stable-diffusion-webui/scripts/openvino_accelerate.py

# Copyright (C) 2023 Intel Corporation
# SPDX-License-Identifier: AGPL-3.0

import cv2
import os
import torch
import time
import hashlib
import functools
import gradio as gr
import numpy as np

import modules
import modules.paths as paths
import modules.scripts as scripts

from modules import images, devices, extra_networks, masking, shared
from modules.processing import (
    StableDiffusionProcessing, Processed, apply_overlay, apply_color_correction,
    get_fixed_seed, create_infotext, setup_color_correction,
    process_images
)
from modules.sd_models import CheckpointInfo
from modules.shared import opts, state

from PIL import Image, ImageOps
from pathlib import Path

from openvino.frontend.pytorch.torchdynamo import backend, compile # noqa: F401
from openvino.frontend.pytorch.torchdynamo.execute import execute, partitioned_modules, compiled_cache # noqa: F401
from openvino.frontend.pytorch.torchdynamo.partition import Partitioner
from openvino.runtime import Core, Type, PartialShape

from torch._dynamo.backends.common import fake_tensor_unsupported
from torch._dynamo.backends.registry import register_backend
from torch._inductor.compile_fx import compile_fx
from torch.fx.experimental.proxy_tensor import make_fx

from hashlib import sha256

from diffusers import (
    StableDiffusionPipeline,
    StableDiffusionImg2ImgPipeline,
    StableDiffusionInpaintPipeline,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    HeunDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt

from diffusers.utils import (
    DIFFUSERS_CACHE,
    HF_HUB_OFFLINE,
    is_safetensors_available,
)

class ModelState:
    def __init__(self):
        self.recompile = 1
        self.device = "CPU"
        self.height = 512
        self.width = 512
        self.batch_size = 1
        self.mode = 0
        self.partition_id = 0
        self.model_hash = ""

model_state = ModelState()

@register_backend
@fake_tensor_unsupported
def openvino_fx(subgraph, example_inputs):
    try:
        executor_parameters = None
        core = Core()
        if os.getenv("OPENVINO_TORCH_MODEL_CACHING") is not None:
            model_hash_str = sha256(subgraph.code.encode('utf-8')).hexdigest()
            model_hash_str_file = model_hash_str + str(model_state.partition_id)
            model_state.partition_id = model_state.partition_id + 1
            executor_parameters = {"model_hash_str": model_hash_str}

        example_inputs.reverse()
        cache_root = "./cache/"
        if os.getenv("OPENVINO_TORCH_CACHE_DIR") is not None:
            cache_root = os.getenv("OPENVINO_TORCH_CACHE_DIR")

        device = "CPU"

        if os.getenv("OPENVINO_TORCH_BACKEND_DEVICE") is not None:
            device = os.getenv("OPENVINO_TORCH_BACKEND_DEVICE")
            assert device in core.available_devices, "Specified device " + device + " is not in the list of OpenVINO Available Devices"

        file_name = get_cached_file_name(*example_inputs, model_hash_str=model_hash_str_file, device=device, cache_root=cache_root)

        if file_name is not None and os.path.isfile(file_name + ".xml") and os.path.isfile(file_name + ".bin"):
            om = core.read_model(file_name + ".xml")

            dtype_mapping = {
                torch.float32: Type.f32,
                torch.float64: Type.f64,
                torch.float16: Type.f16,
                torch.int64: Type.i64,
                torch.int32: Type.i32,
                torch.uint8: Type.u8,
                torch.int8: Type.i8,
                torch.bool: Type.boolean
            }

            for idx, input_data in enumerate(example_inputs):
                om.inputs[idx].get_node().set_element_type(dtype_mapping[input_data.dtype])
                om.inputs[idx].get_node().set_partial_shape(PartialShape(list(input_data.shape)))
            om.validate_nodes_and_infer_types()

            if model_hash_str is not None:
                core.set_property({'CACHE_DIR': cache_root + '/blob'})

            compiled_model = core.compile_model(om, device)
            def _call(*args):
                ov_inputs = [a.detach().cpu().numpy() for a in args]
                ov_inputs.reverse()
                res = compiled_model(ov_inputs)
                result = [torch.from_numpy(res[out]) for out in compiled_model.outputs]
                return result
            return _call
        else:
            example_inputs.reverse()
            model = make_fx(subgraph)(*example_inputs)
            with torch.no_grad():
                model.eval()
            partitioner = Partitioner()
            compiled_model = partitioner.make_partitions(model)

            def _call(*args):
                res = execute(compiled_model, *args, executor="openvino",
                              executor_parameters=executor_parameters)
                return res
            return _call
    except Exception:
        return compile_fx(subgraph, example_inputs)

def get_cached_file_name(*args, model_hash_str, device, cache_root):
    file_name = None
    if model_hash_str is not None:
        model_cache_dir = cache_root + "/model/"
        try:
            os.makedirs(model_cache_dir, exist_ok=True)
            file_name = model_cache_dir + model_hash_str + "_" + device
            for input_data in args:
                if file_name is not None:
                    file_name += "_" + str(input_data.type()) + str(input_data.size())[11:-1].replace(" ", "")
        except OSError as error:
            print("Cache directory ", cache_root, " cannot be created. Model caching is disabled. Error: ", error)
            file_name = None
            model_hash_str = None
    return file_name

def from_single_file(self, pretrained_model_link_or_path, **kwargs):

    cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
    resume_download = kwargs.pop("resume_download", False)
    force_download = kwargs.pop("force_download", False)
    proxies = kwargs.pop("proxies", None)
    local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
    use_auth_token = kwargs.pop("use_auth_token", None)
    revision = kwargs.pop("revision", None)
    extract_ema = kwargs.pop("extract_ema", False)
    image_size = kwargs.pop("image_size", None)
    scheduler_type = kwargs.pop("scheduler_type", "pndm")
    num_in_channels = kwargs.pop("num_in_channels", None)
    upcast_attention = kwargs.pop("upcast_attention", None)
    load_safety_checker = kwargs.pop("load_safety_checker", True)
    prediction_type = kwargs.pop("prediction_type", None)
    text_encoder = kwargs.pop("text_encoder", None)
    tokenizer = kwargs.pop("tokenizer", None)
    local_config_file = kwargs.pop("local_config_file", None)

    torch_dtype = kwargs.pop("torch_dtype", None)

    use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False)

    pipeline_name = self.__name__
    file_extension = pretrained_model_link_or_path.rsplit(".", 1)[-1]
    from_safetensors = file_extension == "safetensors"

    if from_safetensors and use_safetensors is False:
        raise ValueError("Make sure to install `safetensors` with `pip install safetensors`.")

    # TODO: For now we only support stable diffusion
    stable_unclip = None
    model_type = None
    controlnet = False

    if pipeline_name == "StableDiffusionControlNetPipeline":
        # Model type will be inferred from the checkpoint.
        controlnet = True
    elif "StableDiffusion" in pipeline_name:
        # Model type will be inferred from the checkpoint.
        pass
    elif pipeline_name == "StableUnCLIPPipeline":
        model_type = "FrozenOpenCLIPEmbedder"
        stable_unclip = "txt2img"
    elif pipeline_name == "StableUnCLIPImg2ImgPipeline":
        model_type = "FrozenOpenCLIPEmbedder"
        stable_unclip = "img2img"
    elif pipeline_name == "PaintByExamplePipeline":
        model_type = "PaintByExample"
    elif pipeline_name == "LDMTextToImagePipeline":
        model_type = "LDMTextToImage"
    else:
        raise ValueError(f"Unhandled pipeline class: {pipeline_name}")

    # remove huggingface url
    for prefix in ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]:
        if pretrained_model_link_or_path.startswith(prefix):
            pretrained_model_link_or_path = pretrained_model_link_or_path[len(prefix) :]
    # Code based on diffusers.pipelines.pipeline_utils.DiffusionPipeline.from_pretrained
    ckpt_path = Path(pretrained_model_link_or_path)
    if not ckpt_path.is_file():
        # get repo_id and (potentially nested) file path of ckpt in repo
        repo_id = "/".join(ckpt_path.parts[:2])
        file_path = "/".join(ckpt_path.parts[2:])

        if file_path.startswith("blob/"):
            file_path = file_path[len("blob/") :]

        if file_path.startswith("main/"):
            file_path = file_path[len("main/") :]

        from huggingface_hub import hf_hub_download
        pretrained_model_link_or_path = hf_hub_download(
            repo_id,
            filename=file_path,
            cache_dir=cache_dir,
            resume_download=resume_download,
            proxies=proxies,
            local_files_only=local_files_only,
            use_auth_token=use_auth_token,
            revision=revision,
            force_download=force_download,
        )

    pipe = download_from_original_stable_diffusion_ckpt(
        pretrained_model_link_or_path,
        original_config_file=local_config_file,
        pipeline_class=self,
        model_type=model_type,
        stable_unclip=stable_unclip,
        controlnet=controlnet,
        from_safetensors=from_safetensors,
        extract_ema=extract_ema,
        image_size=image_size,
        scheduler_type=scheduler_type,
        num_in_channels=num_in_channels,
        upcast_attention=upcast_attention,
        load_safety_checker=load_safety_checker,
        prediction_type=prediction_type,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
    )

    if torch_dtype is not None:
        pipe.to(torch_dtype=torch_dtype)

    return pipe

StableDiffusionPipeline.from_single_file = functools.partial(from_single_file, StableDiffusionPipeline)

def openvino_clear_caches():
    global partitioned_modules
    global compiled_cache

    compiled_cache.clear()
    partitioned_modules.clear()

def sd_diffusers_model(self):
    import modules.sd_models
    return modules.sd_models.model_data.get_sd_model()

def cond_stage_key(self):
    return None

shared.sd_diffusers_model = sd_diffusers_model

def set_scheduler(sd_model, sampler_name):
    if (sampler_name == "Euler a"):
        sd_model.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_model.scheduler.config)
    elif (sampler_name == "Euler"):
        sd_model.scheduler = EulerDiscreteScheduler.from_config(sd_model.scheduler.config)
    elif (sampler_name == "LMS"):
        sd_model.scheduler = LMSDiscreteScheduler.from_config(sd_model.scheduler.config)
    elif (sampler_name == "Heun"):
        sd_model.scheduler = HeunDiscreteScheduler.from_config(sd_model.scheduler.config)
    elif (sampler_name == "DPM++ 2M"):
        sd_model.scheduler = DPMSolverMultistepScheduler.from_config(sd_model.scheduler.config, algorithm_type="dpmsolver++", use_karras_sigmas=False)
    elif (sampler_name == "LMS Karras"):
        sd_model.scheduler = LMSDiscreteScheduler.from_config(sd_model.scheduler.config, use_karras_sigmas=True)
    elif (sampler_name == "DPM++ 2M Karras"):
        sd_model.scheduler = DPMSolverMultistepScheduler.from_config(sd_model.scheduler.config, algorithm_type="dpmsolver++", use_karras_sigmas=True)
    elif (sampler_name == "DDIM"):
        sd_model.scheduler = DDIMScheduler.from_config(sd_model.scheduler.config)
    elif (sampler_name == "PLMS"):
        sd_model.scheduler = PNDMScheduler.from_config(sd_model.scheduler.config)
    else:
        sd_model.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_model.scheduler.config)

    return sd_model.scheduler

def get_diffusers_sd_model(local_config, model_config, sampler_name, enable_caching, openvino_device, mode):
    if (model_state.recompile == 1):
        torch._dynamo.reset()
        openvino_clear_caches()
        curr_dir_path = os.getcwd()
        checkpoint_name = shared.opts.sd_model_checkpoint.split(" ")[0]
        checkpoint_path = os.path.join(curr_dir_path, 'models', 'Stable-diffusion', checkpoint_name)
        if local_config:
            local_config_file = os.path.join(curr_dir_path, 'configs', model_config)
            sd_model = StableDiffusionPipeline.from_single_file(checkpoint_path, local_config_file=local_config_file, load_safety_checker=False)
        else:
            sd_model = StableDiffusionPipeline.from_single_file(checkpoint_path, load_safety_checker=False, torch_dtype=torch.float32)
        if (mode == 1):
            sd_model = StableDiffusionImg2ImgPipeline(**sd_model.components)
        elif (mode == 2):
            sd_model = StableDiffusionInpaintPipeline(**sd_model.components)
        checkpoint_info = CheckpointInfo(checkpoint_path)
        sd_model.sd_checkpoint_info = checkpoint_info
        sd_model.sd_model_hash = checkpoint_info.calculate_shorthash()
        sd_model.safety_checker = None
        sd_model.cond_stage_key = functools.partial(cond_stage_key, shared.sd_model)
        sd_model.scheduler = set_scheduler(sd_model, sampler_name)
        sd_model.unet = torch.compile(sd_model.unet, backend="openvino_fx")
        sd_model.vae.decode = torch.compile(sd_model.vae.decode, backend="openvino_fx")
        shared.sd_diffusers_model = sd_model
        del sd_model
    return shared.sd_diffusers_model


def init_new(self, all_prompts, all_seeds, all_subseeds):
    crop_region = None

    image_mask = self.image_mask

    if image_mask is not None:
        image_mask = image_mask.convert('L')

        if self.inpainting_mask_invert:
            image_mask = ImageOps.invert(image_mask)

        if self.mask_blur_x > 0:
            np_mask = np.array(image_mask)
            kernel_size = 2 * int(4 * self.mask_blur_x + 0.5) + 1
            np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
            image_mask = Image.fromarray(np_mask)

        if self.mask_blur_y > 0:
            np_mask = np.array(image_mask)
            kernel_size = 2 * int(4 * self.mask_blur_y + 0.5) + 1
            np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
            image_mask = Image.fromarray(np_mask)

        if self.inpaint_full_res:
            self.mask_for_overlay = image_mask
            mask = image_mask.convert('L')
            crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
            crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
            x1, y1, x2, y2 = crop_region

            mask = mask.crop(crop_region)
            image_mask = images.resize_image(2, mask, self.width, self.height)
            self.paste_to = (x1, y1, x2-x1, y2-y1)
        else:
            image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
            np_mask = np.array(image_mask)
            np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
            self.mask_for_overlay = Image.fromarray(np_mask)

        self.overlay_images = []

    latent_mask = self.latent_mask if self.latent_mask is not None else image_mask

    add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
    if add_color_corrections:
        self.color_corrections = []
    imgs = []
    for img in self.init_images:
        # Save init image
        if opts.save_init_img:
            self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
            images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)

        image = images.flatten(img, opts.img2img_background_color)

        if crop_region is None and self.resize_mode != 3:
            image = images.resize_image(self.resize_mode, image, self.width, self.height)

        if image_mask is not None:
            image_masked = Image.new('RGBa', (image.width, image.height))
            image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
            self.mask = image_mask
            self.overlay_images.append(image_masked.convert('RGBA'))

        # crop_region is not None if we are doing inpaint full res
        if crop_region is not None:
            image = image.crop(crop_region)
            image = images.resize_image(2, image, self.width, self.height)

        self.init_images = image
        if image_mask is not None:
            if self.inpainting_fill != 1:
                image = masking.fill(image, latent_mask)

        if add_color_corrections:
            self.color_corrections.append(setup_color_correction(image))

        image = np.array(image).astype(np.float32) / 255.0
        image = np.moveaxis(image, 2, 0)

        imgs.append(image)

    if len(imgs) == 1:
        if self.overlay_images is not None:
            self.overlay_images = self.overlay_images * self.batch_size

        if self.color_corrections is not None and len(self.color_corrections) == 1:
            self.color_corrections = self.color_corrections * self.batch_size

    elif len(imgs) <= self.batch_size:
        self.batch_size = len(imgs)
    else:
        raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")


def process_images_openvino(p: StableDiffusionProcessing, local_config, model_config, sampler_name, enable_caching, openvino_device, mode) -> Processed:
    """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""

    if (mode == 0 and p.enable_hr):
        return process_images(p)

    if type(p.prompt) == list:
        assert(len(p.prompt) > 0)
    else:
        assert p.prompt is not None

    devices.torch_gc()

    seed = get_fixed_seed(p.seed)
    subseed = get_fixed_seed(p.subseed)

    comments = {}
    custom_inputs = {}

    p.setup_prompts()

    if type(seed) == list:
        p.all_seeds = seed
    else:
        p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]

    if type(subseed) == list:
        p.all_subseeds = subseed
    else:
        p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]

    def infotext(iteration=0, position_in_batch=0):
        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)

    if p.scripts is not None:
        p.scripts.process(p)

    if 'ControlNet' in p.extra_generation_params:
        return process_images(p)

    infotexts = []
    output_images = []

    with torch.no_grad():
        with devices.autocast():
            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)

        if state.job_count == -1:
            state.job_count = p.n_iter

        extra_network_data = None
        for n in range(p.n_iter):
            p.iteration = n

            if state.skipped:
                state.skipped = False

            if state.interrupted:
                break

            p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
            p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
            p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
            p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]

            if p.scripts is not None:
                p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)

            if len(p.prompts) == 0:
                break

            if (model_state.height != p.height or model_state.width != p.width or model_state.batch_size != p.batch_size
                    or model_state.mode != mode or model_state.model_hash != shared.sd_model.sd_model_hash):
                model_state.recompile = 1
                model_state.height = p.height
                model_state.width = p.width
                model_state.batch_size = p.batch_size
                model_state.mode = mode
                model_state.model_hash = shared.sd_model.sd_model_hash

            shared.sd_diffusers_model = get_diffusers_sd_model(local_config, model_config, sampler_name, enable_caching, openvino_device, mode)
            shared.sd_diffusers_model.scheduler = set_scheduler(shared.sd_diffusers_model, sampler_name)

            extra_network_data = p.parse_extra_network_prompts()

            if not p.disable_extra_networks:
                with devices.autocast():
                    extra_networks.activate(p, p.extra_network_data)

            if ('lora' in modules.extra_networks.extra_network_registry):
                import lora
                # TODO: multiple Loras aren't supported for Diffusers now, needs to add warning
                if lora.loaded_loras:
                    lora_model = lora.loaded_loras[0]
                    shared.sd_diffusers_model.load_lora_weights(os.path.join(os.getcwd(), "models", "Lora"), weight_name=lora_model.name + ".safetensors")
                    custom_inputs.update(cross_attention_kwargs={"scale" : lora_model.te_multiplier})

            if p.scripts is not None:
                p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)

            # params.txt should be saved after scripts.process_batch, since the
            # infotext could be modified by that callback
            # Example: a wildcard processed by process_batch sets an extra model
            # strength, which is saved as "Model Strength: 1.0" in the infotext
            if n == 0:
                with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
                    file.write(create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments=[], position_in_batch=0 % p.batch_size, iteration=0 // p.batch_size))

            if p.n_iter > 1:
                shared.state.job = f"Batch {n+1} out of {p.n_iter}"

            generator = [torch.Generator(device="cpu").manual_seed(s) for s in p.seeds]

            time_stamps = []

            def callback(iter, t, latents):
                time_stamps.append(time.time()) # noqa: B023

            time_stamps.append(time.time())

            if (mode == 0):
                custom_inputs.update({
                    'width': p.width,
                    'height': p.height,
                })
            elif (mode == 1):
                custom_inputs.update({
                    'image': p.init_images,
                    'strength':p.denoising_strength,
                })
            else:
                custom_inputs.update({
                    'image': p.init_images,
                    'strength':p.denoising_strength,
                    'mask_image': p.mask,
                })
            output = shared.sd_diffusers_model(
                    prompt=p.prompts,
                    negative_prompt=p.negative_prompts,
                    num_inference_steps=p.steps,
                    guidance_scale=p.cfg_scale,
                    generator=generator,
                    output_type="np",
                    callback = callback,
                    callback_steps = 1,
                    **custom_inputs
            )

            model_state.recompile = 0

            warmup_duration = time_stamps[1] - time_stamps[0]
            generation_rate = (p.steps - 1) / (time_stamps[-1] - time_stamps[1])

            x_samples_ddim = output.images

            for i, x_sample in enumerate(x_samples_ddim):
                p.batch_index = i

                x_sample = (255. * x_sample).astype(np.uint8)

                if p.restore_faces:
                    if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")

                    devices.torch_gc()

                    x_sample = modules.face_restoration.restore_faces(x_sample)
                    devices.torch_gc()

                image = Image.fromarray(x_sample)

                if p.scripts is not None:
                    pp = scripts.PostprocessImageArgs(image)
                    p.scripts.postprocess_image(p, pp)
                    image = pp.image

                if p.color_corrections is not None and i < len(p.color_corrections):
                    if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
                    image = apply_color_correction(p.color_corrections[i], image)

                image = apply_overlay(image, p.paste_to, i, p.overlay_images)

                if opts.samples_save and not p.do_not_save_samples:
                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p)

                text = infotext(n, i)
                infotexts.append(text)
                if opts.enable_pnginfo:
                    image.info["parameters"] = text
                output_images.append(image)

                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
                    image_mask = p.mask_for_overlay.convert('RGB')
                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')

                    if opts.save_mask:
                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")

                    if opts.save_mask_composite:
                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")

                    if opts.return_mask:
                        output_images.append(image_mask)

                    if opts.return_mask_composite:
                        output_images.append(image_mask_composite)

            del x_samples_ddim

            devices.torch_gc()

            state.nextjob()

        p.color_corrections = None

        index_of_first_image = 0
        unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
        if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
            grid = images.image_grid(output_images, p.batch_size)

            if opts.return_grid:
                text = infotext()
                infotexts.insert(0, text)
                if opts.enable_pnginfo:
                    grid.info["parameters"] = text
                output_images.insert(0, grid)
                index_of_first_image = 1

            if opts.grid_save:
                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)

    if not p.disable_extra_networks and extra_network_data:
        extra_networks.deactivate(p, p.extra_network_data)

    devices.torch_gc()

    res = Processed(
        p,
        images_list=output_images,
        seed=p.all_seeds[0],
        info=infotext(),
        comments="".join(f"{comment}\n" for comment in comments),
        subseed=p.all_subseeds[0],
        index_of_first_image=index_of_first_image,
        infotexts=infotexts,
    )

    res.info = res.info + ", Warm up time: " + str(round(warmup_duration, 2)) + " secs "

    if (generation_rate >= 1.0):
        res.info = res.info + ", Performance: " + str(round(generation_rate, 2)) + " it/s "
    else:
        res.info = res.info + ", Performance: " + str(round(1/generation_rate, 2)) + " s/it "


    if p.scripts is not None:
        p.scripts.postprocess(p, res)

    return res

class Script(scripts.Script):
    def title(self):
        return "Accelerate with OpenVINO"

    def show(self, is_img2img):
        return True

    def ui(self, is_img2img):
        core = Core()
        config_dir_list = os.listdir(os.path.join(os.getcwd(), 'configs'))

        config_list = []
        for file in config_dir_list:
            if file.endswith('.yaml'):
                config_list.append(file)

        local_config = gr.Checkbox(label="Use a local inference config file", value=False)
        model_config = gr.Dropdown(label="Select a config for the model (Below config files are listed from the configs directory of the WebUI root)", choices=config_list, value="v1-inference.yaml", visible=False)
        openvino_device = gr.Dropdown(label="Select a device", choices=list(core.available_devices), value=model_state.device)
        override_sampler = gr.Checkbox(label="Override the sampling selection from the main UI (Recommended as only below sampling methods have been validated for OpenVINO)", value=True)
        sampler_name = gr.Radio(label="Select a sampling method", choices=["Euler a", "Euler", "LMS", "Heun", "DPM++ 2M", "LMS Karras", "DPM++ 2M Karras", "DDIM", "PLMS"], value="Euler a")
        enable_caching = gr.Checkbox(label="Cache the compiled models on disk for faster model load in subsequent launches (Recommended)", value=True, elem_id=self.elem_id("enable_caching"))
        warmup_status = gr.Textbox(label="Device", interactive=False, visible=False)
        gr.Markdown(
        """
        ###
        ### Note:
        - First inference involves compilation of the model for best performance.
        Since compilation happens only on the first run, the first inference (or warm up inference) will be slower than subsequent inferences.
        - For accurate performance measurements, it is recommended to exclude this slower first inference, as it doesn't reflect normal running time.
        - Model is recompiled when resolution, batchsize, device, or samplers like DPM++ or Karras are changed.
        After recompiling, later inferences will reuse the newly compiled model and achieve faster running times.
        So it's normal for the first inference after a settings change to be slower, while subsequent inferences use the optimized compiled model and run faster.
        """)

        def local_config_change(choice):
            if choice:
                return gr.update(visible=True)
            else:
                return gr.update(visible=False)
        local_config.change(local_config_change, local_config, model_config)

        def device_change(choice):
            if (model_state.device == choice):
                return gr.update(value="Device selected is " + choice, visible=True)
            else:
                model_state.device = choice
                model_state.recompile = 1
                return gr.update(value="Device changed to " + choice + ". Model will be re-compiled", visible=True)
        openvino_device.change(device_change, openvino_device, warmup_status)

        return [local_config, model_config, openvino_device, override_sampler, sampler_name, enable_caching]

    def run(self, p, local_config, model_config, openvino_device, override_sampler, sampler_name, enable_caching):
        model_state.partition_id = 0
        os.environ["OPENVINO_TORCH_BACKEND_DEVICE"] = str(openvino_device)

        if enable_caching:
            os.environ["OPENVINO_TORCH_MODEL_CACHING"] = "1"

        if override_sampler:
            p.sampler_name = sampler_name
        else:
            supported_samplers = ["Euler a", "Euler", "LMS", "Heun", "DPM++ 2M", "LMS Karras", "DPM++ 2M Karras", "DDIM", "PLMS"]
            if (p.sampler_name not in supported_samplers):
                p.sampler_name = "Euler a"

        # mode can be 0, 1, 2 corresponding to txt2img, img2img, inpaint respectively
        mode = 0
        if self.is_txt2img:
            mode = 0
            processed = process_images_openvino(p, local_config, model_config, p.sampler_name, enable_caching, openvino_device, mode)
        else:
            if p.image_mask is None:
                mode = 1
            else:
                mode = 2
            p.init = functools.partial(init_new, p)
            processed = process_images_openvino(p, local_config, model_config, p.sampler_name, enable_caching, openvino_device, mode)
        return processed