# Copyright 2025 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 enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from transformers import (
    CLIPTextModel,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
)

from diffusers.image_processor import VaeImageProcessor
from diffusers.loaders import (
    FromSingleFileMixin,
    StableDiffusionXLLoraLoaderMixin,
    TextualInversionLoaderMixin,
)
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.attention_processor import (
    AttnProcessor2_0,
    FusedAttnProcessor2_0,
    XFormersAttnProcessor,
)
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
from diffusers.schedulers import KarrasDiffusionSchedulers, LMSDiscreteScheduler
from diffusers.utils import (
    USE_PEFT_BACKEND,
    is_invisible_watermark_available,
    is_torch_xla_available,
    logging,
    replace_example_docstring,
    scale_lora_layers,
    unscale_lora_layers,
)
from diffusers.utils.torch_utils import randn_tensor


try:
    from ligo.segments import segment
except ImportError:
    raise ImportError("Please install transformers and ligo-segments to use the mixture pipeline")

if is_invisible_watermark_available():
    from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker

if is_torch_xla_available():
    import torch_xla.core.xla_model as xm

    XLA_AVAILABLE = True
else:
    XLA_AVAILABLE = False


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import StableDiffusionXLPipeline

        >>> pipe = StableDiffusionXLPipeline.from_pretrained(
        ...     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> prompt = "a photo of an astronaut riding a horse on mars"
        >>> image = pipe(prompt).images[0]
        ```
"""


def _tile2pixel_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):
    """Given a tile row and column numbers returns the range of pixels affected by that tiles in the overall image

    Returns a tuple with:
        - Starting coordinates of rows in pixel space
        - Ending coordinates of rows in pixel space
        - Starting coordinates of columns in pixel space
        - Ending coordinates of columns in pixel space
    """
    px_row_init = 0 if tile_row == 0 else tile_row * (tile_height - tile_row_overlap)
    px_row_end = px_row_init + tile_height
    px_col_init = 0 if tile_col == 0 else tile_col * (tile_width - tile_col_overlap)
    px_col_end = px_col_init + tile_width
    return px_row_init, px_row_end, px_col_init, px_col_end


def _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end):
    """Translates coordinates in pixel space to coordinates in latent space"""
    return px_row_init // 8, px_row_end // 8, px_col_init // 8, px_col_end // 8


def _tile2latent_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):
    """Given a tile row and column numbers returns the range of latents affected by that tiles in the overall image

    Returns a tuple with:
        - Starting coordinates of rows in latent space
        - Ending coordinates of rows in latent space
        - Starting coordinates of columns in latent space
        - Ending coordinates of columns in latent space
    """
    px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices(
        tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
    )
    return _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end)


def _tile2latent_exclusive_indices(
    tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, rows, columns
):
    """Given a tile row and column numbers returns the range of latents affected only by that tile in the overall image

    Returns a tuple with:
        - Starting coordinates of rows in latent space
        - Ending coordinates of rows in latent space
        - Starting coordinates of columns in latent space
        - Ending coordinates of columns in latent space
    """
    row_init, row_end, col_init, col_end = _tile2latent_indices(
        tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
    )
    row_segment = segment(row_init, row_end)
    col_segment = segment(col_init, col_end)
    # Iterate over the rest of tiles, clipping the region for the current tile
    for row in range(rows):
        for column in range(columns):
            if row != tile_row and column != tile_col:
                clip_row_init, clip_row_end, clip_col_init, clip_col_end = _tile2latent_indices(
                    row, column, tile_width, tile_height, tile_row_overlap, tile_col_overlap
                )
                row_segment = row_segment - segment(clip_row_init, clip_row_end)
                col_segment = col_segment - segment(clip_col_init, clip_col_end)
    # return row_init, row_end, col_init, col_end
    return row_segment[0], row_segment[1], col_segment[0], col_segment[1]


def _get_crops_coords_list(num_rows, num_cols, output_width):
    """
    Generates a list of lists of `crops_coords_top_left` tuples for focusing on
    different horizontal parts of an image, and repeats this list for the specified
    number of rows in the output structure.

    This function calculates `crops_coords_top_left` tuples to create horizontal
    focus variations (like left, center, right focus) based on `output_width`
    and `num_cols` (which represents the number of horizontal focus points/columns).
    It then repeats the *list* of these horizontal focus tuples `num_rows` times to
    create the final list of lists output structure.

    Args:
        num_rows (int): The desired number of rows in the output list of lists.
                          This determines how many times the list of horizontal
                          focus variations will be repeated.
        num_cols (int): The number of horizontal focus points (columns) to generate.
                          This determines how many horizontal focus variations are
                          created based on dividing the `output_width`.
        output_width (int): The desired width of the output image.

    Returns:
        list[list[tuple[int, int]]]: A list of lists of tuples. Each inner list
                                     contains `num_cols` tuples of `(ctop, cleft)`,
                                     representing horizontal focus points. The outer list
                                     contains `num_rows` such inner lists.
    """
    crops_coords_list = []
    if num_cols <= 0:
        crops_coords_list = []
    elif num_cols == 1:
        crops_coords_list = [(0, 0)]
    else:
        section_width = output_width / num_cols
        for i in range(num_cols):
            cleft = int(round(i * section_width))
            crops_coords_list.append((0, cleft))

    result_list = []
    for _ in range(num_rows):
        result_list.append(list(crops_coords_list))

    return result_list


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    r"""
    Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
    Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
    Flawed](https://arxiv.org/pdf/2305.08891.pdf).

    Args:
        noise_cfg (`torch.Tensor`):
            The predicted noise tensor for the guided diffusion process.
        noise_pred_text (`torch.Tensor`):
            The predicted noise tensor for the text-guided diffusion process.
        guidance_rescale (`float`, *optional*, defaults to 0.0):
            A rescale factor applied to the noise predictions.

    Returns:
        noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor.
    """
    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
    # rescale the results from guidance (fixes overexposure)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
    return noise_cfg


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
    scheduler,
    num_inference_steps: Optional[int] = None,
    device: Optional[Union[str, torch.device]] = None,
    timesteps: Optional[List[int]] = None,
    sigmas: Optional[List[float]] = None,
    **kwargs,
):
    r"""
    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.

    Args:
        scheduler (`SchedulerMixin`):
            The scheduler to get timesteps from.
        num_inference_steps (`int`):
            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
            must be `None`.
        device (`str` or `torch.device`, *optional*):
            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
        timesteps (`List[int]`, *optional*):
            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
            `num_inference_steps` and `sigmas` must be `None`.
        sigmas (`List[float]`, *optional*):
            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
            `num_inference_steps` and `timesteps` must be `None`.

    Returns:
        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
        second element is the number of inference steps.
    """

    if timesteps is not None and sigmas is not None:
        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
    if timesteps is not None:
        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
        if not accepts_timesteps:
            raise ValueError(
                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
                f" timestep schedules. Please check whether you are using the correct scheduler."
            )
        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    elif sigmas is not None:
        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
        if not accept_sigmas:
            raise ValueError(
                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
                f" sigmas schedules. Please check whether you are using the correct scheduler."
            )
        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return timesteps, num_inference_steps


class StableDiffusionXLTilingPipeline(
    DiffusionPipeline,
    StableDiffusionMixin,
    FromSingleFileMixin,
    StableDiffusionXLLoraLoaderMixin,
    TextualInversionLoaderMixin,
):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion XL.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    The pipeline also inherits the following loading methods:
        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
        - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
        - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion XL uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        text_encoder_2 ([` CLIPTextModelWithProjection`]):
            Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
            specifically the
            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
            variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        tokenizer_2 (`CLIPTokenizer`):
            Second Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
            Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
            `stabilityai/stable-diffusion-xl-base-1-0`.
        add_watermarker (`bool`, *optional*):
            Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
            watermark output images. If not defined, it will default to True if the package is installed, otherwise no
            watermarker will be used.
    """

    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
    _optional_components = [
        "tokenizer",
        "tokenizer_2",
        "text_encoder",
        "text_encoder_2",
    ]

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        text_encoder_2: CLIPTextModelWithProjection,
        tokenizer: CLIPTokenizer,
        tokenizer_2: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: KarrasDiffusionSchedulers,
        force_zeros_for_empty_prompt: bool = True,
        add_watermarker: Optional[bool] = None,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            text_encoder_2=text_encoder_2,
            tokenizer=tokenizer,
            tokenizer_2=tokenizer_2,
            unet=unet,
            scheduler=scheduler,
        )
        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

        self.default_sample_size = (
            self.unet.config.sample_size
            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
            else 128
        )

        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()

        if add_watermarker:
            self.watermark = StableDiffusionXLWatermarker()
        else:
            self.watermark = None

    class SeedTilesMode(Enum):
        """Modes in which the latents of a particular tile can be re-seeded"""

        FULL = "full"
        EXCLUSIVE = "exclusive"

    def encode_prompt(
        self,
        prompt: str,
        prompt_2: Optional[str] = None,
        device: Optional[torch.device] = None,
        num_images_per_prompt: int = 1,
        do_classifier_free_guidance: bool = True,
        negative_prompt: Optional[str] = None,
        negative_prompt_2: Optional[str] = None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        pooled_prompt_embeds: Optional[torch.Tensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
        lora_scale: Optional[float] = None,
        clip_skip: Optional[int] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
                used in both text-encoders
            device: (`torch.device`):
                torch device
            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]`, *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`).
            negative_prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
            prompt_embeds (`torch.Tensor`, *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 (`torch.Tensor`, *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.
            pooled_prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
                input argument.
            lora_scale (`float`, *optional*):
                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
            clip_skip (`int`, *optional*):
                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
                the output of the pre-final layer will be used for computing the prompt embeddings.
        """
        device = device or self._execution_device

        # set lora scale so that monkey patched LoRA
        # function of text encoder can correctly access it
        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
            self._lora_scale = lora_scale

            # dynamically adjust the LoRA scale
            if self.text_encoder is not None:
                if not USE_PEFT_BACKEND:
                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
                else:
                    scale_lora_layers(self.text_encoder, lora_scale)

            if self.text_encoder_2 is not None:
                if not USE_PEFT_BACKEND:
                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
                else:
                    scale_lora_layers(self.text_encoder_2, lora_scale)

        prompt = [prompt] if isinstance(prompt, str) else prompt

        if prompt is not None:
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        # Define tokenizers and text encoders
        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
        text_encoders = (
            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
        )

        if prompt_embeds is None:
            prompt_2 = prompt_2 or prompt
            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2

            # textual inversion: process multi-vector tokens if necessary
            prompt_embeds_list = []
            prompts = [prompt, prompt_2]
            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin):
                    prompt = self.maybe_convert_prompt(prompt, tokenizer)

                text_inputs = tokenizer(
                    prompt,
                    padding="max_length",
                    max_length=tokenizer.model_max_length,
                    truncation=True,
                    return_tensors="pt",
                )

                text_input_ids = text_inputs.input_ids
                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                    text_input_ids, untruncated_ids
                ):
                    removed_text = tokenizer.batch_decode(untruncated_ids[:, 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" {tokenizer.model_max_length} tokens: {removed_text}"
                    )

                prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)

                # We are only ALWAYS interested in the pooled output of the final text encoder
                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
                    pooled_prompt_embeds = prompt_embeds[0]

                if clip_skip is None:
                    prompt_embeds = prompt_embeds.hidden_states[-2]
                else:
                    # "2" because SDXL always indexes from the penultimate layer.
                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]

                prompt_embeds_list.append(prompt_embeds)

            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)

        # get unconditional embeddings for classifier free guidance
        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
        elif do_classifier_free_guidance and negative_prompt_embeds is None:
            negative_prompt = negative_prompt or ""
            negative_prompt_2 = negative_prompt_2 or negative_prompt

            # normalize str to list
            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
            negative_prompt_2 = (
                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
            )

            uncond_tokens: List[str]
            if prompt is not None and 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 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, negative_prompt_2]

            negative_prompt_embeds_list = []
            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin):
                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)

                max_length = prompt_embeds.shape[1]
                uncond_input = tokenizer(
                    negative_prompt,
                    padding="max_length",
                    max_length=max_length,
                    truncation=True,
                    return_tensors="pt",
                )

                negative_prompt_embeds = text_encoder(
                    uncond_input.input_ids.to(device),
                    output_hidden_states=True,
                )

                # We are only ALWAYS interested in the pooled output of the final text encoder
                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
                    negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]

                negative_prompt_embeds_list.append(negative_prompt_embeds)

            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)

        if self.text_encoder_2 is not None:
            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
        else:
            prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            if self.text_encoder_2 is not None:
                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
            else:
                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)

            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
            bs_embed * num_images_per_prompt, -1
        )
        if do_classifier_free_guidance:
            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
                bs_embed * num_images_per_prompt, -1
            )

        if self.text_encoder is not None:
            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
                # Retrieve the original scale by scaling back the LoRA layers
                unscale_lora_layers(self.text_encoder, lora_scale)

        if self.text_encoder_2 is not None:
            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
                # Retrieve the original scale by scaling back the LoRA layers
                unscale_lora_layers(self.text_encoder_2, lora_scale)

        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # 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

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def check_inputs(self, prompt, height, width, grid_cols, seed_tiles_mode, tiles_mode):
        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 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 not isinstance(prompt, list) or not all(isinstance(row, list) for row in prompt):
            raise ValueError(f"`prompt` has to be a list of lists but is {type(prompt)}")

        if not all(len(row) == grid_cols for row in prompt):
            raise ValueError("All prompt rows must have the same number of prompt columns")

        if not isinstance(seed_tiles_mode, str) and (
            not isinstance(seed_tiles_mode, list) or not all(isinstance(row, list) for row in seed_tiles_mode)
        ):
            raise ValueError(f"`seed_tiles_mode` has to be a string or list of lists but is {type(prompt)}")

        if any(mode not in tiles_mode for row in seed_tiles_mode for mode in row):
            raise ValueError(f"Seed tiles mode must be one of {tiles_mode}")

    def _get_add_time_ids(
        self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
    ):
        add_time_ids = list(original_size + crops_coords_top_left + target_size)

        passed_add_embed_dim = (
            self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
        )
        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features

        if expected_add_embed_dim != passed_add_embed_dim:
            raise ValueError(
                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
            )

        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
        return add_time_ids

    def _gaussian_weights(self, tile_width, tile_height, nbatches, device, dtype):
        """Generates a gaussian mask of weights for tile contributions"""
        import numpy as np
        from numpy import exp, pi, sqrt

        latent_width = tile_width // 8
        latent_height = tile_height // 8

        var = 0.01
        midpoint = (latent_width - 1) / 2  # -1 because index goes from 0 to latent_width - 1
        x_probs = [
            exp(-(x - midpoint) * (x - midpoint) / (latent_width * latent_width) / (2 * var)) / sqrt(2 * pi * var)
            for x in range(latent_width)
        ]
        midpoint = latent_height / 2
        y_probs = [
            exp(-(y - midpoint) * (y - midpoint) / (latent_height * latent_height) / (2 * var)) / sqrt(2 * pi * var)
            for y in range(latent_height)
        ]

        weights_np = np.outer(y_probs, x_probs)
        weights_torch = torch.tensor(weights_np, device=device)
        weights_torch = weights_torch.to(dtype)
        return torch.tile(weights_torch, (nbatches, self.unet.config.in_channels, 1, 1))

    def upcast_vae(self):
        dtype = self.vae.dtype
        self.vae.to(dtype=torch.float32)
        use_torch_2_0_or_xformers = isinstance(
            self.vae.decoder.mid_block.attentions[0].processor,
            (
                AttnProcessor2_0,
                XFormersAttnProcessor,
                FusedAttnProcessor2_0,
            ),
        )
        # if xformers or torch_2_0 is used attention block does not need
        # to be in float32 which can save lots of memory
        if use_torch_2_0_or_xformers:
            self.vae.post_quant_conv.to(dtype)
            self.vae.decoder.conv_in.to(dtype)
            self.vae.decoder.mid_block.to(dtype)

    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
    def get_guidance_scale_embedding(
        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
    ) -> torch.Tensor:
        """
        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

        Args:
            w (`torch.Tensor`):
                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
            embedding_dim (`int`, *optional*, defaults to 512):
                Dimension of the embeddings to generate.
            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
                Data type of the generated embeddings.

        Returns:
            `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
        """
        assert len(w.shape) == 1
        w = w * 1000.0

        half_dim = embedding_dim // 2
        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
        emb = w.to(dtype)[:, None] * emb[None, :]
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
        if embedding_dim % 2 == 1:  # zero pad
            emb = torch.nn.functional.pad(emb, (0, 1))
        assert emb.shape == (w.shape[0], embedding_dim)
        return emb

    @property
    def guidance_scale(self):
        return self._guidance_scale

    @property
    def clip_skip(self):
        return self._clip_skip

    # 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.
    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None

    @property
    def cross_attention_kwargs(self):
        return self._cross_attention_kwargs

    @property
    def num_timesteps(self):
        return self._num_timesteps

    @property
    def interrupt(self):
        return self._interrupt

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 5.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        original_size: Optional[Tuple[int, int]] = None,
        crops_coords_top_left: Optional[List[List[Tuple[int, int]]]] = None,
        target_size: Optional[Tuple[int, int]] = None,
        negative_original_size: Optional[Tuple[int, int]] = None,
        negative_crops_coords_top_left: Optional[List[List[Tuple[int, int]]]] = None,
        negative_target_size: Optional[Tuple[int, int]] = None,
        clip_skip: Optional[int] = None,
        tile_height: Optional[int] = 1024,
        tile_width: Optional[int] = 1024,
        tile_row_overlap: Optional[int] = 128,
        tile_col_overlap: Optional[int] = 128,
        guidance_scale_tiles: Optional[List[List[float]]] = None,
        seed_tiles: Optional[List[List[int]]] = None,
        seed_tiles_mode: Optional[Union[str, List[List[str]]]] = "full",
        seed_reroll_regions: Optional[List[Tuple[int, int, int, int, int]]] = None,
        **kwargs,
    ):
        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.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image. This is set to 1024 by default for the best results.
                Anything below 512 pixels won't work well for
                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
                and checkpoints that are not specifically fine-tuned on low resolutions.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image. This is set to 1024 by default for the best results.
                Anything below 512 pixels won't work well for
                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
                and checkpoints that are not specifically fine-tuned on low resolutions.
            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 5.0):
                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 (`torch.Generator` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            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_xl.StableDiffusionXLPipelineOutput`] instead
                of a plain tuple.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
                explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            crops_coords_top_left (`List[List[Tuple[int, int]]]`, *optional*, defaults to (0, 0)):
                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                For most cases, `target_size` should be set to the desired height and width of the generated image. If
                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
                micro-conditioning as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
            negative_crops_coords_top_left (`List[List[Tuple[int, int]]]`, *optional*, defaults to (0, 0)):
                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
                micro-conditioning as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                To negatively condition the generation process based on a target image resolution. It should be as same
                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
            tile_height (`int`, *optional*, defaults to 1024):
                Height of each grid tile in pixels.
            tile_width (`int`, *optional*, defaults to 1024):
                Width of each grid tile in pixels.
            tile_row_overlap (`int`, *optional*, defaults to 128):
                Number of overlapping pixels between tiles in consecutive rows.
            tile_col_overlap (`int`, *optional*, defaults to 128):
                Number of overlapping pixels between tiles in consecutive columns.
            guidance_scale_tiles (`List[List[float]]`, *optional*):
                Specific weights for classifier-free guidance in each tile. If `None`, the value provided in `guidance_scale` will be used.
            seed_tiles (`List[List[int]]`, *optional*):
                Specific seeds for the initialization latents in each tile. These will override the latents generated for the whole canvas using the standard `generator` parameter.
            seed_tiles_mode (`Union[str, List[List[str]]]`, *optional*, defaults to `"full"`):
                Mode for seeding tiles, can be `"full"` or `"exclusive"`. If `"full"`, all the latents affected by the tile will be overridden. If `"exclusive"`, only the latents that are exclusively affected by this tile (and no other tiles) will be overridden.
            seed_reroll_regions (`List[Tuple[int, int, int, int, int]]`, *optional*):
                A list of tuples in the form of `(start_row, end_row, start_column, end_column, seed)` defining regions in pixel space for which the latents will be overridden using the given seed. Takes priority over `seed_tiles`.
            **kwargs (`Dict[str, Any]`, *optional*):
                 Additional optional keyword arguments to be passed to the `unet.__call__` and `scheduler.step` functions.

        Examples:

        Returns:
            [`~pipelines.stable_diffusion_xl.StableDiffusionXLTilingPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion_xl.StableDiffusionXLTilingPipelineOutput`] if `return_dict` is True, otherwise a
            `tuple`. When returning a tuple, the first element is a list with the generated images.
        """

        # 0. Default height and width to unet
        height = height or self.default_sample_size * self.vae_scale_factor
        width = width or self.default_sample_size * self.vae_scale_factor

        original_size = original_size or (height, width)
        target_size = target_size or (height, width)
        negative_original_size = negative_original_size or (height, width)
        negative_target_size = negative_target_size or (height, width)

        self._guidance_scale = guidance_scale
        self._clip_skip = clip_skip
        self._cross_attention_kwargs = cross_attention_kwargs
        self._interrupt = False

        grid_rows = len(prompt)
        grid_cols = len(prompt[0])

        tiles_mode = [mode.value for mode in self.SeedTilesMode]

        if isinstance(seed_tiles_mode, str):
            seed_tiles_mode = [[seed_tiles_mode for _ in range(len(row))] for row in prompt]

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            height,
            width,
            grid_cols,
            seed_tiles_mode,
            tiles_mode,
        )

        if seed_reroll_regions is None:
            seed_reroll_regions = []

        batch_size = 1

        device = self._execution_device

        # update crops coords list
        crops_coords_top_left = _get_crops_coords_list(grid_rows, grid_cols, tile_width)
        if negative_original_size is not None and negative_target_size is not None:
            negative_crops_coords_top_left = _get_crops_coords_list(grid_rows, grid_cols, tile_width)

        # update height and width tile size and tile overlap size
        height = tile_height + (grid_rows - 1) * (tile_height - tile_row_overlap)
        width = tile_width + (grid_cols - 1) * (tile_width - tile_col_overlap)

        # 3. Encode input prompt
        lora_scale = (
            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
        )
        text_embeddings = [
            [
                self.encode_prompt(
                    prompt=col,
                    device=device,
                    num_images_per_prompt=num_images_per_prompt,
                    do_classifier_free_guidance=self.do_classifier_free_guidance,
                    negative_prompt=negative_prompt,
                    prompt_embeds=None,
                    negative_prompt_embeds=None,
                    pooled_prompt_embeds=None,
                    negative_pooled_prompt_embeds=None,
                    lora_scale=lora_scale,
                    clip_skip=self.clip_skip,
                )
                for col in row
            ]
            for row in prompt
        ]

        # 3. Prepare latents
        latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
        dtype = text_embeddings[0][0][0].dtype
        latents = randn_tensor(latents_shape, generator=generator, device=device, dtype=dtype)

        # 3.1 overwrite latents for specific tiles if provided
        if seed_tiles is not None:
            for row in range(grid_rows):
                for col in range(grid_cols):
                    if (seed_tile := seed_tiles[row][col]) is not None:
                        mode = seed_tiles_mode[row][col]
                        if mode == self.SeedTilesMode.FULL.value:
                            row_init, row_end, col_init, col_end = _tile2latent_indices(
                                row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
                            )
                        else:
                            row_init, row_end, col_init, col_end = _tile2latent_exclusive_indices(
                                row,
                                col,
                                tile_width,
                                tile_height,
                                tile_row_overlap,
                                tile_col_overlap,
                                grid_rows,
                                grid_cols,
                            )
                        tile_generator = torch.Generator(device).manual_seed(seed_tile)
                        tile_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)
                        latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(
                            tile_shape, generator=tile_generator, device=device
                        )

        # 3.2 overwrite again for seed reroll regions
        for row_init, row_end, col_init, col_end, seed_reroll in seed_reroll_regions:
            row_init, row_end, col_init, col_end = _pixel2latent_indices(
                row_init, row_end, col_init, col_end
            )  # to latent space coordinates
            reroll_generator = torch.Generator(device).manual_seed(seed_reroll)
            region_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)
            latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(
                region_shape, generator=reroll_generator, device=device
            )

        # 4. Prepare timesteps
        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
        extra_set_kwargs = {}
        if accepts_offset:
            extra_set_kwargs["offset"] = 1
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler, num_inference_steps, device, None, None, **extra_set_kwargs
        )

        # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
        if isinstance(self.scheduler, LMSDiscreteScheduler):
            latents = latents * self.scheduler.sigmas[0]

        # 5. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 6. Prepare added time ids & embeddings
        # text_embeddings order: prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
        embeddings_and_added_time = []
        for row in range(grid_rows):
            addition_embed_type_row = []
            for col in range(grid_cols):
                # extract generated values
                prompt_embeds = text_embeddings[row][col][0]
                negative_prompt_embeds = text_embeddings[row][col][1]
                pooled_prompt_embeds = text_embeddings[row][col][2]
                negative_pooled_prompt_embeds = text_embeddings[row][col][3]

                add_text_embeds = pooled_prompt_embeds
                if self.text_encoder_2 is None:
                    text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
                else:
                    text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
                    add_time_ids = self._get_add_time_ids(
                        original_size,
                        crops_coords_top_left[row][col],
                        target_size,
                        dtype=prompt_embeds.dtype,
                        text_encoder_projection_dim=text_encoder_projection_dim,
                    )
                    if negative_original_size is not None and negative_target_size is not None:
                        negative_add_time_ids = self._get_add_time_ids(
                            negative_original_size,
                            negative_crops_coords_top_left[row][col],
                            negative_target_size,
                            dtype=prompt_embeds.dtype,
                            text_encoder_projection_dim=text_encoder_projection_dim,
                        )
                    else:
                        negative_add_time_ids = add_time_ids

                    if self.do_classifier_free_guidance:
                        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
                        add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
                        add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)

                    prompt_embeds = prompt_embeds.to(device)
                    add_text_embeds = add_text_embeds.to(device)
                    add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
                addition_embed_type_row.append((prompt_embeds, add_text_embeds, add_time_ids))
            embeddings_and_added_time.append(addition_embed_type_row)

        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)

        # 7. Mask for tile weights strength
        tile_weights = self._gaussian_weights(tile_width, tile_height, batch_size, device, torch.float32)

        # 8. Denoising loop
        self._num_timesteps = len(timesteps)
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # Diffuse each tile
                noise_preds = []
                for row in range(grid_rows):
                    noise_preds_row = []
                    for col in range(grid_cols):
                        if self.interrupt:
                            continue
                        px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(
                            row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
                        )
                        tile_latents = latents[:, :, px_row_init:px_row_end, px_col_init:px_col_end]
                        # expand the latents if we are doing classifier free guidance
                        latent_model_input = (
                            torch.cat([tile_latents] * 2) if self.do_classifier_free_guidance else tile_latents
                        )
                        latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                        # predict the noise residual
                        added_cond_kwargs = {
                            "text_embeds": embeddings_and_added_time[row][col][1],
                            "time_ids": embeddings_and_added_time[row][col][2],
                        }
                        with torch.amp.autocast(device.type, dtype=dtype, enabled=dtype != self.unet.dtype):
                            noise_pred = self.unet(
                                latent_model_input,
                                t,
                                encoder_hidden_states=embeddings_and_added_time[row][col][0],
                                cross_attention_kwargs=self.cross_attention_kwargs,
                                added_cond_kwargs=added_cond_kwargs,
                                return_dict=False,
                            )[0]

                        # perform guidance
                        if self.do_classifier_free_guidance:
                            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                            guidance = (
                                guidance_scale
                                if guidance_scale_tiles is None or guidance_scale_tiles[row][col] is None
                                else guidance_scale_tiles[row][col]
                            )
                            noise_pred_tile = noise_pred_uncond + guidance * (noise_pred_text - noise_pred_uncond)
                            noise_preds_row.append(noise_pred_tile)
                    noise_preds.append(noise_preds_row)

                # Stitch noise predictions for all tiles
                noise_pred = torch.zeros(latents.shape, device=device)
                contributors = torch.zeros(latents.shape, device=device)

                # Add each tile contribution to overall latents
                for row in range(grid_rows):
                    for col in range(grid_cols):
                        px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(
                            row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
                        )
                        noise_pred[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += (
                            noise_preds[row][col] * tile_weights
                        )
                        contributors[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += tile_weights

                # Average overlapping areas with more than 1 contributor
                noise_pred /= contributors
                noise_pred = noise_pred.to(dtype)

                # compute the previous noisy sample x_t -> x_t-1
                latents_dtype = latents.dtype
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
                if latents.dtype != latents_dtype:
                    if torch.backends.mps.is_available():
                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
                        latents = latents.to(latents_dtype)

                # update progress bar
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()

                if XLA_AVAILABLE:
                    xm.mark_step()

        if not output_type == "latent":
            # make sure the VAE is in float32 mode, as it overflows in float16
            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

            if needs_upcasting:
                self.upcast_vae()
                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
            elif latents.dtype != self.vae.dtype:
                if torch.backends.mps.is_available():
                    # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
                    self.vae = self.vae.to(latents.dtype)

            # unscale/denormalize the latents
            # denormalize with the mean and std if available and not None
            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
            if has_latents_mean and has_latents_std:
                latents_mean = (
                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
                )
                latents_std = (
                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
                )
                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
            else:
                latents = latents / self.vae.config.scaling_factor

            image = self.vae.decode(latents, return_dict=False)[0]

            # cast back to fp16 if  needed
            if needs_upcasting:
                self.vae.to(dtype=torch.float16)
        else:
            image = latents

        if not output_type == "latent":
            # apply watermark if available
            if self.watermark is not None:
                image = self.watermark.apply_watermark(image)

            image = self.image_processor.postprocess(image, output_type=output_type)

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image,)

        return StableDiffusionXLPipelineOutput(images=image)
