.exists():
        convert_vae_encoder_onnx(vae_inpaint, VAE_ENCODER_ONNX_PATH_INPAINT, 512, 512)
        !mo --input_model $VAE_ENCODER_ONNX_PATH_INPAINT --output_dir $sd2_inpainting_model_dir
        print('VAE encoder successfully converted to IR')
    else:
        print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH_INPAINT}")
    
    VAE_DECODER_ONNX_PATH_INPAINT = sd2_inpainting_model_dir / 'vae_decoder.onnx'
    VAE_DECODER_OV_PATH_INPAINT = VAE_DECODER_ONNX_PATH_INPAINT.with_suffix('.xml')
    if not VAE_DECODER_OV_PATH_INPAINT.exists():
        convert_vae_decoder_onnx(vae_inpaint, VAE_DECODER_ONNX_PATH_INPAINT, 64, 64)
        !mo --input_model $VAE_DECODER_ONNX_PATH_INPAINT --output_dir $sd2_inpainting_model_dir
        print('VAE decoder successfully converted to IR')
    else:
        print(f"VAE decoder will be loaded from {VAE_DECODER_OV_PATH_INPAINT}")
    
    del vae_inpaint
    gc.collect();


.. parsed-literal::

    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/torch/onnx/_internal/jit_utils.py:258: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
      _C._jit_pass_onnx_node_shape_type_inference(node, params_dict, opset_version)
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/torch/onnx/utils.py:687: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
      _C._jit_pass_onnx_graph_shape_type_inference(
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/torch/onnx/utils.py:1178: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)
      _C._jit_pass_onnx_graph_shape_type_inference(


.. parsed-literal::

    VAE encoder successfully converted to ONNX
    [ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
    Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.1/openvino_2_0_transition_guide.html
    [ SUCCESS ] Generated IR version 11 model.
    [ SUCCESS ] XML file: /home/ea/work/openvino_notebooks/notebooks/236-stable-diffusion-v2/sd2_inpainting/vae_encoder.xml
    [ SUCCESS ] BIN file: /home/ea/work/openvino_notebooks/notebooks/236-stable-diffusion-v2/sd2_inpainting/vae_encoder.bin
    VAE encoder successfully converted to IR


.. parsed-literal::

    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/torch/onnx/_internal/jit_utils.py:258: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)
      _C._jit_pass_onnx_node_shape_type_inference(node, params_dict, opset_version)
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/torch/onnx/utils.py:687: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)
      _C._jit_pass_onnx_graph_shape_type_inference(
    /home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/torch/onnx/utils.py:1178: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)
      _C._jit_pass_onnx_graph_shape_type_inference(


.. parsed-literal::

    VAE decoder successfully converted to ONNX
    [ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
    Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.1/openvino_2_0_transition_guide.html
    [ SUCCESS ] Generated IR version 11 model.
    [ SUCCESS ] XML file: /home/ea/work/openvino_notebooks/notebooks/236-stable-diffusion-v2/sd2_inpainting/vae_decoder.xml
    [ SUCCESS ] BIN file: /home/ea/work/openvino_notebooks/notebooks/236-stable-diffusion-v2/sd2_inpainting/vae_decoder.bin
    VAE decoder successfully converted to IR


Prepare Inference pipeline `⇑ <#top>`__
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


As it was discussed previously, Inpainting inference pipeline is based
on Text-to-Image inference pipeline with addition mask processing step.
We will reuse ``OVStableDiffusionPipeline`` basic utilities in
``OVStableDiffusionInpaintingPipeline`` class.

.. 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 prepare_mask_and_masked_image(image:PIL.Image.Image, mask:PIL.Image.Image):
        """
        Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
        converted to ``np.array`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
        ``image`` and ``1`` for the ``mask``.
    
        The ``image`` will be converted to ``np.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
        binarized (``mask > 0.5``) and cast to ``np.float32`` too.
    
        Args:
            image (Union[np.array, PIL.Image]): The image to inpaint.
                It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array``
            mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
                It can be a ``PIL.Image``, or a ``height x width`` ``np.array``.
    
        Returns:
            tuple[np.array]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
                dimensions: ``batch x channels x height x width``.
        """
        if isinstance(image, (PIL.Image.Image, np.ndarray)):
            image = [image]
    
        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
            image = [np.array(i.convert("RGB"))[None, :] for i in image]
            image = np.concatenate(image, axis=0)
        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
            image = np.concatenate([i[None, :] for i in image], axis=0)
    
        image = image.transpose(0, 3, 1, 2)
        image = image.astype(np.float32) / 127.5 - 1.0
    
        # preprocess mask
        if isinstance(mask, (PIL.Image.Image, np.ndarray)):
            mask = [mask]
    
        if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
            mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
            mask = mask.astype(np.float32) / 255.0
        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
    
        mask[mask < 0.5] = 0
        mask[mask >= 0.5] = 1
    
        masked_image = image * (mask < 0.5)
    
        return mask, masked_image


.. parsed-literal::

    /tmp/ipykernel_1181138/859685649.py:8: FutureWarning: Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.
      from diffusers.pipeline_utils import DiffusionPipeline


.. code:: ipython3

    class OVStableDiffusionInpaintingPipeline(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_decoder (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.
                vae_encoder (Model):
                    Variational Auto-Encoder (VAE) Model to encode images to latent representation.
                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
            self.register_to_config(_progress_bar_config={})
    
        def prepare_mask_latents(
            self,
            mask,
            masked_image,
            height=512,
            width=512,
            do_classifier_free_guidance=True,
        ):
            """
            Prepare mask as Unet nput and encode input masked image to latent space using vae encoder
    
            Parameters:
              mask (np.array): input mask array
              masked_image (np.array): masked input image tensor
              heigh (int, *optional*, 512): generated image height
              width (int, *optional*, 512): generated image width
              do_classifier_free_guidance (bool, *optional*, True): whether to use classifier free guidance or not
            Returns:
              mask (np.array): resized mask tensor
              masked_image_latents (np.array): masked image encoded into latent space using VAE
            """
            mask = torch.nn.functional.interpolate(torch.from_numpy(mask), size=(height // 8, width // 8))
            mask = mask.numpy()
    
            # encode the mask image into latents space so we can concatenate it to the latents
            moments = self.vae_encoder(masked_image)[self._vae_e_output]
            mean, logvar = np.split(moments, 2, axis=1)
            std = np.exp(logvar * 0.5)
            masked_image_latents = (mean + std * np.random.randn(*mean.shape)) * 0.18215
    
            mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask
            masked_image_latents = (
                np.concatenate([masked_image_latents] * 2)
                if do_classifier_free_guidance
                else masked_image_latents
            )
            return mask, masked_image_latents
    
        def __call__(
            self,
            prompt: Union[str, List[str]],
            image: PIL.Image.Image,
            mask_image: PIL.Image.Image,
            negative_prompt: Union[str, List[str]] = None,
            num_inference_steps: Optional[int] = 50,
            guidance_scale: Optional[float] = 7.5,
            eta: Optional[float] = 0,
            output_type: Optional[str] = "pil",
            seed: Optional[int] = None,
        ):
            """
            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):
                     Source image for inpainting.
                mask_image (PIL.Image.Image):
                     Mask area for inpainting
                negative_prompt (str or List[str]):
                    The negative prompt or prompts to guide the image 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.
            Returns:
                Dictionary with keys:
                    sample - the last generated image PIL.Image.Image or np.array
            """
            if seed is not None:
                np.random.seed(seed)
            # 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 prompt text embeddings
            text_embeddings = self._encode_prompt(
                prompt,
                do_classifier_free_guidance=do_classifier_free_guidance,
                negative_prompt=negative_prompt,
            )
            # prepare mask
            mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
            # 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, 1)
            latent_timestep = timesteps[:1]
    
            # get the initial random noise unless the user supplied it
            latents, meta = self.prepare_latents(None, latent_timestep)
            mask, masked_image_latents = self.prepare_mask_latents(
                mask,
                masked_image,
                do_classifier_free_guidance=do_classifier_free_guidance,
            )
    
            # 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 t in self.progress_bar(timesteps):
                # expand the latents if we 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)
                latent_model_input = np.concatenate(
                    [latent_model_input, mask, masked_image_latents], axis=1
                )
                # predict the noise residual
                noise_pred = self.unet(
                    [latent_model_input, np.array(t, dtype=np.float32), 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()
            # 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}
    
        def _encode_prompt(self, prompt:Union[str, List[str]], num_images_per_prompt:int = 1, do_classifier_free_guidance:bool = True, negative_prompt:Union[str, List[str]] = None):
            """
            Encodes the prompt into text encoder hidden states.
    
            Parameters:
                prompt (str or list(str)): prompt to be encoded
                num_images_per_prompt (int): number of images that should be generated per prompt
                do_classifier_free_guidance (bool): whether to use classifier free guidance or not
                negative_prompt (str or list(str)): negative prompt to be encoded
            Returns:
                text_embeddings (np.ndarray): text encoder hidden states
            """
            batch_size = len(prompt) if isinstance(prompt, list) else 1
    
            # tokenize input prompts
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="np",
            )
            text_input_ids = text_inputs.input_ids
    
            text_embeddings = self.text_encoder(
                text_input_ids)[self._text_encoder_output]
    
            # duplicate text embeddings for each generation per prompt
            if num_images_per_prompt != 1:
                bs_embed, seq_len, _ = text_embeddings.shape
                text_embeddings = np.tile(
                    text_embeddings, (1, num_images_per_prompt, 1))
                text_embeddings = np.reshape(
                    text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
    
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                uncond_tokens: List[str]
                max_length = text_input_ids.shape[-1]
                if negative_prompt is None:
                    uncond_tokens = [""] * batch_size
                elif isinstance(negative_prompt, str):
                    uncond_tokens = [negative_prompt]
                else:
                    uncond_tokens = negative_prompt
                uncond_input = self.tokenizer(
                    uncond_tokens,
                    padding="max_length",
                    max_length=max_length,
                    truncation=True,
                    return_tensors="np",
                )
    
                uncond_embeddings = self.text_encoder(uncond_input.input_ids)[self._text_encoder_output]
    
                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                seq_len = uncond_embeddings.shape[1]
                uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
                uncond_embeddings = np.reshape(uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
    
                # For classifier free guidance, we need to do two forward passes.
                # Here we concatenate the unconditional and text embeddings into a single batch
                # to avoid doing two forward passes
                text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
    
            return text_embeddings
    
        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 we use LMSDiscreteScheduler, let's make sure latents are mulitplied 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 allow for 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 

Zoom Video Generation `⇑ <#top>`__
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


For achieving zoom effect, we will use inpainting to expand images
beyond their original borders. We run our
``OVStableDiffusionInpaintingPipeline`` in the loop, where each next
frame will add edges to previous. The frame generation process
illustrated on diagram below:

.. figure:: https://user-images.githubusercontent.com/29454499/228739686-436f2759-4c79-42a2-a70f-959fb226834c.png
   :alt: frame generation)

   frame generation)

After processing current frame, we decrease size of current image by
mask size pixels from each side and use it as input for next step.
Changing size of mask we can influence the size of painting area and
image scaling.

There are 2 zooming directions:

-  Zoom Out - move away from object
-  Zoom In - move closer to object

Zoom In will be processed in the same way as Zoom Out, but after
generation is finished, we record frames in reversed order.

.. code:: ipython3

    from tqdm import trange
    def generate_video(
        pipe:OVStableDiffusionInpaintingPipeline,
        prompt:Union[str, List[str]],
        negative_prompt:Union[str, List[str]],
        guidance_scale:float = 7.5,
        num_inference_steps:int = 20,
        num_frames:int = 20,
        mask_width:int = 128,
        seed:int = 9999,
        zoom_in:bool = False,
    ):
        """
        Zoom video generation function
        
        Parameters:
          pipe (OVStableDiffusionInpaintingPipeline): inpainting pipeline.
          prompt (str or List[str]): The prompt or prompts to guide the image generation.
          negative_prompt (str or List[str]): The negative prompt or prompts to guide the image generation.
          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.
          num_inference_steps (int, *optional*, defaults to 50): The number of denoising steps for each frame. More denoising steps usually lead to a higher quality image at the expense of slower inference.
          num_frames (int, *optional*, 20): number frames for video.
          mask_width (int, *optional*, 128): size of border mask for inpainting on each step.
          seed (int, *optional*, None): Seed for random generator state initialization.
          zoom_in (bool, *optional*, False): zoom mode Zoom In or Zoom Out.
        Returns:
          output_path (str): Path where generated video loacated.
        """
    
        height = 512
        width = height
    
        current_image = PIL.Image.new(mode="RGBA", size=(height, width))
        mask_image = np.array(current_image)[:, :, 3]
        mask_image = PIL.Image.fromarray(255 - mask_image).convert("RGB")
        current_image = current_image.convert("RGB")
        pipe.set_progress_bar_config(desc='Generating initial image...')
        init_images = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            image=current_image,
            guidance_scale=guidance_scale,
            mask_image=mask_image,
            seed=seed,
            num_inference_steps=num_inference_steps,
        )["sample"]
        pipe.set_progress_bar_config()
    
        image_grid(init_images, rows=1, cols=1)
    
        num_outpainting_steps = num_frames
        num_interpol_frames = 30
    
        current_image = init_images[0]
        all_frames = []
        all_frames.append(current_image)
        for i in trange(num_outpainting_steps, desc=f'Generating {num_outpainting_steps} additional images...'):
            prev_image_fix = current_image
    
            prev_image = shrink_and_paste_on_blank(current_image, mask_width)
    
            current_image = prev_image
    
            # create mask (black image with white mask_width width edges)
            mask_image = np.array(current_image)[:, :, 3]
            mask_image = PIL.Image.fromarray(255 - mask_image).convert("RGB")
    
            # inpainting step
            current_image = current_image.convert("RGB")
            images = pipe(
                prompt=prompt,
                negative_prompt=negative_prompt,
                image=current_image,
                guidance_scale=guidance_scale,
                mask_image=mask_image,
                seed=seed,
                num_inference_steps=num_inference_steps,
            )["sample"]
            current_image = images[0]
            current_image.paste(prev_image, mask=prev_image)
    
            # interpolation steps bewteen 2 inpainted images (=sequential zoom and crop)
            for j in range(num_interpol_frames - 1):
                interpol_image = current_image
                interpol_width = round((1 - (1 - 2 * mask_width / height) ** (1 - (j + 1) / num_interpol_frames)) * height / 2)
                interpol_image = interpol_image.crop(
                    (
                        interpol_width,
                        interpol_width,
                        width - interpol_width,
                        height - interpol_width,
                    )
                )
    
                interpol_image = interpol_image.resize((height, width))
    
                # paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
                interpol_width2 = round((1 - (height - 2 * mask_width) / (height - 2 * interpol_width)) / 2 * height)
                prev_image_fix_crop = shrink_and_paste_on_blank(prev_image_fix, interpol_width2)
                interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
                all_frames.append(interpol_image)
            all_frames.append(current_image)
    
        video_file_name = f"infinite_zoom_{'in' if zoom_in else 'out'}"
        fps = 30
        save_path = video_file_name + ".mp4"
        write_video(save_path, all_frames, fps, reversed_order=zoom_in)
        return save_path

.. code:: ipython3

    def shrink_and_paste_on_blank(current_image:PIL.Image.Image, mask_width:int):
        """
        Decreases size of current_image by mask_width pixels from each side,
        then adds a mask_width width transparent frame,
        so that the image the function returns is the same size as the input.
        
        Parameters:
            current_image (PIL.Image): input image to transform
            mask_width (int): width in pixels to shrink from each side
        Returns:
           prev_image (PIL.Image): resized image with extended borders
        """
    
        height = current_image.height
        width = current_image.width
    
        # shrink down by mask_width
        prev_image = current_image.resize((height - 2 * mask_width, width - 2 * mask_width))
        prev_image = prev_image.convert("RGBA")
        prev_image = np.array(prev_image)
    
        # create blank non-transparent image
        blank_image = np.array(current_image.convert("RGBA")) * 0
        blank_image[:, :, 3] = 1
    
        # paste shrinked onto blank
        blank_image[
            mask_width : height - mask_width, mask_width : width - mask_width, :
        ] = prev_image
        prev_image = PIL.Image.fromarray(blank_image)
    
        return prev_image
    
    
    def image_grid(imgs:List[PIL.Image.Image], rows:int, cols:int):
        """
        Insert images to grid
        
        Parameters:
            imgs (List[PIL.Image.Image]): list of images for making grid
            rows (int): number of rows in grid
            cols (int): number of columns in grid
        Returns:
            grid (PIL.Image): image with input images collage
        """
        assert len(imgs) == rows * cols
    
        w, h = imgs[0].size
        grid = PIL.Image.new("RGB", size=(cols * w, rows * h))
    
        for i, img in enumerate(imgs):
            grid.paste(img, box=(i % cols * w, i // cols * h))
        return grid
    
    
    def write_video(file_path:str, frames:List[PIL.Image.Image], fps:float, reversed_order:bool = True, gif:bool = True):
        """
        Writes frames to an mp4 video file and optionaly to gif
        
        Parameters:
            file_path (str): Path to output video, must end with .mp4
            frames (List of PIL.Image): list of frames
            fps (float): Desired frame rate
            reversed_order (bool): if order of images to be reversed (default = True)
            gif (bool): save frames to gif format (default = True)
        Returns:
            None
        """
        if reversed_order:
            frames.reverse()
    
        w, h = frames[0].size
        fourcc = cv2.VideoWriter_fourcc("m", "p", "4", "v")
        # fourcc = cv2.VideoWriter_fourcc(*'avc1')
        writer = cv2.VideoWriter(file_path, fourcc, fps, (w, h))
    
        for frame in frames:
            np_frame = np.array(frame.convert("RGB"))
            cv_frame = cv2.cvtColor(np_frame, cv2.COLOR_RGB2BGR)
            writer.write(cv_frame)
    
        writer.release()
        if gif:
            frames[0].save(
                file_path.replace(".mp4", ".gif"),
                save_all=True,
                append_images=frames[1:],
                duratiobn=len(frames) / fps,
                loop=0,
            )

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


Configuration steps:

1. Load models on device.
2. Configure tokenizer and scheduler.
3. Create instance of ``OVStableDiffusionInpaintingPipeline`` class.

.. code:: ipython3

    from openvino.runtime import Core
    
    core = Core()
    
    tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')

Select inference device `⇑ <#top>`__
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


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




.. parsed-literal::

    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')



.. code:: ipython3

    
    text_enc_inpaint = core.compile_model(TEXT_ENCODER_OV_PATH_INPAINT, device.value)
    unet_model_inpaint = core.compile_model(UNET_OV_PATH_INPAINT, device.value)
    vae_decoder_inpaint = core.compile_model(VAE_DECODER_OV_PATH_INPAINT, device.value)
    vae_encoder_inpaint = core.compile_model(VAE_ENCODER_OV_PATH_INPAINT, device.value)
    
    ov_pipe_inpaint = OVStableDiffusionInpaintingPipeline(
        tokenizer=tokenizer,
        text_encoder=text_enc_inpaint,
        unet=unet_model_inpaint,
        vae_encoder=vae_encoder_inpaint,
        vae_decoder=vae_decoder_inpaint,
        scheduler=scheduler_inpaint,
    )

Run Infinite Zoom video generation `⇑ <#top>`__
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


.. code:: ipython3

    import gradio as gr
    from socket import gethostbyname, gethostname
    
    def generate(
        prompt,
        negative_prompt,
        seed,
        steps,
        frames,
        edge_size,
        zoom_in,
        progress=gr.Progress(track_tqdm=True),
    ):
        video_path = generate_video(
            ov_pipe_inpaint,
            prompt,
            negative_prompt,
            num_inference_steps=steps,
            num_frames=frames,
            mask_width=edge_size,
            seed=seed,
            zoom_in=zoom_in,
        )
        return video_path.replace(".mp4", ".gif")
    
    
    gr.close_all()
    demo = gr.Interface(
        generate,
        [
            gr.Textbox(
                "valley in the Alps at sunset, epic vista, beautiful landscape, 4k, 8k",
                label="Prompt",
            ),
            gr.Textbox("lurry, bad art, blurred, text, watermark", label="Negative prompt"),
            gr.Slider(value=9999, label="Seed", maximum=10000000),
            gr.Slider(value=20, label="Steps", minimum=1, maximum=50),
            gr.Slider(value=3, label="Frames", minimum=1, maximum=50),
            gr.Slider(value=128, label="Edge size", minimum=32, maximum=256),
            gr.Checkbox(label="Zoom in"),
        ],
        "image",
    )
    ipaddr = gethostbyname(gethostname())
    demo.queue().launch(share=True)


.. parsed-literal::

    Running on local URL:  http://127.0.0.1:7861
    Running on public URL: https://462b1833bf3b980731.gradio.live
    
    This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)



.. .. raw:: html

..     <div><iframe src="https://462b1833bf3b980731.gradio.live" width="100%" height="500" allow="autoplay; camera; microphone; clipboard-read; clipboard-write;" frameborder="0" allowfullscreen></iframe></div>