Image Generation with Stable Diffusion and IP-Adapter
=====================================================

`IP-Adapter <https://hf.co/papers/2308.06721>`__ is an effective and
lightweight adapter that adds image prompting capabilities to a
diffusion model. This adapter works by decoupling the cross-attention
layers of the image and text features. All the other model components
are frozen and only the embedded image features in the UNet are trained.
As a result, IP-Adapter files are typically only ~100MBs.
|ip-adapter-pipe.png|

In this tutorial, we will consider how to convert and run Stable
Diffusion pipeline with loading IP-Adapter. We will use
`stable-diffusion-v1.5 <https://huggingface.co/runwayml/stable-diffusion-v1-5>`__
as base model and apply official
`IP-Adapter <https://huggingface.co/h94/IP-Adapter>`__ weights. Also for
speedup generation process we will use
`LCM-LoRA <https://huggingface.co/latent-consistency/lcm-lora-sdv1-5>`__

.. |ip-adapter-pipe.png| image:: https://huggingface.co/h94/IP-Adapter/resolve/main/fig1.png

**Table of contents:**


-  `Prerequisites <#prerequisites>`__
-  `Prepare Diffusers pipeline <#prepare-diffusers-pipeline>`__
-  `Convert PyTorch models <#convert-pytorch-models>`__

   -  `Image Encoder <#image-encoder>`__
   -  `U-net <#u-net>`__
   -  `VAE Encoder and Decoder <#vae-encoder-and-decoder>`__
   -  `Text Encoder <#text-encoder>`__

-  `Prepare OpenVINO inference
   pipeline <#prepare-openvino-inference-pipeline>`__
-  `Run model inference <#run-model-inference>`__

   -  `Select inference device <#select-inference-device>`__
   -  `Generation image variation <#generation-image-variation>`__
   -  `Generation conditioned by image and
      text <#generation-conditioned-by-image-and-text>`__
   -  `Generation image blending <#generation-image-blending>`__

-  `Interactive demo <#interactive-demo>`__

Installation Instructions
~~~~~~~~~~~~~~~~~~~~~~~~~

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

We recommend running the notebook in a virtual environment. You only
need a Jupyter server to start. For details, please refer to
`Installation
Guide <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/README.md#-installation-guide>`__.

Prerequisites
-------------



.. code:: ipython3

    import platform
    
    %pip install -q "torch>=2.1" transformers accelerate diffusers "openvino>=2023.3.0" "gradio>=4.19" opencv-python "peft==0.6.2" "protobuf>=3.20" --extra-index-url https://download.pytorch.org/whl/cpu
    
    if platform.system() != "Windows":
        %pip install -q "matplotlib>=3.4"
    else:
        %pip install -q "matplotlib>=3.4,<3.7"


.. parsed-literal::

    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
    botocore 1.34.162 requires urllib3<1.27,>=1.25.4; python_version < "3.10", but you have urllib3 2.2.2 which is incompatible.
    tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.4 which is incompatible.
    tf2onnx 1.16.1 requires protobuf~=3.20, but you have protobuf 5.27.4 which is incompatible.
    wandb 0.15.4 requires protobuf!=4.21.0,<5,>=3.12.0; python_version < "3.9" and sys_platform == "linux", but you have protobuf 5.27.4 which is incompatible.
    Note: you may need to restart the kernel to use updated packages.
    Note: you may need to restart the kernel to use updated packages.


Prepare Diffusers pipeline
--------------------------



First of all, we should collect all components of our pipeline together.
To work with Stable Diffusion, we will use HuggingFace
`Diffusers <https://github.com/huggingface/diffusers>`__ library. To
experiment with Stable Diffusion models, Diffusers exposes the
`StableDiffusionPipeline <https://huggingface.co/docs/diffusers/using-diffusers/conditional_image_generation>`__
similar to the `other Diffusers
pipelines <https://huggingface.co/docs/diffusers/api/pipelines/overview>`__.
Additionally, the pipeline supports load adapters that extend Stable
Diffusion functionality such as `Low-Rank Adaptation
(LoRA) <https://huggingface.co/papers/2106.09685>`__,
`PEFT <https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference>`__,
`IP-Adapter <https://ip-adapter.github.io/>`__, and `Textual
Inversion <https://textual-inversion.github.io/>`__. You can find more
information about supported adapters in `diffusers
documentation <https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters>`__.

In this tutorial, we will focus on ip-adapter. IP-Adapter can be
integrated into diffusion pipeline using ``load_ip_adapter`` method.
IP-Adapter allows you to use both image and text to condition the image
generation process. For adjusting the text prompt and image prompt
condition ratio, we can use ``set_ip_adapter_scale()`` method. If you
only use the image prompt, you should set the scale to 1.0. You can
lower the scale to get more generation diversity, but it’ll be less
aligned with the prompt. scale=0.5 can achieve good results when you use
both text and image prompts.

As discussed before, we will also use LCM LoRA for speeding generation
process. You can find more information about LCM LoRA in this
`notebook <latent-consistency-models-image-generation-with-output.html>`__.
For applying LCM LoRA, we should use ``load_lora_weights`` method.
Additionally, LCM requires using LCMScheduler for efficient generation.

.. code:: ipython3

    from pathlib import Path
    from diffusers import AutoPipelineForText2Image
    from transformers import CLIPVisionModelWithProjection
    from diffusers.utils import load_image
    from diffusers import LCMScheduler
    
    
    stable_diffusion_id = "runwayml/stable-diffusion-v1-5"
    ip_adapter_id = "h94/IP-Adapter"
    ip_adapter_weight_name = "ip-adapter_sd15.bin"
    lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
    models_dir = Path("model")
    
    load_original_pipeline = not all(
        [
            (models_dir / model_name).exists()
            for model_name in [
                "text_encoder.xml",
                "image_encoder.xml",
                "unet.xml",
                "vae_decoder.xml",
                "vae_encoder.xml",
            ]
        ]
    )
    
    
    def get_pipeline_components(
        stable_diffusion_id,
        ip_adapter_id,
        ip_adapter_weight_name,
        lcm_lora_id,
        ip_adapter_scale=0.6,
    ):
        image_encoder = CLIPVisionModelWithProjection.from_pretrained("h94/IP-Adapter", subfolder="models/image_encoder")
        pipeline = AutoPipelineForText2Image.from_pretrained(stable_diffusion_id, image_encoder=image_encoder)
        pipeline.load_lora_weights(lcm_lora_id)
        pipeline.fuse_lora()
        pipeline.load_ip_adapter(ip_adapter_id, subfolder="models", weight_name=ip_adapter_weight_name)
        pipeline.set_ip_adapter_scale(0.6)
        scheduler = LCMScheduler.from_pretrained(stable_diffusion_id, subfolder="scheduler")
        return (
            pipeline.tokenizer,
            pipeline.feature_extractor,
            scheduler,
            pipeline.text_encoder,
            pipeline.image_encoder,
            pipeline.unet,
            pipeline.vae,
        )
    
    
    if load_original_pipeline:
        (
            tokenizer,
            feature_extractor,
            scheduler,
            text_encoder,
            image_encoder,
            unet,
            vae,
        ) = get_pipeline_components(stable_diffusion_id, ip_adapter_id, ip_adapter_weight_name, lcm_lora_id)
        scheduler.save_pretrained(models_dir / "scheduler")
    else:
        tokenizer, feature_extractor, scheduler, text_encoder, image_encoder, unet, vae = (
            None,
            None,
            None,
            None,
            None,
            None,
            None,
        )


.. parsed-literal::

    The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
    2024-08-28 05:57:10.757745: 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`.
    2024-08-28 05:57:10.792241: 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.
    2024-08-28 05:57:11.340886: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT



.. parsed-literal::

    Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]


Convert PyTorch models
----------------------



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

The pipeline consists of four important parts:

-  Image Encoder to create image condition for IP-Adapter.
-  Text Encoder to create condition to generate an image from a text
   prompt.
-  U-Net for step-by-step denoising latent image representation.
-  Autoencoder (VAE) for decoding latent space to image.

Let us convert each part:

Image Encoder
~~~~~~~~~~~~~



IP-Adapter relies on an image encoder to generate the image features.
Usually
`CLIPVisionModelWithProjection <https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPVisionModelWithProjection>`__
is used as Image Encoder. For preprocessing input image, Image Encoder
uses ``CLIPImageProcessor`` named feature extractor in pipeline. The
image encoder accept resized and normalized image processed by feature
extractor as input and returns image embeddings.

.. code:: ipython3

    import openvino as ov
    import torch
    import gc
    
    
    def cleanup_torchscript_cache():
        """
        Helper for removing cached model representation
        """
        torch._C._jit_clear_class_registry()
        torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
        torch.jit._state._clear_class_state()
    
    
    IMAGE_ENCODER_PATH = models_dir / "image_encoder.xml"
    
    if not IMAGE_ENCODER_PATH.exists():
        with torch.no_grad():
            ov_model = ov.convert_model(
                image_encoder,
                example_input=torch.zeros((1, 3, 224, 224)),
                input=[-1, 3, 224, 224],
            )
        ov.save_model(ov_model, IMAGE_ENCODER_PATH)
        feature_extractor.save_pretrained(models_dir / "feature_extractor")
        del ov_model
        cleanup_torchscript_cache()
    
    del image_encoder
    del feature_extractor
    
    gc.collect();


.. parsed-literal::

    WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.


.. parsed-literal::

    [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4664: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
      warnings.warn(


U-net
~~~~~



U-Net model gradually denoises latent image representation guided by
text encoder hidden state.

Generally, U-Net model conversion process remain the same like in Stable
Diffusion, expect additional input that accept image embeddings
generated by Image Encoder. In Stable Diffusion pipeline, this data
provided into model using dictionary ``added_cond_kwargs`` and key
``image_embeds`` inside it. After OpenVINO conversion, this input will
be decomposed from dictionary. In some cases, such decomposition may
lead to loosing information about input shape and data type. We can
restore it manually as demonstrated in the code bellow.

U-Net model 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.
-  ``image_embeds`` - hidden state of image encoder.

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

.. code:: ipython3

    UNET_PATH = models_dir / "unet.xml"
    
    
    if not UNET_PATH.exists():
        inputs = {
            "sample": torch.randn((2, 4, 64, 64)),
            "timestep": torch.tensor(1),
            "encoder_hidden_states": torch.randn((2, 77, 768)),
            "added_cond_kwargs": {"image_embeds": torch.ones((2, 1024))},
        }
    
        with torch.no_grad():
            ov_model = ov.convert_model(unet, example_input=inputs)
        # dictionary with added_cond_kwargs will be decomposed during conversion
        # in some cases decomposition may lead to losing data type and shape information
        # We need to recover it manually after the conversion
        ov_model.inputs[-1].get_node().set_element_type(ov.Type.f32)
        ov_model.validate_nodes_and_infer_types()
        ov.save_model(ov_model, UNET_PATH)
        del ov_model
        cleanup_torchscript_cache()
    
    del unet
    
    gc.collect();


.. parsed-literal::

    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/unets/unet_2d_condition.py:1110: 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 dim % default_overall_up_factor != 0:
    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/embeddings.py:1624: FutureWarning: You have passed a tensor as `image_embeds`.This is deprecated and will be removed in a future release. Please make sure to update your script to pass `image_embeds` as a list of tensors to suppress this warning.
      deprecate("image_embeds not a list", "1.0.0", deprecation_message, standard_warn=False)
    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:136: 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
    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:145: 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
    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:146: 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
    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:162: 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:


VAE Encoder and Decoder
~~~~~~~~~~~~~~~~~~~~~~~



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. VAE encoder is used in Image-to-Image generation pipelines for
creating initial latent state based on input image. The main difference
between IP-Adapter encoded image and VAE encoded image that the first is
used as addition into input prompt making connection between text and
image during conditioning, while the second used as Unet sample
initialization and does not give guarantee preserving some attributes of
initial image. It is still can be useful to use both ip-adapter and VAE
image in pipeline, we can discuss it in inference examples.

.. code:: ipython3

    VAE_DECODER_PATH = models_dir / "vae_decoder.xml"
    VAE_ENCODER_PATH = models_dir / "vae_encoder.xml"
    
    if not VAE_DECODER_PATH.exists():
    
        class VAEDecoderWrapper(torch.nn.Module):
            def __init__(self, vae):
                super().__init__()
                self.vae = vae
    
            def forward(self, latents):
                return self.vae.decode(latents)
    
        vae_decoder = VAEDecoderWrapper(vae)
        with torch.no_grad():
            ov_model = ov.convert_model(vae_decoder, example_input=torch.ones([1, 4, 64, 64]))
        ov.save_model(ov_model, VAE_DECODER_PATH)
        del ov_model
        cleanup_torchscript_cache()
        del vae_decoder
    
    if not VAE_ENCODER_PATH.exists():
    
        class VAEEncoderWrapper(torch.nn.Module):
            def __init__(self, vae):
                super().__init__()
                self.vae = vae
    
            def forward(self, image):
                return self.vae.encode(x=image)["latent_dist"].sample()
    
        vae_encoder = VAEEncoderWrapper(vae)
        vae_encoder.eval()
        image = torch.zeros((1, 3, 512, 512))
        with torch.no_grad():
            ov_model = ov.convert_model(vae_encoder, example_input=image)
        ov.save_model(ov_model, VAE_ENCODER_PATH)
        del ov_model
        cleanup_torchscript_cache()
    
    del vae
    gc.collect();


.. parsed-literal::

    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1303: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
    	%2494 : Float(1, 4, 64, 64, strides=[16384, 4096, 64, 1], requires_grad=0, device=cpu) = aten::randn(%2488, %2489, %2490, %2491, %2492, %2493) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/torch_utils.py:81:0
    This may cause errors in trace checking. To disable trace checking, pass check_trace=False to torch.jit.trace()
      _check_trace(
    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1303: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
    Tensor-likes are not close!
    
    Mismatched elements: 10253 / 16384 (62.6%)
    Greatest absolute difference: 0.0016326904296875 at index (0, 1, 63, 63) (up to 1e-05 allowed)
    Greatest relative difference: 0.009607371181261107 at index (0, 3, 63, 59) (up to 1e-05 allowed)
      _check_trace(


Text Encoder
~~~~~~~~~~~~



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.

The input of the text encoder is tensor ``input_ids``, which contains
indexes of tokens from text processed by the tokenizer and padded to the
maximum length accepted by the 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.

.. code:: ipython3

    TEXT_ENCODER_PATH = models_dir / "text_encoder.xml"
    
    if not TEXT_ENCODER_PATH.exists():
        with torch.no_grad():
            ov_model = ov.convert_model(
                text_encoder,
                example_input=torch.ones([1, 77], dtype=torch.long),
                input=[
                    (1, 77),
                ],
            )
        ov.save_model(ov_model, TEXT_ENCODER_PATH)
        del ov_model
        cleanup_torchscript_cache()
        tokenizer.save_pretrained(models_dir / "tokenizer")
    
    del text_encoder
    del tokenizer


.. parsed-literal::

    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: 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 input_shape[-1] > 1 or self.sliding_window is not None:
    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-761/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: 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 past_key_values_length > 0:


Prepare OpenVINO inference pipeline
-----------------------------------



As shown on diagram below, the only difference between original Stable
Diffusion pipeline and IP-Adapter Stable Diffusion pipeline only in
additional conditioning by image processed via Image Encoder.
|pipeline.png|

The stable diffusion model with ip-adapter takes a latent image
representation, a text prompt is transformed to text embeddings via CLIP
text encoder and ip-adapter image is transformed to image embeddings via
CLIP Image Encoder. Next, the U-Net iteratively *denoises* the random
latent image representations while being conditioned on the text and
image embeddings. The output of the U-Net, being the noise residual, is
used to compute a denoised latent image representation via a scheduler
algorithm.

The *denoising* process is repeated given number of times (by default 4
taking into account that we use LCM) 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 (VAE).

.. |pipeline.png| image:: https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/1afc2ca6-e7ea-4c9e-a2d3-1173346dd9d6

.. code:: ipython3

    import inspect
    from typing import List, Optional, Union, Dict, Tuple
    import numpy as np
    
    import PIL
    import cv2
    import torch
    
    from transformers import CLIPTokenizer, CLIPImageProcessor
    from diffusers import DiffusionPipeline
    from diffusers.pipelines.stable_diffusion.pipeline_output import (
        StableDiffusionPipelineOutput,
    )
    from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
    
    
    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 randn_tensor(
        shape: Union[Tuple, List],
        generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,
        dtype: Optional["torch.dtype"] = None,
    ):
        """A helper function to create random tensors on the desired `device` with the desired `dtype`. When
        passing a list of generators, you can seed each batch size individually.
    
        """
        batch_size = shape[0]
        rand_device = torch.device("cpu")
    
        # make sure generator list of length 1 is treated like a non-list
        if isinstance(generator, list) and len(generator) == 1:
            generator = generator[0]
    
        if isinstance(generator, list):
            shape = (1,) + shape[1:]
            latents = [torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype) for i in range(batch_size)]
            latents = torch.cat(latents, dim=0)
        else:
            latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype)
    
        return latents
    
    
    def preprocess(image: PIL.Image.Image, height, width):
        """
        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(height, width, src_width, src_height)
        image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
        pad_width = width - dst_width
        pad_height = height - 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: ov.Model,
            text_encoder: ov.Model,
            tokenizer: CLIPTokenizer,
            unet: ov.Model,
            scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
            image_encoder: ov.Model,
            feature_extractor: CLIPImageProcessor,
            vae_encoder: ov.Model,
        ):
            """
            Pipeline for text-to-image generation using Stable Diffusion and IP-Adapter with OpenVINO
            Parameters:
                vae_decoder (ov.Model):
                    Variational Auto-Encoder (VAE) Model to decode images to and from latent representations.
                text_encoder (ov.Model):CLIPImageProcessor
                    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 (ov.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
                image_encoder (ov.Model):
                    IP-Adapter image encoder for embedding input image as input prompt for generation
                feature_extractor :
            """
            super().__init__()
            self.scheduler = scheduler
            self.vae_decoder = vae_decoder
            self.image_encoder = image_encoder
            self.text_encoder = text_encoder
            self.unet = unet
            self.height = 512
            self.width = 512
            self.vae_scale_factor = 8
            self.tokenizer = tokenizer
            self.vae_encoder = vae_encoder
            self.feature_extractor = feature_extractor
    
        def __call__(
            self,
            prompt: Union[str, List[str]],
            ip_adapter_image: PIL.Image.Image,
            image: PIL.Image.Image = None,
            num_inference_steps: Optional[int] = 4,
            negative_prompt: Union[str, List[str]] = None,
            guidance_scale: Optional[float] = 0.5,
            eta: Optional[float] = 0.0,
            output_type: Optional[str] = "pil",
            height: Optional[int] = None,
            width: Optional[int] = None,
            generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
            latents: Optional[torch.FloatTensor] = None,
            strength: float = 1.0,
            **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.
                negative_prompt (str or List[str]):https://user-images.githubusercontent.com/29454499/258651862-28b63016-c5ff-4263-9da8-73ca31100165.jpeg
                    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.
                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.
                height (int, *optional*, 512):
                    Generated image height
                width (int, *optional*, 512):
                    Generated image width
                generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                    A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                    generation deterministic.
                latents (`torch.FloatTensor`, *optional*):
                    Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
                    generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                    tensor is generated by sampling using the supplied random `generator`.
            Returns:
                Dictionary with keys:
                    sample - the last generated image PIL.Image.Image or np.arrayhttps://huggingface.co/latent-consistency/lcm-lora-sdv1-5
                    iterations - *optional* (if gif=True) images for all diffusion steps, List of PIL.Image.Image or np.array.
            """
            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,
            )
            # get ip-adapter image embeddings
            image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image)
            if do_classifier_free_guidance:
                image_embeds = np.concatenate([negative_image_embeds, image_embeds])
    
            # 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(
                1,
                4,
                height or self.height,
                width or self.width,
                generator=generator,
                latents=latents,
                image=image,
                latent_timestep=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, image_embeds])[0]
                # 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 * (1 / 0.18215))[0]
    
            image = self.postprocess_image(image, meta, output_type)
            return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=False)
    
        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)[0]
    
            # 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)[0]
    
                # 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,
            batch_size,
            num_channels_latents,
            height,
            width,
            dtype=torch.float32,
            generator=None,
            latents=None,
            image=None,
            latent_timestep=None,
        ):
            shape = (
                batch_size,
                num_channels_latents,
                height // self.vae_scale_factor,
                width // self.vae_scale_factor,
            )
            if isinstance(generator, list) and len(generator) != batch_size:
                raise ValueError(
                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
                )
    
            if latents is None:
                latents = randn_tensor(shape, generator=generator, dtype=dtype)
    
            if image is None:
                # scale the initial noise by the standard deviation required by the scheduler
                latents = latents * self.scheduler.init_noise_sigma
                return latents.numpy(), {}
            input_image, meta = preprocess(image, height, width)
            image_latents = self.vae_encoder(input_image)[0]
            image_latents = image_latents * 0.18215
            latents = self.scheduler.add_noise(torch.from_numpy(image_latents), latents, 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 initial image size (if required),
            normalize and convert to [0, 255] pixels range. Optionally, converts it from np.ndarray to PIL.Image format
    
            Parameters:
                image (np.ndarray):
                    Generated image
                meta (Dict):
                    Metadata obtained on the 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):
                    Post-processed 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 encode_image(self, image, num_images_per_prompt=1):
            if not isinstance(image, torch.Tensor):
                image = self.feature_extractor(image, return_tensors="pt").pixel_values
    
            image_embeds = self.image_encoder(image)[0]
            if num_images_per_prompt > 1:
                image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
    
            uncond_image_embeds = np.zeros(image_embeds.shape)
            return image_embeds, uncond_image_embeds
    
        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

Run model inference
-------------------



Now let’s configure our pipeline and take a look on generation results.

Select inference device
~~~~~~~~~~~~~~~~~~~~~~~



Select inference device from dropdown list.

.. code:: ipython3

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




.. parsed-literal::

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



.. code:: ipython3

    from transformers import AutoTokenizer
    
    ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device.value != "CPU" else {}
    vae_decoder = core.compile_model(VAE_DECODER_PATH, device.value, ov_config)
    vae_encoder = core.compile_model(VAE_ENCODER_PATH, device.value, ov_config)
    text_encoder = core.compile_model(TEXT_ENCODER_PATH, device.value)
    image_encoder = core.compile_model(IMAGE_ENCODER_PATH, device.value)
    unet = core.compile_model(UNET_PATH, device.value)
    
    scheduler = LCMScheduler.from_pretrained(models_dir / "scheduler")
    tokenizer = AutoTokenizer.from_pretrained(models_dir / "tokenizer")
    feature_extractor = CLIPImageProcessor.from_pretrained(models_dir / "feature_extractor")
    
    ov_pipe = OVStableDiffusionPipeline(
        vae_decoder,
        text_encoder,
        tokenizer,
        unet,
        scheduler,
        image_encoder,
        feature_extractor,
        vae_encoder,
    )


.. parsed-literal::

    The config attributes {'skip_prk_steps': True} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.


Generation image variation
~~~~~~~~~~~~~~~~~~~~~~~~~~



If we stay input text prompt empty and provide only ip-adapter image, we
can get variation of the same image.

.. code:: ipython3

    import matplotlib.pyplot as plt
    
    
    def visualize_results(images, titles):
        """
        Helper function for results visualization
    
        Parameters:
           orig_img (PIL.Image.Image): original image
           processed_img (PIL.Image.Image): processed image after editing
           img1_title (str): title for the image on the left
           img2_title (str): title for the image on the right
        Returns:
           fig (matplotlib.pyplot.Figure): matplotlib generated figure contains drawing result
        """
        im_w, im_h = images[0].size
        is_horizontal = im_h <= im_w
        figsize = (10, 15 * len(images)) if is_horizontal else (15 * len(images), 10)
        fig, axs = plt.subplots(
            1 if is_horizontal else len(images),
            len(images) if is_horizontal else 1,
            figsize=figsize,
            sharex="all",
            sharey="all",
        )
        fig.patch.set_facecolor("white")
        list_axes = list(axs.flat)
        for a in list_axes:
            a.set_xticklabels([])
            a.set_yticklabels([])
            a.get_xaxis().set_visible(False)
            a.get_yaxis().set_visible(False)
            a.grid(False)
        for image, title, ax in zip(images, titles, list_axes):
            ax.imshow(np.array(image))
            ax.set_title(title, fontsize=20)
        fig.subplots_adjust(wspace=0.0 if is_horizontal else 0.01, hspace=0.01 if is_horizontal else 0.0)
        fig.tight_layout()
        return fig

.. code:: ipython3

    generator = torch.Generator(device="cpu").manual_seed(576)
    
    image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png")
    
    result = ov_pipe(
        prompt="",
        ip_adapter_image=image,
        gaidance_scale=1,
        negative_prompt="",
        num_inference_steps=4,
        generator=generator,
    )
    
    fig = visualize_results([image, result.images[0]], ["input image", "result"])



.. parsed-literal::

      0%|          | 0/4 [00:00<?, ?it/s]



.. image:: stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_22_1.png


Generation conditioned by image and text
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



IP-Adapter allows you to use both image and text to condition the image
generation process. Both IP-Adapter image and text prompt serve as
extension for each other, for example we can use a text prompt to add
“sunglasses” 😎 on previous image.

.. code:: ipython3

    generator = torch.Generator(device="cpu").manual_seed(576)
    
    result = ov_pipe(
        prompt="best quality, high quality, wearing sunglasses",
        ip_adapter_image=image,
        gaidance_scale=1,
        negative_prompt="monochrome, low-res, bad anatomy, worst quality, low quality",
        num_inference_steps=4,
        generator=generator,
    )



.. parsed-literal::

      0%|          | 0/4 [00:00<?, ?it/s]


.. code:: ipython3

    fig = visualize_results([image, result.images[0]], ["input image", "result"])



.. image:: stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_25_0.png


Generation image blending
~~~~~~~~~~~~~~~~~~~~~~~~~



IP-Adapter also works great with Image-to-Image translation. It helps to
achieve image blending effect.

.. code:: ipython3

    image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/vermeer.jpg")
    ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/river.png")
    
    result = ov_pipe(
        prompt="best quality, high quality",
        image=image,
        ip_adapter_image=ip_image,
        gaidance_scale=1,
        generator=generator,
        strength=0.7,
        num_inference_steps=8,
    )



.. parsed-literal::

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


.. code:: ipython3

    fig = visualize_results([image, ip_image, result.images[0]], ["input image", "ip-adapter image", "result"])



.. image:: stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_28_0.png


Interactive demo
----------------



Now, you can try model using own images and text prompts.

.. code:: ipython3

    import requests
    from pathlib import Path
    
    if not Path("gradio_helper.py").exists():
        r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/stable-diffusion-ip-adapter/gradio_helper.py")
        open("gradio_helper.py", "w").write(r.text)
    
    from gradio_helper import make_demo
    
    demo = make_demo(ov_pipe)
    
    try:
        demo.queue().launch(debug=False)
    except Exception:
        demo.queue().launch(share=True, debug=False)
    # if you are launching remotely, specify server_name and server_port
    # demo.launch(server_name='your server name', server_port='server port in int')
    # Read more in the docs: https://gradio.app/docs/


.. parsed-literal::

    Running on local URL:  http://127.0.0.1:7860
    
    To create a public link, set `share=True` in `launch()`.







