app.py

from diffusers import StableDiffusionPipeline
import gc
import gradio as gr

pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").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 pv

text_encoder_path=Path("text_encoder.xml")

def cleanup_cache():
    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)
        text_encoder.eval()
        
        with torch.no_grad():
            ov_model=pv.convert_model(text_encoder,example_input=input_ids,input=[(1,77),])
        pv.save_model(ov_model,ir_path)
        del ov_model
        cleanup_cache()
        print(f"Text Encoder successfully converted to TR and saved to {ir_path}")
if not text_encoder_path.exists():
    convert_encoder(text_encoder,text_encoder_path)
else:
    print(f"Text encoder will be loaded from {text_encoder_path}")
del text_encoder
gc.collect()

import numpy as np
unet_path=Path("unet.xml")
dtype_mapping={
    torch.float32: pv.Type.f32,
    torch.float64: pv.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
    """

        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=pv.PartialShape(tuple(input_tensor.shape))
            element_type=dtype_mapping[input_tensor.dtype]
            input_info.append((shape,element_type))
            
        unet.eval()
        with torch.no_grad():
            pv_model=pv.convert_model(unet,example_input=dummy_inputs,input=input_info)
        pv.save_model(pv_model,ir_path)
        del pv_model
        cleanup_cache()
        print(f"Unet successfully converted to IR and saved to {ir_path}")
        
if not unet_path.exists():
    convert_unet(unet,unet_path)
    gc.collect()
else:
    print(f"unet will be loaded from {unet_path}")
del unet
gc.collect()

VAE_ENCODER_PATH = Path("vae_encoder.xml")

def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path):
    class VAEEncoder(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 = VAEEncoder(vae)
    vae_encoder.eval()
    image = torch.zeros((1, 3, 512, 512))
    with torch.no_grad():
        ov_model = pv.convert_model(vae_encoder, example_input=image, input=[((1,3,512,512),)])
    pv.save_model(ov_model, ir_path)
    del ov_model
    cleanup_cache()
    print(f'VAE encoder successfully converted to IR and saved to {ir_path}')


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

VAE_DECODER_PATH = Path('vae_decoder.xml')

def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path):
    class VAEDecoder(torch.nn.Module):
        def __init__(self, vae):
            super().__init__()
            self.vae = vae

        def forward(self, latents):
            return self.vae.decode(latents)
    
    vae_decoder = VAEDecoder(vae)
    latents = torch.zeros((1, 4, 64, 64))

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


if not VAE_DECODER_PATH.exists():
    convert_vae_decoder(vae, VAE_DECODER_PATH)
else:
    print(f"VAE decoder will be loaded from {VAE_DECODER_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_window(dst_width:int,dst_height:int,image_width:int,image_height:int):
    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):
    src_width,src_height=image.size
    dst_width,dst_height=scale_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,
    ):
        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,
    ):
        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=pv.Core()
import ipywidgets as widgets
device=widgets.Dropdown(
    options=core.available_devices+["AUTO"],
    value="CPU",
    desciption="Device:",
    disabled=False,
)
device
text_enc=core.compile_model(text_encoder_path,device.value)
unet_model=core.compile_model(unet_path,device.value)

pv_config={"INFERENCE_PRECISION_HINT":"f32"}if device.value !="CPU" else {}
vae_decoder=core.compile_model(VAE_DECODER_PATH,device.value,pv_config)
vae_encoder=core.compile_model(VAE_ENCODER_PATH,device.value,pv_config)
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")

pv_pipe=OVStableDiffusionPipeline(
    tokenizer=tokenizer,
    text_encoder=text_enc,
    unet=unet_model,
    vae_encoder=vae_encoder,
    vae_decoder=vae_decoder,
    scheduler=lms)
from ipywidgets import widgets
sample_text=("A Dog wearing golden rich mens necklace")
text_prompt=widgets.Text(value=sample_text,description="A Dog wearing golden rich mens necklace ")
num_steps=widgets.IntSlider(min=1,max=50,value=20,description="steps:")
seed=widgets.IntSlider(min=0,max=10000000,description="seed:",value=54)
widgets.VBox([text_prompt,seed,num_steps])
result=pv_pipe(text_prompt.value,num_inference_steps=num_steps.value,seed=seed.value)





def generate_text(text,seed,num_steps,strength,_=gr.Progress(track_tqdm=True)):
    result=pv_pipe(text,num_inference_steps=num_steps,seed=seed)
    return result["sample"][0]
def generate_image(img,text,seed,num_steps,strength,_=gr.Progress(track_tqdm=True)):
    result=pv_pipe(text,img,num_inference_steps=num_steps,seed=seed,strength=strength)
    return result["sample"][0]

with gr.Blocks() as demo:
    with gr.Tab("Zero-shot Text-to-Image Generation"):
        with gr.Row():
            with gr.Column():
                text_input=gr.Textbox(lines=3,label="Text")
                seed_input=gr.Slider(0,10000000,value=42,label="seed")
                steps_input=gr.Slider(1,50,value=20,step=1,label="steps")
            out=gr.Image(label="Result",type="pil")
        btn=gr.Button()
        btn.click(generate_text,[text_input,seed_input,steps_input],out)
        gr.Examples([[sample_text,42,20]],[text_input,seed_input,steps_input])
try:
    demo.queue().launch(debug=True)
except Exception:
    demo.queue().launch(share=True,debug=True)