text_to_image.py

# -*- coding: utf-8 -*-
"""text-to-image.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1OcehPd4sJRgAE0kaYV9y8oTf0G0VElbU
"""

# Commented out IPython magic to ensure Python compatibility.
'''pip install -q "openvino>=2023.1.0"
pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "diffusers[torch]>=0.9.0"
pip install -q "huggingface-hub>=0.9.1"
pip install -q gradio
pip install -q transformers
pip install kaleido cohere openai tiktoken
pip install typing-extensions==3.10.0.2
pip install diffusers transformers'''

from diffusers import StableDiffusionPipeline
import gc

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

del pipe
gc.collect()

from pathlib import Path
import torch
import openvino as ov

TEXT_ENCODER_OV_PATH = Path("text_encoder.xml")

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()

def convert_encoder(text_encoder: torch.nn.Module, ir_path:Path):
    """
    Convert Text Encoder mode.
    Function accepts text encoder model, and prepares example inputs for conversion,
    Parameters:
        text_encoder (torch.nn.Module): text_encoder model from Stable Diffusion pipeline
        ir_path (Path): File for storing model
    Returns:
        None
    """
    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 IR format
        ov_model = ov.convert_model(text_encoder, example_input=input_ids, input=[(1,77),])
    ov.save_model(ov_model, ir_path)
    del ov_model
    cleanup_torchscript_cache()
    print(f'Text Encoder successfully converted to IR and saved to {ir_path}')


if not TEXT_ENCODER_OV_PATH.exists():
    convert_encoder(text_encoder, TEXT_ENCODER_OV_PATH)
else:
    print(f"Text encoder will be loaded from {TEXT_ENCODER_OV_PATH}")

del text_encoder
gc.collect()

import numpy as np

UNET_OV_PATH = Path('unet.xml')

dtype_mapping = {
    torch.float32: ov.Type.f32,
    torch.float64: ov.Type.f64
}


def convert_unet(unet:torch.nn.Module, ir_path:Path):
    """
    Convert U-net model to IR format.
    Function accepts unet model, prepares example inputs for conversion,
    Parameters:
        unet (StableDiffusionPipeline): unet from Stable Diffusion pipeline
        ir_path (Path): File for storing model
    Returns:
        None
    """
    # prepare inputs
    encoder_hidden_state = torch.ones((2, 77, 768))
    latents_shape = (2, 4, 512 // 8, 512 // 8)
    latents = torch.randn(latents_shape)
    t = torch.from_numpy(np.array(1, dtype=float))
    dummy_inputs = (latents, t, encoder_hidden_state)
    input_info = []
    for input_tensor in dummy_inputs:
        shape = ov.PartialShape(tuple(input_tensor.shape))
        element_type = dtype_mapping[input_tensor.dtype]
        input_info.append((shape, element_type))

    unet.eval()
    with torch.no_grad():
        ov_model = ov.convert_model(unet, example_input=dummy_inputs, input=input_info)
    ov.save_model(ov_model, ir_path)
    del ov_model
    cleanup_torchscript_cache()
    print(f'Unet successfully converted to IR and saved to {ir_path}')


if not UNET_OV_PATH.exists():
    convert_unet(unet, UNET_OV_PATH)
    gc.collect()
else:
    print(f"Unet will be loaded from {UNET_OV_PATH}")
del unet
gc.collect()

VAE_ENCODER_OV_PATH = Path("vae_encoder.xml")

def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path):
    """
    Convert VAE model for encoding to IR format.
    Function accepts vae model, creates wrapper class for export only necessary for inference part,
    prepares example inputs for conversion,
    Parameters:
        vae (torch.nn.Module): VAE model from StableDiffusio pipeline
        ir_path (Path): File for storing model
    Returns:
        None
    """
    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, input=[((1,3,512,512),)])
    ov.save_model(ov_model, ir_path)
    del ov_model
    cleanup_torchscript_cache()
    print(f'VAE encoder successfully converted to IR and saved to {ir_path}')


if not VAE_ENCODER_OV_PATH.exists():
    convert_vae_encoder(vae, VAE_ENCODER_OV_PATH)
else:
    print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH}")

VAE_DECODER_OV_PATH = Path('vae_decoder.xml')

def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path):
    """
    Convert VAE model for decoding to IR format.
    Function accepts vae model, creates wrapper class for export only necessary for inference part,
    prepares example inputs for conversion,
    Parameters:
        vae (torch.nn.Module): VAE model frm StableDiffusion pipeline
        ir_path (Path): File for storing model
    Returns:
        None
    """
    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)
    latents = torch.zeros((1, 4, 64, 64))

    vae_decoder.eval()
    with torch.no_grad():
        ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[((1,4,64,64),)])
    ov.save_model(ov_model, ir_path)
    del ov_model
    cleanup_torchscript_cache()
    print(f'VAE decoder successfully converted to IR and saved to {ir_path}')


if not VAE_DECODER_OV_PATH.exists():
    convert_vae_decoder(vae, VAE_DECODER_OV_PATH)
else:
    print(f"VAE decoder will be loaded from {VAE_DECODER_OV_PATH}")

del vae
gc.collect()

import inspect
from typing import List, Optional, Union, Dict

import PIL
import cv2

from transformers import CLIPTokenizer
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from openvino.runtime import Model


def scale_fit_to_window(dst_width:int, dst_height:int, image_width:int, image_height:int):
    """
    Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
    and fitting image to specific window size

    Parameters:
      dst_width (int): destination window width
      dst_height (int): destination window height
      image_width (int): source image width
      image_height (int): source image height
    Returns:
      result_width (int): calculated width for resize
      result_height (int): calculated height for resize
    """
    im_scale = min(dst_height / image_height, dst_width / image_width)
    return int(im_scale * image_width), int(im_scale * image_height)


def preprocess(image: PIL.Image.Image):
    """
    Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
    then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
    converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
    The function returns preprocessed input tensor and padding size, which can be used in postprocessing.

    Parameters:
      image (PIL.Image.Image): input image
    Returns:
       image (np.ndarray): preprocessed image tensor
       meta (Dict): dictionary with preprocessing metadata info
    """
    src_width, src_height = image.size
    dst_width, dst_height = scale_fit_to_window(
        512, 512, src_width, src_height)
    image = np.array(image.resize((dst_width, dst_height),
                     resample=PIL.Image.Resampling.LANCZOS))[None, :]
    pad_width = 512 - dst_width
    pad_height = 512 - dst_height
    pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
    image = np.pad(image, pad, mode="constant")
    image = image.astype(np.float32) / 255.0
    image = 2.0 * image - 1.0
    image = image.transpose(0, 3, 1, 2)
    return image, {"padding": pad, "src_width": src_width, "src_height": src_height}


class OVStableDiffusionPipeline(DiffusionPipeline):
    def __init__(
        self,
        vae_decoder: Model,
        text_encoder: Model,
        tokenizer: CLIPTokenizer,
        unet: Model,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
        vae_encoder: Model = None,
    ):
        """
        Pipeline for text-to-image generation using Stable Diffusion.
        Parameters:
            vae (Model):
                Variational Auto-Encoder (VAE) Model to decode images to and from latent representations.
            text_encoder (Model):
                Frozen text-encoder. Stable Diffusion uses the text portion of
                [CLIP](https://huggingface.co./docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
                the clip-vit-large-patch14(https://huggingface.co./openai/clip-vit-large-patch14) variant.
            tokenizer (CLIPTokenizer):
                Tokenizer of class CLIPTokenizer(https://huggingface.co./docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
            unet (Model): Conditional U-Net architecture to denoise the encoded image latents.
            scheduler (SchedulerMixin):
                A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of
                DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
        """
        super().__init__()
        self.scheduler = scheduler
        self.vae_decoder = vae_decoder
        self.vae_encoder = vae_encoder
        self.text_encoder = text_encoder
        self.unet = unet
        self._text_encoder_output = text_encoder.output(0)
        self._unet_output = unet.output(0)
        self._vae_d_output = vae_decoder.output(0)
        self._vae_e_output = vae_encoder.output(0) if vae_encoder is not None else None
        self.height = 512
        self.width = 512
        self.tokenizer = tokenizer

    def __call__(
        self,
        prompt: Union[str, List[str]],
        image: PIL.Image.Image = None,
        num_inference_steps: Optional[int] = 50,
        negative_prompt: Union[str, List[str]] = None,
        guidance_scale: Optional[float] = 7.5,
        eta: Optional[float] = 0.0,
        output_type: Optional[str] = "pil",
        seed: Optional[int] = None,
        strength: float = 1.0,
        gif: Optional[bool] = False,
        **kwargs,
    ):
        """
        Function invoked when calling the pipeline for generation.
        Parameters:
            prompt (str or List[str]):
                The prompt or prompts to guide the image generation.
            image (PIL.Image.Image, *optional*, None):
                 Intinal image for generation.
            num_inference_steps (int, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            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.
            eta (float, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [DDIMScheduler], will be ignored for others.
            output_type (`str`, *optional*, defaults to "pil"):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): PIL.Image.Image or np.array.
            seed (int, *optional*, None):
                Seed for random generator state initialization.
            gif (bool, *optional*, False):
                Flag for storing all steps results or not.
        Returns:
            Dictionary with keys:
                sample - the last generated image PIL.Image.Image or np.array
                iterations - *optional* (if gif=True) images for all diffusion steps, List of PIL.Image.Image or np.array.
        """
        if seed is not None:
            np.random.seed(seed)

        img_buffer = []
        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)

        # set timesteps
        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
        extra_set_kwargs = {}
        if accepts_offset:
            extra_set_kwargs["offset"] = 1

        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
        latent_timestep = timesteps[:1]

        # get the initial random noise unless the user supplied it
        latents, meta = self.prepare_latents(image, latent_timestep)

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        for i, t in enumerate(self.progress_bar(timesteps)):
            # expand the latents if you are doing classifier free guidance
            latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

            # predict the noise residual
            noise_pred = self.unet([latent_model_input, t, text_embeddings])[self._unet_output]
            # perform guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            latents = self.scheduler.step(torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs)["prev_sample"].numpy()
            if gif:
                image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
                image = self.postprocess_image(image, meta, output_type)
                img_buffer.extend(image)

        # scale and decode the image latents with vae
        image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]

        image = self.postprocess_image(image, meta, output_type)
        return {"sample": image, 'iterations': img_buffer}

    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 you use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
            if isinstance(self.scheduler, LMSDiscreteScheduler):
                noise = noise * self.scheduler.sigmas[0].numpy()
                return noise, {}
        input_image, meta = preprocess(image)
        latents = self.vae_encoder(input_image)[self._vae_e_output] * 0.18215
        latents = self.scheduler.add_noise(torch.from_numpy(latents), torch.from_numpy(noise), latent_timestep).numpy()
        return latents, meta

    def postprocess_image(self, image:np.ndarray, meta:Dict, output_type:str = "pil"):
        """
        Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
        normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format

        Parameters:
            image (np.ndarray):
                Generated image
            meta (Dict):
                Metadata obtained on latents preparing step, can be empty
            output_type (str, *optional*, pil):
                Output format for result, can be pil or numpy
        Returns:
            image (List of np.ndarray or PIL.Image.Image):
                Postprocessed images
        """
        if "padding" in meta:
            pad = meta["padding"]
            (_, end_h), (_, end_w) = pad[1:3]
            h, w = image.shape[2:]
            unpad_h = h - end_h
            unpad_w = w - end_w
            image = image[:, :, :unpad_h, :unpad_w]
        image = np.clip(image / 2 + 0.5, 0, 1)
        image = np.transpose(image, (0, 2, 3, 1))
        # 9. Convert to PIL
        if output_type == "pil":
            image = self.numpy_to_pil(image)
            if "src_height" in meta:
                orig_height, orig_width = meta["src_height"], meta["src_width"]
                image = [img.resize((orig_width, orig_height),
                                    PIL.Image.Resampling.LANCZOS) for img in image]
        else:
            if "src_height" in meta:
                orig_height, orig_width = meta["src_height"], meta["src_width"]
                image = [cv2.resize(img, (orig_width, orig_width))
                         for img in image]
        return image

    def get_timesteps(self, num_inference_steps:int, strength:float):
        """
        Helper function for getting scheduler timesteps for generation
        In case of image-to-image generation, it updates number of steps according to strength

        Parameters:
           num_inference_steps (int):
              number of inference steps for generation
           strength (float):
               value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
               Values that approach 1.0 enable lots of variations but will also produce images that are not semantically consistent with the input.
        """
        # get the original timestep using init_timestep
        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

        t_start = max(num_inference_steps - init_timestep, 0)
        timesteps = self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start

core = ov.Core()

"""Select device from dropdown list for running inference using OpenVINO."""

import ipywidgets as widgets

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

device

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

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

ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device.value != "CPU" else {}

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

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

from transformers import CLIPTokenizer
from diffusers.schedulers import LMSDiscreteScheduler

lms = LMSDiscreteScheduler(
    beta_start=0.00085,
    beta_end=0.012,
    beta_schedule="scaled_linear"
)
tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')

ov_pipe = OVStableDiffusionPipeline(
    tokenizer=tokenizer,
    text_encoder=text_enc,
    unet=unet_model,
    vae_encoder=vae_encoder,
    vae_decoder=vae_decoder,
    scheduler=lms
)