# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import inspect
from math import ceil
from typing import Callable, List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import PIL_INTERPOLATION, deprecate, logging
from transformers import CLIPImageProcessor, CLIPTokenizer

from ...chain.tile import make_tile_mask
from ...constants import LATENT_CHANNELS, LATENT_FACTOR
from ...params import Size
from ..utils import (
    expand_latents,
    parse_regions,
    random_seed,
    repair_nan,
    resize_latent_shape,
)

logger = logging.get_logger(__name__)


# inpaint constants
NUM_UNET_INPUT_CHANNELS = 9

DEFAULT_WINDOW = 32
DEFAULT_STRIDE = 8


def preprocess(image):
    if isinstance(image, torch.Tensor):
        return image
    elif isinstance(image, PIL.Image.Image):
        image = [image]

    if isinstance(image[0], PIL.Image.Image):
        w, h = image[0].size
        w, h = (x - x % 64 for x in (w, h))  # resize to integer multiple of 64

        image = [
            np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
            for i in image
        ]
        image = np.concatenate(image, axis=0)
        image = np.array(image).astype(np.float32) / 255.0
        image = image.transpose(0, 3, 1, 2)
        image = 2.0 * image - 1.0
        image = torch.from_numpy(image)
    elif isinstance(image[0], torch.Tensor):
        image = torch.cat(image, dim=0)
    return image


def prepare_mask_and_masked_image(image, mask, latents_shape):
    image = np.array(
        image.convert("RGB").resize((latents_shape[1] * 8, latents_shape[0] * 8))
    )
    image = image[None].transpose(0, 3, 1, 2)
    image = image.astype(np.float32) / 127.5 - 1.0

    image_mask = np.array(
        mask.convert("L").resize((latents_shape[1] * 8, latents_shape[0] * 8))
    )
    masked_image = image * (image_mask < 127.5)

    mask = mask.resize(
        (latents_shape[1], latents_shape[0]), PIL_INTERPOLATION["nearest"]
    )
    mask = np.array(mask.convert("L"))
    mask = mask.astype(np.float32) / 255.0
    mask = mask[None, None]
    mask[mask < 0.5] = 0
    mask[mask >= 0.5] = 1

    return mask, masked_image


class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
    vae_encoder: OnnxRuntimeModel
    vae_decoder: OnnxRuntimeModel
    text_encoder: OnnxRuntimeModel
    tokenizer: CLIPTokenizer
    unet: OnnxRuntimeModel
    scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
    safety_checker: OnnxRuntimeModel
    feature_extractor: CLIPImageProcessor

    _optional_components = ["safety_checker", "feature_extractor"]

    def __init__(
        self,
        vae_encoder: OnnxRuntimeModel,
        vae_decoder: OnnxRuntimeModel,
        text_encoder: OnnxRuntimeModel,
        tokenizer: CLIPTokenizer,
        unet: OnnxRuntimeModel,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
        safety_checker: OnnxRuntimeModel,
        feature_extractor: CLIPImageProcessor,
        requires_safety_checker: bool = True,
        window: Optional[int] = None,
        stride: Optional[int] = None,
    ):
        super().__init__()

        self.window = window or DEFAULT_WINDOW
        self.stride = stride or DEFAULT_STRIDE

        if (
            hasattr(scheduler.config, "steps_offset")
            and scheduler.config.steps_offset != 1
        ):
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                " file"
            )
            deprecate(
                "steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False
            )
            new_config = dict(scheduler.config)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

        if (
            hasattr(scheduler.config, "clip_sample")
            and scheduler.config.clip_sample is True
        ):
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
            )
            deprecate(
                "clip_sample not set", "1.0.0", deprecation_message, standard_warn=False
            )
            new_config = dict(scheduler.config)
            new_config["clip_sample"] = False
            scheduler._internal_dict = FrozenDict(new_config)

        if safety_checker is None and requires_safety_checker:
            logger.warning(
                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
            )

        if safety_checker is not None and feature_extractor is None:
            raise ValueError(
                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

        self.register_modules(
            vae_encoder=vae_encoder,
            vae_decoder=vae_decoder,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
        self.register_to_config(requires_safety_checker=requires_safety_checker)

    def _encode_prompt(
        self,
        prompt: Union[str, List[str]],
        num_images_per_prompt: Optional[int],
        do_classifier_free_guidance: bool,
        negative_prompt: Optional[str],
        prompt_embeds: Optional[np.ndarray] = None,
        negative_prompt_embeds: Optional[np.ndarray] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            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]`):
                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
                if `guidance_scale` is less than `1`).
            prompt_embeds (`np.ndarray`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`np.ndarray`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
        """
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if prompt_embeds is None:
            # get prompt text embeddings
            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
            untruncated_ids = self.tokenizer(
                prompt, padding="max_length", return_tensors="np"
            ).input_ids

            if not np.array_equal(text_input_ids, untruncated_ids):
                removed_text = self.tokenizer.batch_decode(
                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
                )
                logger.warning(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )

            prompt_embeds = self.text_encoder(
                input_ids=text_input_ids.astype(np.int32)
            )[0]

        prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt] * batch_size
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="np",
            )
            negative_prompt_embeds = self.text_encoder(
                input_ids=uncond_input.input_ids.astype(np.int32)
            )[0]

        if do_classifier_free_guidance:
            negative_prompt_embeds = np.repeat(
                negative_prompt_embeds, num_images_per_prompt, axis=0
            )

            # 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
            prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])

        return prompt_embeds

    def check_inputs(
        self,
        prompt: Union[str, List[str]],
        height: Optional[int],
        width: Optional[int],
        callback_steps: int,
        negative_prompt: Optional[str] = None,
        prompt_embeds: Optional[np.ndarray] = None,
        negative_prompt_embeds: Optional[np.ndarray] = None,
    ):
        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(
                f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
            )

        if (callback_steps is None) or (
            callback_steps is not None
            and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (
            not isinstance(prompt, str) and not isinstance(prompt, list)
        ):
            raise ValueError(
                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
            )

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

    def get_views(
        self, panorama_height: int, panorama_width: int, window_size: int, stride: int
    ) -> Tuple[List[Tuple[int, int, int, int]], Tuple[int, int]]:
        # Here, we define the mappings F_i (see Eq. 7 in the MultiDiffusion paper https://arxiv.org/abs/2302.08113)
        panorama_height /= 8
        panorama_width /= 8

        num_blocks_height = ceil(abs((panorama_height - window_size) / stride)) + 1
        num_blocks_width = ceil(abs((panorama_width - window_size) / stride)) + 1

        total_num_blocks = int(num_blocks_height * num_blocks_width)
        logger.debug(
            "panorama generated %s views, %s by %s blocks",
            total_num_blocks,
            num_blocks_height,
            num_blocks_width,
        )

        views = []
        for i in range(total_num_blocks):
            h_start = int((i // num_blocks_width) * stride)
            h_end = h_start + window_size
            w_start = int((i % num_blocks_width) * stride)
            w_end = w_start + window_size
            views.append((h_start, h_end, w_start, w_end))

        return (views, (h_end * 8, w_end * 8))

    @torch.no_grad()
    def text2img(
        self,
        prompt: Union[str, List[str]] = None,
        height: Optional[int] = 512,
        width: Optional[int] = 512,
        num_inference_steps: Optional[int] = 50,
        guidance_scale: Optional[float] = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: Optional[float] = 0.0,
        generator: Optional[np.random.RandomState] = None,
        latents: Optional[np.ndarray] = None,
        prompt_embeds: Optional[np.ndarray] = None,
        negative_prompt_embeds: Optional[np.ndarray] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
        callback_steps: int = 1,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`):
                `Image`, or tensor representing an image batch which will be upscaled. *
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
                is less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`np.random.RandomState`, *optional*):
                One or a list of [numpy generator(s)](TODO) to make generation deterministic.
            latents (`np.ndarray`, *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 will ge generated by sampling using the supplied random `generator`.
            prompt_embeds (`np.ndarray`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`np.ndarray`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            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`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """

        # check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            height,
            width,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
        )

        # define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if generator is None:
            generator = np.random

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        prompt, regions = parse_regions(prompt)

        prompt_embeds = self._encode_prompt(
            prompt,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )

        # 3.b. Encode region prompts
        region_embeds: List[np.ndarray] = []

        for _top, _left, _bottom, _right, _weight, _feather, region_prompt in regions:
            if region_prompt.endswith("+"):
                region_prompt = region_prompt[:-1] + " " + prompt

            region_prompt_embeds = self._encode_prompt(
                region_prompt,
                num_images_per_prompt,
                do_classifier_free_guidance,
                negative_prompt,
            )

            region_embeds.append(region_prompt_embeds)

        # get the initial random noise unless the user supplied it
        latents_dtype = prompt_embeds.dtype
        latents_shape = (
            batch_size * num_images_per_prompt,
            LATENT_CHANNELS,
            height // LATENT_FACTOR,
            width // LATENT_FACTOR,
        )
        if latents is None:
            latents = generator.randn(*latents_shape).astype(latents_dtype)
        elif latents.shape != latents_shape:
            raise ValueError(
                f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"
            )

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps)

        latents = latents * np.float64(self.scheduler.init_noise_sigma)

        # 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

        timestep_dtype = next(
            (
                input.type
                for input in self.unet.model.get_inputs()
                if input.name == "timestep"
            ),
            "tensor(float)",
        )
        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]

        # panorama additions
        views, resize = self.get_views(height, width, self.window, self.stride)
        logger.trace("panorama resized latents to %s", resize)

        count = np.zeros(resize_latent_shape(latents, resize))
        value = np.zeros(resize_latent_shape(latents, resize))

        # adjust latents
        latents = expand_latents(
            latents,
            random_seed(generator),
            Size(resize[1], resize[0]),
            sigma=self.scheduler.init_noise_sigma,
        )

        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
            last = i == (len(self.scheduler.timesteps) - 1)
            count.fill(0)
            value.fill(0)

            for h_start, h_end, w_start, w_end in views:
                # get the latents corresponding to the current view coordinates
                latents_for_view = latents[:, :, h_start:h_end, w_start:w_end]

                # expand the latents if we are doing classifier free guidance
                latent_model_input = (
                    np.concatenate([latents_for_view] * 2)
                    if do_classifier_free_guidance
                    else latents_for_view
                )
                latent_model_input = self.scheduler.scale_model_input(
                    torch.from_numpy(latent_model_input), t
                )
                latent_model_input = latent_model_input.cpu().numpy()

                # predict the noise residual
                timestep = np.array([t], dtype=timestep_dtype)
                noise_pred = self.unet(
                    sample=latent_model_input,
                    timestep=timestep,
                    encoder_hidden_states=prompt_embeds,
                )
                noise_pred = noise_pred[0]

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
                    noise_pred = noise_pred_uncond + guidance_scale * (
                        noise_pred_text - noise_pred_uncond
                    )

                # compute the previous noisy sample x_t -> x_t-1
                scheduler_output = self.scheduler.step(
                    torch.from_numpy(noise_pred),
                    t,
                    torch.from_numpy(latents_for_view),
                    **extra_step_kwargs,
                )
                latents_view_denoised = scheduler_output.prev_sample.numpy()

                value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                count[:, :, h_start:h_end, w_start:w_end] += 1

            if not last:
                for r, region in enumerate(regions):
                    top, left, bottom, right, weight, feather, prompt = region
                    logger.debug(
                        "running region prompt: %s, %s, %s, %s, %s, %s, %s",
                        top,
                        left,
                        bottom,
                        right,
                        weight,
                        feather,
                        prompt,
                    )

                    # convert coordinates to latent space
                    h_start = top // LATENT_FACTOR
                    h_end = bottom // LATENT_FACTOR
                    w_start = left // LATENT_FACTOR
                    w_end = right // LATENT_FACTOR

                    # get the latents corresponding to the current view coordinates
                    latents_for_region = latents[:, :, h_start:h_end, w_start:w_end]
                    logger.trace(
                        "region latent shape: [:,:,%s:%s,%s:%s] -> %s",
                        h_start,
                        h_end,
                        w_start,
                        w_end,
                        latents_for_region.shape,
                    )

                    # expand the latents if we are doing classifier free guidance
                    latent_region_input = (
                        np.concatenate([latents_for_region] * 2)
                        if do_classifier_free_guidance
                        else latents_for_region
                    )
                    latent_region_input = self.scheduler.scale_model_input(
                        torch.from_numpy(latent_region_input), t
                    )
                    latent_region_input = latent_region_input.cpu().numpy()

                    # predict the noise residual
                    timestep = np.array([t], dtype=timestep_dtype)
                    region_noise_pred = self.unet(
                        sample=latent_region_input,
                        timestep=timestep,
                        encoder_hidden_states=region_embeds[r],
                    )
                    region_noise_pred = region_noise_pred[0]

                    # perform guidance
                    if do_classifier_free_guidance:
                        region_noise_pred_uncond, region_noise_pred_text = np.split(
                            region_noise_pred, 2
                        )
                        region_noise_pred = (
                            region_noise_pred_uncond
                            + guidance_scale
                            * (region_noise_pred_text - region_noise_pred_uncond)
                        )

                    # compute the previous noisy sample x_t -> x_t-1
                    scheduler_output = self.scheduler.step(
                        torch.from_numpy(region_noise_pred),
                        t,
                        torch.from_numpy(latents_for_region),
                        **extra_step_kwargs,
                    )
                    latents_region_denoised = scheduler_output.prev_sample.numpy()

                    if feather[0] > 0.0:
                        mask = make_tile_mask(
                            (h_end - h_start, w_end - w_start),
                            (h_end - h_start, w_end - w_start),
                            feather[0],
                            feather[1],
                        )
                        mask = np.expand_dims(mask, axis=0)
                        mask = np.repeat(mask, 4, axis=0)
                        mask = np.expand_dims(mask, axis=0)
                    else:
                        mask = 1

                    if weight >= 100.0:
                        value[:, :, h_start:h_end, w_start:w_end] = (
                            latents_region_denoised * mask
                        )
                        count[:, :, h_start:h_end, w_start:w_end] = mask
                    else:
                        value[:, :, h_start:h_end, w_start:w_end] += (
                            latents_region_denoised * weight * mask
                        )
                        count[:, :, h_start:h_end, w_start:w_end] += weight * mask

            # take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113
            latents = np.where(count > 0, value / count, value)
            latents = repair_nan(latents)

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                callback(i, t, latents)

        # remove extra margins
        latents = latents[
            :, :, 0 : (height // LATENT_FACTOR), 0 : (width // LATENT_FACTOR)
        ]

        latents = np.clip(latents, -4, +4)
        latents = 1 / 0.18215 * latents
        # image = self.vae_decoder(latent_sample=latents)[0]
        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
        image = np.concatenate(
            [
                self.vae_decoder(latent_sample=latents[i : i + 1])[0]
                for i in range(latents.shape[0])
            ]
        )

        image = np.clip(image / 2 + 0.5, 0, 1)
        image = image.transpose((0, 2, 3, 1))

        if self.safety_checker is not None:
            safety_checker_input = self.feature_extractor(
                self.numpy_to_pil(image), return_tensors="np"
            ).pixel_values.astype(image.dtype)

            images, has_nsfw_concept = [], []
            for i in range(image.shape[0]):
                image_i, has_nsfw_concept_i = self.safety_checker(
                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
                )
                images.append(image_i)
                has_nsfw_concept.append(has_nsfw_concept_i[0])
            image = np.concatenate(images)
        else:
            has_nsfw_concept = None

        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(
            images=image, nsfw_content_detected=has_nsfw_concept
        )

    @torch.no_grad()
    def img2img(
        self,
        prompt: Union[str, List[str]] = None,
        image: Union[np.ndarray, PIL.Image.Image] = None,
        strength: float = 0.8,
        num_inference_steps: Optional[int] = 50,
        guidance_scale: Optional[float] = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: Optional[float] = 0.0,
        generator: Optional[np.random.RandomState] = None,
        prompt_embeds: Optional[np.ndarray] = None,
        negative_prompt_embeds: Optional[np.ndarray] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
        callback_steps: int = 1,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`):
                `Image`, or tensor representing an image batch which will be upscaled. *
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
                is less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`np.random.RandomState`, *optional*):
                One or a list of [numpy generator(s)](TODO) to make generation deterministic.
            latents (`np.ndarray`, *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 will ge generated by sampling using the supplied random `generator`.
            prompt_embeds (`np.ndarray`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`np.ndarray`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            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`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """

        height = image.height
        width = image.width

        # check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            height,
            width,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
        )

        # define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if strength < 0 or strength > 1:
            raise ValueError(
                f"The value of strength should in [0.0, 1.0] but is {strength}"
            )

        if generator is None:
            generator = np.random

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps)

        # prep image
        image = preprocess(image).cpu().numpy()

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        prompt_embeds = self._encode_prompt(
            prompt,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )

        # get the initial random noise unless the user supplied it
        latents_dtype = prompt_embeds.dtype
        image = image.astype(latents_dtype)

        # encode the init image into latents and scale the latents
        latents = self.vae_encoder(sample=image)[0]
        latents = 0.18215 * latents

        if isinstance(prompt, str):
            prompt = [prompt]
        if len(prompt) > latents.shape[0] and len(prompt) % latents.shape[0] == 0:
            # expand init_latents for batch_size
            deprecation_message = (
                f"You have passed {len(prompt)} text prompts (`prompt`), but only {latents.shape[0]} initial"
                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
                " your script to pass as many initial images as text prompts to suppress this warning."
            )
            deprecate(
                "len(prompt) != len(image)",
                "1.0.0",
                deprecation_message,
                standard_warn=False,
            )
            additional_image_per_prompt = len(prompt) // latents.shape[0]
            latents = np.concatenate(
                [latents] * additional_image_per_prompt * num_images_per_prompt, axis=0
            )
        elif len(prompt) > latents.shape[0] and len(prompt) % latents.shape[0] != 0:
            raise ValueError(
                f"Cannot duplicate `image` of batch size {latents.shape[0]} to {len(prompt)} text prompts."
            )
        else:
            latents = np.concatenate([latents] * num_images_per_prompt, axis=0)

        # get the original timestep using init_timestep
        offset = self.scheduler.config.get("steps_offset", 0)
        init_timestep = int(num_inference_steps * strength) + offset
        init_timestep = min(init_timestep, num_inference_steps)

        timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
        timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)

        noise = generator.randn(*latents.shape).astype(latents_dtype)
        latents = self.scheduler.add_noise(
            torch.from_numpy(latents),
            torch.from_numpy(noise),
            torch.from_numpy(timesteps),
        )
        latents = latents.numpy()

        # 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

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

        timestep_dtype = next(
            (
                input.type
                for input in self.unet.model.get_inputs()
                if input.name == "timestep"
            ),
            "tensor(float)",
        )
        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]

        # panorama additions
        views, resize = self.get_views(height, width, self.window, self.stride)
        logger.trace("panorama resized latents to %s", resize)

        count = np.zeros(resize_latent_shape(latents, resize))
        value = np.zeros(resize_latent_shape(latents, resize))

        # adjust latents
        latents = expand_latents(
            latents,
            random_seed(generator),
            Size(resize[1], resize[0]),
            sigma=self.scheduler.init_noise_sigma,
        )

        for i, t in enumerate(self.progress_bar(timesteps)):
            count.fill(0)
            value.fill(0)

            for h_start, h_end, w_start, w_end in views:
                # get the latents corresponding to the current view coordinates
                latents_for_view = latents[:, :, h_start:h_end, w_start:w_end]

                # expand the latents if we are doing classifier free guidance
                latent_model_input = (
                    np.concatenate([latents_for_view] * 2)
                    if do_classifier_free_guidance
                    else latents_for_view
                )
                latent_model_input = self.scheduler.scale_model_input(
                    torch.from_numpy(latent_model_input), t
                )
                latent_model_input = latent_model_input.cpu().numpy()

                # predict the noise residual
                timestep = np.array([t], dtype=timestep_dtype)
                noise_pred = self.unet(
                    sample=latent_model_input,
                    timestep=timestep,
                    encoder_hidden_states=prompt_embeds,
                )
                noise_pred = noise_pred[0]

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
                    noise_pred = noise_pred_uncond + guidance_scale * (
                        noise_pred_text - noise_pred_uncond
                    )

                # compute the previous noisy sample x_t -> x_t-1
                scheduler_output = self.scheduler.step(
                    torch.from_numpy(noise_pred),
                    t,
                    torch.from_numpy(latents_for_view),
                    **extra_step_kwargs,
                )
                latents_view_denoised = scheduler_output.prev_sample.numpy()

                value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                count[:, :, h_start:h_end, w_start:w_end] += 1

            # take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113
            latents = np.where(count > 0, value / count, value)

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                callback(i, t, latents)

        # remove extra margins
        latents = latents[
            :, :, 0 : (height // LATENT_FACTOR), 0 : (width // LATENT_FACTOR)
        ]

        latents = 1 / 0.18215 * latents
        # image = self.vae_decoder(latent_sample=latents)[0]
        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
        image = np.concatenate(
            [
                self.vae_decoder(latent_sample=latents[i : i + 1])[0]
                for i in range(latents.shape[0])
            ]
        )

        image = np.clip(image / 2 + 0.5, 0, 1)
        image = image.transpose((0, 2, 3, 1))

        if self.safety_checker is not None:
            safety_checker_input = self.feature_extractor(
                self.numpy_to_pil(image), return_tensors="np"
            ).pixel_values.astype(image.dtype)

            images, has_nsfw_concept = [], []
            for i in range(image.shape[0]):
                image_i, has_nsfw_concept_i = self.safety_checker(
                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
                )
                images.append(image_i)
                has_nsfw_concept.append(has_nsfw_concept_i[0])
            image = np.concatenate(images)
        else:
            has_nsfw_concept = None

        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(
            images=image, nsfw_content_detected=has_nsfw_concept
        )

    @torch.no_grad()
    def inpaint(
        self,
        prompt: Union[str, List[str]],
        image: PIL.Image.Image,
        mask_image: PIL.Image.Image,
        height: Optional[int] = 512,
        width: Optional[int] = 512,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[np.random.RandomState] = None,
        latents: Optional[np.ndarray] = None,
        prompt_embeds: Optional[np.ndarray] = None,
        negative_prompt_embeds: Optional[np.ndarray] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
        callback_steps: int = 1,
    ):
        r"""
        Function invoked when calling the pipeline for generation.
        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            image (`PIL.Image.Image`):
                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
                be masked out with `mask_image` and repainted according to `prompt`.
            mask_image (`PIL.Image.Image`):
                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
                instead of 3, so the expected shape would be `(B, H, W, 1)`.
            height (`int`, *optional*, defaults to 512):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to 512):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
                if `guidance_scale` is less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`np.random.RandomState`, *optional*):
                A np.random.RandomState to make generation deterministic.
            latents (`np.ndarray`, *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 will ge generated by sampling using the supplied random `generator`.
            prompt_embeds (`np.ndarray`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`np.ndarray`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            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`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """

        # check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            height,
            width,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
        )

        # define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if generator is None:
            generator = np.random

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps)

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        prompt_embeds = self._encode_prompt(
            prompt,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )

        num_channels_latents = LATENT_CHANNELS
        latents_shape = (
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height // LATENT_FACTOR,
            width // LATENT_FACTOR,
        )
        latents_dtype = prompt_embeds.dtype
        if latents is None:
            latents = generator.randn(*latents_shape).astype(latents_dtype)
        else:
            if latents.shape != latents_shape:
                raise ValueError(
                    f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"
                )

        # prepare mask and masked_image
        mask, masked_image = prepare_mask_and_masked_image(
            image, mask_image, latents_shape[-2:]
        )
        mask = mask.astype(latents.dtype)
        masked_image = masked_image.astype(latents.dtype)

        masked_image_latents = self.vae_encoder(sample=masked_image)[0]
        masked_image_latents = 0.18215 * masked_image_latents

        # duplicate mask and masked_image_latents for each generation per prompt
        mask = mask.repeat(batch_size * num_images_per_prompt, 0)
        masked_image_latents = masked_image_latents.repeat(
            batch_size * num_images_per_prompt, 0
        )

        mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask
        masked_image_latents = (
            np.concatenate([masked_image_latents] * 2)
            if do_classifier_free_guidance
            else masked_image_latents
        )

        num_channels_mask = mask.shape[1]
        num_channels_masked_image = masked_image_latents.shape[1]

        unet_input_channels = NUM_UNET_INPUT_CHANNELS
        if (
            num_channels_latents + num_channels_mask + num_channels_masked_image
            != unet_input_channels
        ):
            raise ValueError(
                "Incorrect configuration settings! The config of `pipeline.unet` expects"
                f" {unet_input_channels} but received `num_channels_latents`: {num_channels_latents} +"
                f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
                f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
                " `pipeline.unet` or your `mask_image` or `image` input."
            )

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * np.float64(self.scheduler.init_noise_sigma)

        # 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

        timestep_dtype = next(
            (
                input.type
                for input in self.unet.model.get_inputs()
                if input.name == "timestep"
            ),
            "tensor(float)",
        )
        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]

        # panorama additions
        views, resize = self.get_views(height, width, self.window, self.stride)
        logger.trace("panorama resized latents to %s", resize)

        count = np.zeros(resize_latent_shape(latents, resize))
        value = np.zeros(resize_latent_shape(latents, resize))

        # adjust latents
        latents = expand_latents(
            latents,
            random_seed(generator),
            Size(resize[1], resize[0]),
            sigma=self.scheduler.init_noise_sigma,
        )

        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
            count.fill(0)
            value.fill(0)

            for h_start, h_end, w_start, w_end in views:
                # get the latents corresponding to the current view coordinates
                latents_for_view = latents[:, :, h_start:h_end, w_start:w_end]
                mask_for_view = mask[:, :, h_start:h_end, w_start:w_end]
                masked_latents_for_view = masked_image_latents[
                    :, :, h_start:h_end, w_start:w_end
                ]

                # expand the latents if we are doing classifier free guidance
                latent_model_input = (
                    np.concatenate([latents_for_view] * 2)
                    if do_classifier_free_guidance
                    else latents_for_view
                )
                # concat latents, mask, masked_image_latnets in the channel dimension
                latent_model_input = self.scheduler.scale_model_input(
                    torch.from_numpy(latent_model_input), t
                )
                latent_model_input = latent_model_input.cpu().numpy()
                latent_model_input = np.concatenate(
                    [latent_model_input, mask_for_view, masked_latents_for_view], axis=1
                )

                # predict the noise residual
                timestep = np.array([t], dtype=timestep_dtype)
                noise_pred = self.unet(
                    sample=latent_model_input,
                    timestep=timestep,
                    encoder_hidden_states=prompt_embeds,
                )[0]

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
                    noise_pred = noise_pred_uncond + guidance_scale * (
                        noise_pred_text - noise_pred_uncond
                    )

                # compute the previous noisy sample x_t -> x_t-1
                scheduler_output = self.scheduler.step(
                    torch.from_numpy(noise_pred),
                    t,
                    torch.from_numpy(latents_for_view),
                    **extra_step_kwargs,
                )
                latents_view_denoised = scheduler_output.prev_sample.numpy()

                value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                count[:, :, h_start:h_end, w_start:w_end] += 1

            # take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113
            latents = np.where(count > 0, value / count, value)

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                callback(i, t, latents)

        # remove extra margins
        latents = latents[
            :, :, 0 : (height // LATENT_FACTOR), 0 : (width // LATENT_FACTOR)
        ]

        latents = 1 / 0.18215 * latents
        # image = self.vae_decoder(latent_sample=latents)[0]
        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
        image = np.concatenate(
            [
                self.vae_decoder(latent_sample=latents[i : i + 1])[0]
                for i in range(latents.shape[0])
            ]
        )

        image = np.clip(image / 2 + 0.5, 0, 1)
        image = image.transpose((0, 2, 3, 1))

        if self.safety_checker is not None:
            safety_checker_input = self.feature_extractor(
                self.numpy_to_pil(image), return_tensors="np"
            ).pixel_values.astype(image.dtype)
            # safety_checker does not support batched inputs yet
            images, has_nsfw_concept = [], []
            for i in range(image.shape[0]):
                image_i, has_nsfw_concept_i = self.safety_checker(
                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
                )
                images.append(image_i)
                has_nsfw_concept.append(has_nsfw_concept_i[0])
            image = np.concatenate(images)
        else:
            has_nsfw_concept = None

        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(
            images=image, nsfw_content_detected=has_nsfw_concept
        )

    def __call__(
        self,
        *args,
        **kwargs,
    ):
        if "image" in kwargs or (
            len(args) > 1
            and (
                isinstance(args[1], np.ndarray) or isinstance(args[1], PIL.Image.Image)
            )
        ):
            if "mask_image" in kwargs or (
                len(args) > 2
                and (
                    isinstance(args[1], np.ndarray)
                    or isinstance(args[1], PIL.Image.Image)
                )
            ):
                logger.debug("running inpaint panorama pipeline")
                return self.inpaint(*args, **kwargs)
            else:
                logger.debug("running img2img panorama pipeline")
                return self.img2img(*args, **kwargs)
        else:
            logger.debug("running txt2img panorama pipeline")
            return self.text2img(*args, **kwargs)

    def set_window_size(self, window: int, stride: int):
        self.window = window
        self.stride = stride