EngageEngine/pipeline.py

# Copyright 2024 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 typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
import PIL
import numpy as np
from torchvision.transforms.functional import to_pil_image

from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionModelWithProjection,
)

from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.loaders import (
    FromSingleFileMixin,
    IPAdapterMixin,
    StableDiffusionXLLoraLoaderMixin,
    TextualInversionLoaderMixin,
)

from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel, ControlNetModel
from diffusers.models.attention_processor import (
    AttnProcessor2_0,
    FusedAttnProcessor2_0,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
    USE_PEFT_BACKEND,
    deprecate,
    is_torch_xla_available,
    logging,
    replace_example_docstring,
    scale_lora_layers,
    unscale_lora_layers,
)
from diffusers.utils.torch_utils import randn_tensor, is_compiled_module, is_torch_version
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput

from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel

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 retrieve_latents(

        encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"

):
    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
        return encoder_output.latent_dist.sample(generator)
    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
        return encoder_output.latent_dist.mode()
    elif hasattr(encoder_output, "latents"):
        return encoder_output.latents
    else:
        raise AttributeError("Could not access latents of provided encoder_output")


# 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):
    """

    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and

    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4

    """
    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,

        **kwargs,

):
    """

    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 support arbitrary spacing between timesteps. If `None`, then the default

                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`

                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:
        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)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return timesteps, num_inference_steps


class EngagePipeline(
    DiffusionPipeline,
    StableDiffusionMixin,
    FromSingleFileMixin,
    StableDiffusionXLLoraLoaderMixin,
    TextualInversionLoaderMixin,
    IPAdapterMixin,
):
    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

        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters



    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",
        "image_encoder",
        "feature_extractor",
    ]
    _callback_tensor_inputs = [
        "latents",
        "prompt_embeds",
        "negative_prompt_embeds",
        "add_text_embeds",
        "add_time_ids",
        "negative_pooled_prompt_embeds",
        "negative_add_time_ids",
    ]

    def __init__(

            self,

            vae: AutoencoderKL,

            text_encoder: CLIPTextModel,

            text_encoder_2: CLIPTextModelWithProjection,

            tokenizer: CLIPTokenizer,

            tokenizer_2: CLIPTokenizer,

            unet: UNet2DConditionModel,

            controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],

            scheduler: KarrasDiffusionSchedulers,

            image_encoder: CLIPVisionModelWithProjection = None,

            feature_extractor: CLIPImageProcessor = None,

            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,
            controlnet=controlnet,
            scheduler=scheduler,
            image_encoder=image_encoder,
            feature_extractor=feature_extractor,
        )
        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)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.control_image_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
        )
        self.mask_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
        )

        self.default_sample_size = self.unet.config.sample_size

    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.FloatTensor] = None,

            negative_prompt_embeds: Optional[torch.FloatTensor] = None,

            pooled_prompt_embeds: Optional[torch.FloatTensor] = None,

            negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = 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.FloatTensor`, *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.FloatTensor`, *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.FloatTensor`, *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.FloatTensor`, *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
                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
                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.encode_image
    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
        dtype = next(self.image_encoder.parameters()).dtype

        if not isinstance(image, torch.Tensor):
            image = self.feature_extractor(image, return_tensors="pt").pixel_values

        image = image.to(device=device, dtype=dtype)
        if output_hidden_states:
            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
            uncond_image_enc_hidden_states = self.image_encoder(
                torch.zeros_like(image), output_hidden_states=True
            ).hidden_states[-2]
            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
                num_images_per_prompt, dim=0
            )
            return image_enc_hidden_states, uncond_image_enc_hidden_states
        else:
            image_embeds = self.image_encoder(image).image_embeds
            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
            uncond_image_embeds = torch.zeros_like(image_embeds)

            return image_embeds, uncond_image_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
    def prepare_ip_adapter_image_embeds(

            self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance

    ):
        if ip_adapter_image_embeds is None:
            if not isinstance(ip_adapter_image, list):
                ip_adapter_image = [ip_adapter_image]

            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
                raise ValueError(
                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
                )

            image_embeds = []
            for single_ip_adapter_image, image_proj_layer in zip(
                    ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
            ):
                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
                single_image_embeds, single_negative_image_embeds = self.encode_image(
                    single_ip_adapter_image, device, 1, output_hidden_state
                )
                single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
                single_negative_image_embeds = torch.stack(
                    [single_negative_image_embeds] * num_images_per_prompt, dim=0
                )

                if do_classifier_free_guidance:
                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
                    single_image_embeds = single_image_embeds.to(device)

                image_embeds.append(single_image_embeds)
        else:
            repeat_dims = [1]
            image_embeds = []
            for single_image_embeds in ip_adapter_image_embeds:
                if do_classifier_free_guidance:
                    single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
                    single_image_embeds = single_image_embeds.repeat(
                        num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
                    )
                    single_negative_image_embeds = single_negative_image_embeds.repeat(
                        num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))
                    )
                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
                else:
                    single_image_embeds = single_image_embeds.repeat(
                        num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
                    )
                image_embeds.append(single_image_embeds)

        return image_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,

            prompt_2,

            height,

            width,

            callback_steps,

            negative_prompt=None,

            negative_prompt_2=None,

            prompt_embeds=None,

            negative_prompt_embeds=None,

            pooled_prompt_embeds=None,

            negative_pooled_prompt_embeds=None,

            ip_adapter_image=None,

            ip_adapter_image_embeds=None,

            callback_on_step_end_tensor_inputs=None,



            control_image=None,

            controlnet_conditioning_scale=1.0,

            control_guidance_start=0.0,

            control_guidance_end=1.0,



            padding_mask_crop=None,

            strength=None,

            mask_image=None,

            num_inference_steps=None,



    ):
        if strength is not None:
            if strength < 0 or strength > 1:
                raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
        if control_image is 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 padding_mask_crop is not None:
            if not isinstance(control_image, PIL.Image.Image):
                raise ValueError(
                    f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(control_image)}."
                )
            if not isinstance(mask_image, PIL.Image.Image):
                raise ValueError(
                    f"The mask image should be a PIL image when inpainting mask crop, but is of type"
                    f" {type(mask_image)}."
                )

        if 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 callback_on_step_end_tensor_inputs is not None and not all(
                k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
        ):
            raise ValueError(
                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
            )

        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_2 is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt_2`: {prompt_2} 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)}")
        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")

        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."
            )
        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} 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}."
                )

        if prompt_embeds is not None and pooled_prompt_embeds is None:
            raise ValueError(
                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
            )

        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
            raise ValueError(
                "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
            )

        # `prompt` needs more sophisticated handling when there are multiple
        # conditionings.
        if control_image is not None:
            if isinstance(self.controlnet, MultiControlNetModel):
                if isinstance(prompt, list):
                    logger.warning(
                        f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
                        " prompts. The conditionings will be fixed across the prompts."
                    )

            # Check `image`
            is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
                self.controlnet, torch._dynamo.eval_frame.OptimizedModule
            )
            if (
                    isinstance(self.controlnet, ControlNetModel)
                    or is_compiled
                    and isinstance(self.controlnet._orig_mod, ControlNetModel)
            ):
                self.check_control_image(control_image, prompt, prompt_embeds)
            elif (
                    isinstance(self.controlnet, MultiControlNetModel)
                    or is_compiled
                    and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
            ):
                if not isinstance(control_image, list):
                    raise TypeError("For multiple controlnets: `image` must be type `list`")

                # When `image` is a nested list:
                # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
                elif any(isinstance(i, list) for i in control_image):
                    raise ValueError("A single batch of multiple conditionings are supported at the moment.")
                elif len(control_image) != len(self.controlnet.nets):
                    raise ValueError(
                        f"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(control_image)} images and {len(self.controlnet.nets)} ControlNets."
                    )

                for image_ in control_image:
                    self.check_control_image(image_, prompt, prompt_embeds)
            else:
                assert False

            # Check `controlnet_conditioning_scale`
            if (
                    isinstance(self.controlnet, ControlNetModel)
                    or is_compiled
                    and isinstance(self.controlnet._orig_mod, ControlNetModel)
            ):
                if not isinstance(controlnet_conditioning_scale, float):
                    raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
            elif (
                    isinstance(self.controlnet, MultiControlNetModel)
                    or is_compiled
                    and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
            ):
                if isinstance(controlnet_conditioning_scale, list):
                    if any(isinstance(i, list) for i in controlnet_conditioning_scale):
                        raise ValueError("A single batch of multiple conditionings are supported at the moment.")
                elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
                        self.controlnet.nets
                ):
                    raise ValueError(
                        "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
                        " the same length as the number of controlnets"
                    )
            else:
                assert False

            if not isinstance(control_guidance_start, (tuple, list)):
                control_guidance_start = [control_guidance_start]

            if not isinstance(control_guidance_end, (tuple, list)):
                control_guidance_end = [control_guidance_end]

            if len(control_guidance_start) != len(control_guidance_end):
                raise ValueError(
                    f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
                )

            if isinstance(self.controlnet, MultiControlNetModel):
                if len(control_guidance_start) != len(self.controlnet.nets):
                    raise ValueError(
                        f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
                    )

            for start, end in zip(control_guidance_start, control_guidance_end):
                if start >= end:
                    raise ValueError(
                        f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
                    )
                if start < 0.0:
                    raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
                if end > 1.0:
                    raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")

        if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
            raise ValueError(
                "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
            )

        if ip_adapter_image_embeds is not None:
            if not isinstance(ip_adapter_image_embeds, list):
                raise ValueError(
                    f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
                )
            elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
                raise ValueError(
                    f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
                )

    def check_control_image(self, control_image, prompt, prompt_embeds):
        image_is_pil = isinstance(control_image, PIL.Image.Image)
        image_is_tensor = isinstance(control_image, torch.Tensor)
        image_is_np = isinstance(control_image, np.ndarray)
        image_is_pil_list = isinstance(control_image, list) and isinstance(control_image[0], PIL.Image.Image)
        image_is_tensor_list = isinstance(control_image, list) and isinstance(control_image[0], torch.Tensor)
        image_is_np_list = isinstance(control_image, list) and isinstance(control_image[0], np.ndarray)

        if (
                not image_is_pil
                and not image_is_tensor
                and not image_is_np
                and not image_is_pil_list
                and not image_is_tensor_list
                and not image_is_np_list
        ):
            raise TypeError(
                f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(control_image)}"
            )

        if image_is_pil:
            image_batch_size = 1
        else:
            image_batch_size = len(control_image)

        if prompt is not None and isinstance(prompt, str):
            prompt_batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            prompt_batch_size = len(prompt)
        elif prompt_embeds is not None:
            prompt_batch_size = prompt_embeds.shape[0]

        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
            raise ValueError(
                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
            )

    def prepare_control_image(

            self,

            control_image,

            width,

            height,

            batch_size,

            num_images_per_prompt,

            device,

            dtype,

            crops_coords=None,

            resize_mode="Default",

            do_classifier_free_guidance=False,

            guess_mode=False,

    ):
        control_image = self.control_image_processor.preprocess(control_image,
                                                                height=height, width=width,
                                                                crops_coords=crops_coords,
                                                                resize_mode=resize_mode).to(dtype=torch.float32)
        control_image_batch_size = control_image.shape[0]

        if control_image_batch_size == 1:
            repeat_by = batch_size
        else:
            # image batch size is the same as prompt batch size
            repeat_by = num_images_per_prompt

        control_image = control_image.repeat_interleave(repeat_by, dim=0)

        control_image = control_image.to(device=device, dtype=dtype)

        if do_classifier_free_guidance and not guess_mode:
            control_image = torch.cat([control_image] * 2)

        return control_image

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(self,

                        batch_size,

                        num_channels_latents,

                        height,

                        width,

                        dtype,

                        device,

                        generator,

                        latents=None,

                        ):
        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    def prepare_inpaint_latents(

            self,

            batch_size,

            num_channels_latents,

            height,

            width,

            dtype,

            device,

            generator,

            latents=None,

            image=None,

            timestep=None,

            is_strength_max=True,

            add_noise=True,

            return_noise=False,

            return_image_latents=False,

    ):
        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if (image is None or timestep is None) and not is_strength_max:
            raise ValueError(
                "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
                "However, either the image or the noise timestep has not been provided."
            )

        if return_image_latents or (latents is None and not is_strength_max):
            image = image.to(device=device, dtype=dtype)

            if image.shape[1] == 4:
                image_latents = image
            else:
                image_latents = self._encode_vae_image(image=image, generator=generator)
            image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)

        if latents is None and add_noise:
            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
            # if strength is 1. then initialise the latents to noise, else initial to image + noise
            latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
            # if pure noise then scale the initial latents by the  Scheduler's init sigma
            latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
        elif add_noise:
            noise = latents.to(device)
            latents = noise * self.scheduler.init_noise_sigma
        else:
            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
            latents = image_latents.to(device)

        outputs = (latents,)

        if return_noise:
            outputs += (noise,)

        if return_image_latents:
            outputs += (image_latents,)

        return outputs

    def prepare_mask_latents(

            self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance

    ):
        # resize the mask to latents shape as we concatenate the mask to the latents
        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
        # and half precision
        mask = torch.nn.functional.interpolate(
            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
        )
        mask = mask.to(device=device, dtype=dtype)

        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
        if mask.shape[0] < batch_size:
            if not batch_size % mask.shape[0] == 0:
                raise ValueError(
                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
                    " of masks that you pass is divisible by the total requested batch size."
                )
            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)

        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask

        masked_image_latents = None
        if masked_image is not None:
            masked_image = masked_image.to(device=device, dtype=dtype)
            masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
            if masked_image_latents.shape[0] < batch_size:
                if not batch_size % masked_image_latents.shape[0] == 0:
                    raise ValueError(
                        "The passed images and the required batch size don't match. Images are supposed to be duplicated"
                        f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
                        " Make sure the number of images that you pass is divisible by the total requested batch size."
                    )
                masked_image_latents = masked_image_latents.repeat(
                    batch_size // masked_image_latents.shape[0], 1, 1, 1
                )

            masked_image_latents = (
                torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
            )

            # aligning device to prevent device errors when concating it with the latent model input
            masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)

        return mask, masked_image_latents

    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
        dtype = image.dtype
        if self.vae.config.force_upcast:
            image = image.float()
            self.vae.to(dtype=torch.float32)

        if isinstance(generator, list):
            image_latents = [
                retrieve_latents(self.vae.encode(image[i: i + 1]), generator=generator[i])
                for i in range(image.shape[0])
            ]
            image_latents = torch.cat(image_latents, dim=0)
        else:
            image_latents = retrieve_latents(self.vae.encode(image), generator=generator)

        if self.vae.config.force_upcast:
            self.vae.to(dtype)

        image_latents = image_latents.to(dtype)
        image_latents = self.vae.config.scaling_factor * image_latents

        return image_latents

    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 _get_inpaint_add_time_ids(

            self,

            original_size,

            crops_coords_top_left,

            target_size,

            aesthetic_score,

            negative_aesthetic_score,

            dtype,

            text_encoder_projection_dim=None,

    ):

        add_time_ids = list(original_size + crops_coords_top_left + target_size)
        add_neg_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
                and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_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. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model."
            )
        elif (
                expected_add_embed_dim < passed_add_embed_dim
                and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_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. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model."
            )
        elif 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)
        add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)

        return add_time_ids, add_neg_time_ids

    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,
                LoRAXFormersAttnProcessor,
                LoRAAttnProcessor2_0,
                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, embedding_dim=512, dtype=torch.float32):
        """

        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298



        Args:

            timesteps (`torch.Tensor`):

                generate embedding vectors at these timesteps

            embedding_dim (`int`, *optional*, defaults to 512):

                dimension of the embeddings to generate

            dtype:

                data type of the generated embeddings



        Returns:

            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), 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

    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.get_timesteps
    def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):
        # get the original timestep using init_timestep
        if denoising_start is None:
            init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
            t_start = max(num_inference_steps - init_timestep, 0)
        else:
            t_start = 0

        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order:]

        # Strength is irrelevant if we directly request a timestep to start at;
        # that is, strength is determined by the denoising_start instead.
        if denoising_start is not None:
            discrete_timestep_cutoff = int(
                round(
                    self.scheduler.config.num_train_timesteps
                    - (denoising_start * self.scheduler.config.num_train_timesteps)
                )
            )

            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
            if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
                # if the scheduler is a 2nd order scheduler we might have to do +1
                # because `num_inference_steps` might be even given that every timestep
                # (except the highest one) is duplicated. If `num_inference_steps` is even it would
                # mean that we cut the timesteps in the middle of the denoising step
                # (between 1st and 2nd devirative) which leads to incorrect results. By adding 1
                # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler
                num_inference_steps = num_inference_steps + 1

            # because t_n+1 >= t_n, we slice the timesteps starting from the end
            timesteps = timesteps[-num_inference_steps:]
            return timesteps, num_inference_steps

        return timesteps, num_inference_steps - t_start

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

    @property
    def guidance_rescale(self):
        return self._guidance_rescale

    @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 denoising_end(self):
        return self._denoising_end

    @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,

            prompt_2: Optional[Union[str, List[str]]] = None,

            height: Optional[int] = None,

            width: Optional[int] = None,

            num_inference_steps: int = 50,

            timesteps: List[int] = None,

            denoising_end: Optional[float] = None,

            guidance_scale: float = 5.0,

            negative_prompt: Optional[Union[str, List[str]]] = None,

            negative_prompt_2: 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,

            latents: Optional[torch.FloatTensor] = None,

            prompt_embeds: Optional[torch.FloatTensor] = None,

            negative_prompt_embeds: Optional[torch.FloatTensor] = None,

            pooled_prompt_embeds: Optional[torch.FloatTensor] = None,

            negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,

            ip_adapter_image: Optional[PipelineImageInput] = None,

            ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,

            output_type: Optional[str] = "pil",

            return_dict: bool = True,

            cross_attention_kwargs: Optional[Dict[str, Any]] = None,

            guidance_rescale: float = 0.0,

            original_size: Optional[Tuple[int, int]] = None,

            crops_coords_top_left: Tuple[int, int] = (0, 0),

            target_size: Optional[Tuple[int, int]] = None,

            negative_original_size: Optional[Tuple[int, int]] = None,

            negative_crops_coords_top_left: Tuple[int, int] = (0, 0),

            negative_target_size: Optional[Tuple[int, int]] = None,

            clip_skip: Optional[int] = None,

            callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,

            callback_on_step_end_tensor_inputs: List[str] = ["latents"],

            gradio_progress=None,

            exposure=0.0,

            **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.

            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

            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.

            timesteps (`List[int]`, *optional*):

                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument

                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is

                passed will be used. Must be in descending order.

            denoising_end (`float`, *optional*):

                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be

                completed before it is intentionally prematurely terminated. As a result, the returned sample will

                still retain a substantial amount of noise as determined by the discrete timesteps selected by the

                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a

                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image

                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)

            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`).

            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

            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.

            latents (`torch.FloatTensor`, *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 (`torch.FloatTensor`, *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.FloatTensor`, *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.FloatTensor`, *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.FloatTensor`, *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.

            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.

            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):

                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters.

                Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding

                if `do_classifier_free_guidance` is set to `True`.

                If not provided, embeddings are computed from the `ip_adapter_image` 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_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).

            guidance_rescale (`float`, *optional*, defaults to 0.0):

                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are

                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of

                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).

                Guidance rescale factor should fix overexposure when using zero terminal SNR.

            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 (`Tuple[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 (`Tuple[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.

            callback_on_step_end (`Callable`, *optional*):

                A function that calls at the end of each denoising steps during the inference. The function is called

                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,

                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by

                `callback_on_step_end_tensor_inputs`.

            callback_on_step_end_tensor_inputs (`List`, *optional*):

                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list

                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the

                `._callback_tensor_inputs` attribute of your pipeline class.



        Examples:



        Returns:

            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:

            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a

            `tuple`. When returning a tuple, the first element is a list with the generated images.

        """

        callback = kwargs.pop("callback", None)
        callback_steps = kwargs.pop("callback_steps", None)

        if callback is not None:
            deprecate(
                "callback",
                "1.0.0",
                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )
        if callback_steps is not None:
            deprecate(
                "callback_steps",
                "1.0.0",
                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )

        # 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)

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            prompt_2,
            height,
            width,
            callback_steps,
            negative_prompt,
            negative_prompt_2,
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
            ip_adapter_image,
            ip_adapter_image_embeds,
            callback_on_step_end_tensor_inputs,
        )

        self._guidance_scale = guidance_scale
        self._guidance_rescale = guidance_rescale
        self._clip_skip = clip_skip
        self._cross_attention_kwargs = cross_attention_kwargs
        self._denoising_end = denoising_end
        self._interrupt = False

        # 2. 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]

        device = self._execution_device

        # 3. Encode input prompt
        lora_scale = (
            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
        )

        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = self.encode_prompt(
            prompt=prompt,
            prompt_2=prompt_2,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=self.do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            negative_prompt_2=negative_prompt_2,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            lora_scale=lora_scale,
            clip_skip=self.clip_skip,
        )

        # 4. Prepare timesteps
        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)

        # 5. Prepare latent variables
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 6. 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)

        # 7. Prepare added time ids & embeddings
        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,
            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,
                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)

        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
            image_embeds = self.prepare_ip_adapter_image_embeds(
                ip_adapter_image,
                ip_adapter_image_embeds,
                device,
                batch_size * num_images_per_prompt,
                self.do_classifier_free_guidance,
            )

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

        # 8.1 Apply denoising_end
        if (
                self.denoising_end is not None
                and isinstance(self.denoising_end, float)
                and self.denoising_end > 0
                and self.denoising_end < 1
        ):
            discrete_timestep_cutoff = int(
                round(
                    self.scheduler.config.num_train_timesteps
                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
                )
            )
            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
            timesteps = timesteps[:num_inference_steps]

        # 9. Optionally get Guidance Scale Embedding
        timestep_cond = None
        if self.unet.config.time_cond_proj_dim is not None:
            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
            timestep_cond = self.get_guidance_scale_embedding(
                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
            ).to(device=device, dtype=latents.dtype)

        latents = latents + exposure

        self._num_timesteps = len(timesteps)
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if self.interrupt:
                    continue

                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents

                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                # predict the noise residual
                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
                    added_cond_kwargs["image_embeds"] = image_embeds
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    timestep_cond=timestep_cond,
                    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)
                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)

                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs:
                        callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                    latents = callback_outputs.pop("latents", latents)
                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
                    negative_pooled_prompt_embeds = callback_outputs.pop(
                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
                    )
                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
                    negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

                if gradio_progress is not None:
                    gradio_progress((i + 1) / int(self._num_timesteps),
                                    "Brainstorming...",
                                    total=int(self._num_timesteps))

                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)

            # 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
            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)

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def control_sketch(

            self,

            prompt: Union[str, List[str]] = None,

            prompt_2: Optional[Union[str, List[str]]] = None,

            control_image: PipelineImageInput = None,

            height: Optional[int] = None,

            width: Optional[int] = None,

            num_inference_steps: int = 50,

            denoising_end: Optional[float] = None,

            guidance_scale: float = 5.0,

            negative_prompt: Optional[Union[str, List[str]]] = None,

            negative_prompt_2: 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,

            latents: Optional[torch.FloatTensor] = None,

            prompt_embeds: Optional[torch.FloatTensor] = None,

            negative_prompt_embeds: Optional[torch.FloatTensor] = None,

            pooled_prompt_embeds: Optional[torch.FloatTensor] = None,

            negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,

            ip_adapter_image: Optional[PipelineImageInput] = None,

            ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,

            output_type: Optional[str] = "pil",

            return_dict: bool = True,

            cross_attention_kwargs: Optional[Dict[str, Any]] = None,

            controlnet_conditioning_scale: Union[float, List[float]] = 1.0,

            guess_mode: bool = False,

            control_guidance_start: Union[float, List[float]] = 0.0,

            control_guidance_end: Union[float, List[float]] = 1.0,

            original_size: Tuple[int, int] = None,

            crops_coords_top_left: Tuple[int, int] = (0, 0),

            target_size: Tuple[int, int] = None,

            negative_original_size: Optional[Tuple[int, int]] = None,

            negative_crops_coords_top_left: Tuple[int, int] = (0, 0),

            negative_target_size: Optional[Tuple[int, int]] = None,

            clip_skip: Optional[int] = None,

            callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,

            callback_on_step_end_tensor_inputs: List[str] = ["latents"],

            gradio_progress=None,

            **kwargs,

    ):
        r"""

        The call function to the pipeline for generation.



        Args:

            prompt (`str` or `List[str]`, *optional*):

                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.

            prompt_2 (`str` or `List[str]`, *optional*):

                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is

                used in both text-encoders.

            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:

                    `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):

                The ControlNet input condition to provide guidance to the `unet` for generation. If the type is

                specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be

                accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height

                and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in

                `init`, images must be passed as a list such that each element of the list can be correctly batched for

                input to a single ControlNet.

            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):

                The height in pixels of the generated image. 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. 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.

            denoising_end (`float`, *optional*):

                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be

                completed before it is intentionally prematurely terminated. As a result, the returned sample will

                still retain a substantial amount of noise as determined by the discrete timesteps selected by the

                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a

                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image

                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)

            guidance_scale (`float`, *optional*, defaults to 5.0):

                A higher guidance scale value encourages the model to generate images closely linked to the text

                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.

            negative_prompt (`str` or `List[str]`, *optional*):

                The prompt or prompts to guide what to not include in image generation. If not defined, you need to

                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).

            negative_prompt_2 (`str` or `List[str]`, *optional*):

                The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`

                and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.

            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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies

                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.

            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):

                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make

                generation deterministic.

            latents (`torch.FloatTensor`, *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 is generated by sampling using the supplied random `generator`.

            prompt_embeds (`torch.FloatTensor`, *optional*):

                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not

                provided, text embeddings are generated from the `prompt` input argument.

            negative_prompt_embeds (`torch.FloatTensor`, *optional*):

                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If

                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.

            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):

                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If

                not provided, pooled text embeddings are generated from `prompt` input argument.

            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):

                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt

                weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input

                argument.

            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.

            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):

                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters.

                Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding

                if `do_classifier_free_guidance` is set to `True`.

                If not provided, embeddings are computed from the `ip_adapter_image` input argument.

            output_type (`str`, *optional*, defaults to `"pil"`):

                The output format of the generated image. Choose between `PIL.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.

            cross_attention_kwargs (`dict`, *optional*):

                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in

                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).

            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):

                The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added

                to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set

                the corresponding scale as a list.

            guess_mode (`bool`, *optional*, defaults to `False`):

                The ControlNet encoder tries to recognize the content of the input image even if you remove all

                prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.

            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):

                The percentage of total steps at which the ControlNet starts applying.

            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):

                The percentage of total steps at which the ControlNet stops applying.

            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 (`Tuple[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 (`Tuple[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.

            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.

            callback_on_step_end (`Callable`, *optional*):

                A function that calls at the end of each denoising steps during the inference. The function is called

                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,

                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by

                `callback_on_step_end_tensor_inputs`.

            callback_on_step_end_tensor_inputs (`List`, *optional*):

                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list

                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the

                `._callback_tensor_inputs` attribute of your pipeine class.



        Examples:



        Returns:

            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:

                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,

                otherwise a `tuple` is returned containing the output images.

        """

        callback = kwargs.pop("callback", None)
        callback_steps = kwargs.pop("callback_steps", None)

        if callback is not None:
            deprecate(
                "callback",
                "1.0.0",
                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
            )
        if callback_steps is not None:
            deprecate(
                "callback_steps",
                "1.0.0",
                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
            )

        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet

        # align format for control guidance
        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
            control_guidance_start, control_guidance_end = (
                mult * [control_guidance_start],
                mult * [control_guidance_end],
            )

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            prompt_2,
            None,
            None,
            callback_steps,
            negative_prompt,
            negative_prompt_2,
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
            ip_adapter_image,
            ip_adapter_image_embeds,
            callback_on_step_end_tensor_inputs,

            control_image,
            controlnet_conditioning_scale,
            control_guidance_start,
            control_guidance_end,
        )

        self._guidance_scale = guidance_scale
        self._clip_skip = clip_skip
        self._cross_attention_kwargs = cross_attention_kwargs
        self._denoising_end = denoising_end

        # 2. 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]

        device = self._execution_device

        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)

        global_pool_conditions = (
            controlnet.config.global_pool_conditions
            if isinstance(controlnet, ControlNetModel)
            else controlnet.nets[0].config.global_pool_conditions
        )
        guess_mode = guess_mode or global_pool_conditions

        # 3.1 Encode input prompt
        text_encoder_lora_scale = (
            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
        )
        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = self.encode_prompt(
            prompt,
            prompt_2,
            device,
            num_images_per_prompt,
            self.do_classifier_free_guidance,
            negative_prompt,
            negative_prompt_2,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
            clip_skip=self.clip_skip,
        )

        # 3.2 Encode ip_adapter_image
        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
            image_embeds = self.prepare_ip_adapter_image_embeds(
                ip_adapter_image,
                ip_adapter_image_embeds,
                device,
                batch_size * num_images_per_prompt,
                self.do_classifier_free_guidance,
            )

        # 4. Prepare image
        if isinstance(controlnet, ControlNetModel):
            control_image = self.prepare_control_image(
                control_image=control_image,
                width=width,
                height=height,
                batch_size=batch_size * num_images_per_prompt,
                num_images_per_prompt=num_images_per_prompt,
                device=device,
                dtype=controlnet.dtype,
                do_classifier_free_guidance=self.do_classifier_free_guidance,
                guess_mode=guess_mode,
            )
            height, width = control_image.shape[-2:]
        elif isinstance(controlnet, MultiControlNetModel):
            control_images = []

            for control_image_ in control_image:
                control_image_ = self.prepare_control_image(
                    control_image=control_image_,
                    width=width,
                    height=height,
                    batch_size=batch_size * num_images_per_prompt,
                    num_images_per_prompt=num_images_per_prompt,
                    device=device,
                    dtype=controlnet.dtype,
                    do_classifier_free_guidance=self.do_classifier_free_guidance,
                    guess_mode=guess_mode,
                )

                control_images.append(control_image_)

            control_image = control_images
            height, width = control_image[0].shape[-2:]
        else:
            assert False

        # 5. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps
        self._num_timesteps = len(timesteps)

        # 6. Prepare latent variables
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 6.5 Optionally get Guidance Scale Embedding
        timestep_cond = None
        if self.unet.config.time_cond_proj_dim is not None:
            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
            timestep_cond = self.get_guidance_scale_embedding(
                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
            ).to(device=device, dtype=latents.dtype)

        # 7. 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)

        # 7.1 Create tensor stating which controlnets to keep
        controlnet_keep = []
        for i in range(len(timesteps)):
            keeps = [
                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
                for s, e in zip(control_guidance_start, control_guidance_end)
            ]
            controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)

        # 7.2 Prepare added time ids & embeddings
        if isinstance(control_image, list):
            original_size = original_size or control_image[0].shape[-2:]
        else:
            original_size = original_size or control_image.shape[-2:]
        target_size = target_size or (height, width)

        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,
            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,
                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)

        # 8. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order

        # 8.1 Apply denoising_end
        if (
                self.denoising_end is not None
                and isinstance(self.denoising_end, float)
                and self.denoising_end > 0
                and self.denoising_end < 1
        ):
            discrete_timestep_cutoff = int(
                round(
                    self.scheduler.config.num_train_timesteps
                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
                )
            )
            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
            timesteps = timesteps[:num_inference_steps]

        is_unet_compiled = is_compiled_module(self.unet)
        is_controlnet_compiled = is_compiled_module(self.controlnet)
        is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # Relevant thread:
                # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
                if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:
                    torch._inductor.cudagraph_mark_step_begin()
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}

                # controlnet(s) inference
                if guess_mode and self.do_classifier_free_guidance:
                    # Infer ControlNet only for the conditional batch.
                    control_model_input = latents
                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
                    controlnet_added_cond_kwargs = {
                        "text_embeds": add_text_embeds.chunk(2)[1],
                        "time_ids": add_time_ids.chunk(2)[1],
                    }
                else:
                    control_model_input = latent_model_input
                    controlnet_prompt_embeds = prompt_embeds
                    controlnet_added_cond_kwargs = added_cond_kwargs

                if isinstance(controlnet_keep[i], list):
                    cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
                else:
                    controlnet_cond_scale = controlnet_conditioning_scale
                    if isinstance(controlnet_cond_scale, list):
                        controlnet_cond_scale = controlnet_cond_scale[0]
                    cond_scale = controlnet_cond_scale * controlnet_keep[i]

                down_block_res_samples, mid_block_res_sample = self.controlnet(
                    control_model_input,
                    t,
                    encoder_hidden_states=controlnet_prompt_embeds,
                    controlnet_cond=control_image,
                    conditioning_scale=cond_scale,
                    guess_mode=guess_mode,
                    added_cond_kwargs=controlnet_added_cond_kwargs,
                    return_dict=False,
                )

                if guess_mode and self.do_classifier_free_guidance:
                    # Infered ControlNet only for the conditional batch.
                    # To apply the output of ControlNet to both the unconditional and conditional batches,
                    # add 0 to the unconditional batch to keep it unchanged.
                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])

                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
                    added_cond_kwargs["image_embeds"] = image_embeds

                # predict the noise residual
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    timestep_cond=timestep_cond,
                    cross_attention_kwargs=self.cross_attention_kwargs,
                    down_block_additional_residuals=down_block_res_samples,
                    mid_block_additional_residual=mid_block_res_sample,
                    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)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs:
                        callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                    latents = callback_outputs.pop("latents", latents)
                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

                if gradio_progress is not None:
                    gradio_progress((i + 1) / int(self._num_timesteps),
                                    "Brainstorming...",
                                    total=int(self._num_timesteps))

        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)

            # 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

            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)

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def control_inpaint_sketch(

            self,

            prompt: Union[str, List[str]] = None,

            prompt_2: Optional[Union[str, List[str]]] = None,

            image: PipelineImageInput = None,

            mask_image: PipelineImageInput = None,

            control_image: Union[

                PipelineImageInput,

                List[PipelineImageInput],

            ] = None,

            height: Optional[int] = None,

            width: Optional[int] = None,

            padding_mask_crop: Optional[int] = None,

            strength: float = 0.9999,

            num_inference_steps: int = 50,

            denoising_start: Optional[float] = None,

            denoising_end: Optional[float] = None,

            guidance_scale: float = 5.0,

            negative_prompt: Optional[Union[str, List[str]]] = None,

            negative_prompt_2: 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,

            latents: Optional[torch.FloatTensor] = None,

            prompt_embeds: Optional[torch.FloatTensor] = None,

            negative_prompt_embeds: Optional[torch.FloatTensor] = None,

            ip_adapter_image: Optional[PipelineImageInput] = None,

            ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,

            pooled_prompt_embeds: Optional[torch.FloatTensor] = None,

            negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,

            output_type: Optional[str] = "pil",

            return_dict: bool = True,

            cross_attention_kwargs: Optional[Dict[str, Any]] = None,

            controlnet_conditioning_scale: Union[float, List[float]] = 1.0,

            guess_mode: bool = False,

            control_guidance_start: Union[float, List[float]] = 0.0,

            control_guidance_end: Union[float, List[float]] = 1.0,

            guidance_rescale: float = 0.0,

            original_size: Tuple[int, int] = None,

            crops_coords_top_left: Tuple[int, int] = (0, 0),

            target_size: Tuple[int, int] = None,

            aesthetic_score: float = 6.0,

            negative_aesthetic_score: float = 2.5,

            clip_skip: Optional[int] = None,

            callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,

            callback_on_step_end_tensor_inputs: List[str] = ["latents"],

            gradio_progress=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.

            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

            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 self.unet.config.sample_size * self.vae_scale_factor):

                The height in pixels of the generated image.

            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):

                The width in pixels of the generated image.

            padding_mask_crop (`int`, *optional*, defaults to `None`):

                The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If

                `padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ration of the image and

                contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on

                the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large

                and contain information inreleant for inpainging, such as background.

            strength (`float`, *optional*, defaults to 0.9999):

                Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be

                between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the

                `strength`. The number of denoising steps depends on the amount of noise initially added. When

                `strength` is 1, added noise will be maximum and the denoising process will run for the full number of

                iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked

                portion of the reference `image`. Note that in the case of `denoising_start` being declared as an

                integer, the value of `strength` will be ignored.

            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.

            denoising_start (`float`, *optional*):

                When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be

                bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and

                it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,

                strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline

                is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image

                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).

            denoising_end (`float`, *optional*):

                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be

                completed before it is intentionally prematurely terminated. As a result, the returned sample will

                still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be

                denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the

                final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline

                forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image

                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).

            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`).

            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.FloatTensor`, *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.FloatTensor`, *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.

            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.

            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):

                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters.

                Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding

                if `do_classifier_free_guidance` is set to `True`.

                If not provided, embeddings are computed from the `ip_adapter_image` input argument.

            pooled_prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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.

            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`, *optional*):

                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)

                to make generation deterministic.

            latents (`torch.FloatTensor`, *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`.

            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.

            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 `(width, height)` 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 (`Tuple[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 `(width, height)`. 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).

            aesthetic_score (`float`, *optional*, defaults to 6.0):

                Used to simulate an aesthetic score of the generated image by influencing the positive text condition.

                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_aesthetic_score (`float`, *optional*, defaults to 2.5):

                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). Can be used to

                simulate an aesthetic score of the generated image by influencing the negative text condition.

            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.

            callback_on_step_end (`Callable`, *optional*):

                A function that calls at the end of each denoising steps during the inference. The function is called

                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,

                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by

                `callback_on_step_end_tensor_inputs`.

            callback_on_step_end_tensor_inputs (`List`, *optional*):

                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list

                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the

                `._callback_tensor_inputs` attribute of your pipeine class.



        Examples:



        Returns:

            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:

            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a

            `tuple. `tuple. When returning a tuple, the first element is a list with the generated images.

        """

        callback = kwargs.pop("callback", None)
        callback_steps = kwargs.pop("callback_steps", None)

        if callback is not None:
            deprecate(
                "callback",
                "1.0.0",
                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
            )
        if callback_steps is not None:
            deprecate(
                "callback_steps",
                "1.0.0",
                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
            )

        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet

        # align format for control guidance
        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
            control_guidance_start, control_guidance_end = (
                mult * [control_guidance_start],
                mult * [control_guidance_end],
            )

        # # 0.0 Default height and width to unet
        # height = height or self.unet.config.sample_size * self.vae_scale_factor
        # width = width or self.unet.config.sample_size * self.vae_scale_factor

        # 0.1 align format for control guidance
        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
            control_guidance_start, control_guidance_end = (
                mult * [control_guidance_start],
                mult * [control_guidance_end],
            )

        # 1. Check inputs
        self.check_inputs(
            prompt,
            prompt_2,
            None,
            None,
            callback_steps,
            negative_prompt,
            negative_prompt_2,
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
            ip_adapter_image,
            ip_adapter_image_embeds,
            callback_on_step_end_tensor_inputs,

            control_image,
            controlnet_conditioning_scale,
            control_guidance_start,
            control_guidance_end,

            padding_mask_crop,
            strength,
            mask_image,
            num_inference_steps
        )

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

        # 2. 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]

        device = self._execution_device

        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)

        # 3. Encode input prompt
        text_encoder_lora_scale = (
            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
        )

        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = self.encode_prompt(
            prompt=prompt,
            prompt_2=prompt_2,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=self.do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            negative_prompt_2=negative_prompt_2,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
            clip_skip=self.clip_skip,
        )

        # 3.1 Encode ip_adapter_image
        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
            image_embeds = self.prepare_ip_adapter_image_embeds(
                ip_adapter_image,
                ip_adapter_image_embeds,
                device,
                batch_size * num_images_per_prompt,
                self.do_classifier_free_guidance,
            )

        # 4. set timesteps
        def denoising_value_valid(dnv):
            return isinstance(dnv, float) and 0 < dnv < 1

        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps, num_inference_steps = self.get_timesteps(
            num_inference_steps,
            strength,
            device,
            denoising_start=denoising_start if denoising_value_valid(denoising_start) else None,
        )
        # check that number of inference steps is not < 1 - as this doesn't make sense
        if num_inference_steps < 1:
            raise ValueError(
                f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
                f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
            )
        # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
        # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
        is_strength_max = strength == 1.0
        self._num_timesteps = len(timesteps)

        # 5. Preprocess mask and image - resizes image and mask w.r.t height and width
        # 5.1 Prepare init image
        if padding_mask_crop is not None:
            height, width = self.image_processor.get_default_height_width(image, height, width)
            crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)
            resize_mode = "fill"
        else:
            crops_coords = None
            resize_mode = "default"

        original_image = image
        init_image = self.image_processor.preprocess(
            image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode
        )
        init_image = init_image.to(dtype=torch.float32)

        # 5.2 Prepare control images
        if isinstance(controlnet, ControlNetModel):
            control_image = self.prepare_control_image(
                control_image=control_image,
                width=width,
                height=height,
                batch_size=batch_size * num_images_per_prompt,
                num_images_per_prompt=num_images_per_prompt,
                device=device,
                dtype=controlnet.dtype,
                crops_coords=crops_coords,
                resize_mode=resize_mode,
                do_classifier_free_guidance=self.do_classifier_free_guidance,
                guess_mode=guess_mode,
            )
        elif isinstance(controlnet, MultiControlNetModel):
            control_images = []

            for control_image_ in control_image:
                control_image_ = self.prepare_control_image(
                    control_image=control_image_,
                    width=width,
                    height=height,
                    batch_size=batch_size * num_images_per_prompt,
                    num_images_per_prompt=num_images_per_prompt,
                    device=device,
                    dtype=controlnet.dtype,
                    crops_coords=crops_coords,
                    resize_mode=resize_mode,
                    do_classifier_free_guidance=self.do_classifier_free_guidance,
                    guess_mode=guess_mode,
                )

                control_images.append(control_image_)

            control_image = control_images
        else:
            raise ValueError(f"{controlnet.__class__} is not supported.")

        # 5.3 Prepare mask
        mask = self.mask_processor.preprocess(
            mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
        )

        masked_image = init_image * (mask < 0.5)
        _, _, height, width = init_image.shape

        # 6. Prepare latent variables
        num_channels_latents = self.vae.config.latent_channels
        num_channels_unet = self.unet.config.in_channels
        return_image_latents = num_channels_unet == 4

        add_noise = True if denoising_start is None else False
        latents_outputs = self.prepare_inpaint_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
            image=init_image,
            timestep=latent_timestep,
            is_strength_max=is_strength_max,
            add_noise=add_noise,
            return_noise=True,
            return_image_latents=return_image_latents,
        )

        if return_image_latents:
            latents, noise, image_latents = latents_outputs
        else:
            latents, noise = latents_outputs

        # 7. Prepare mask latent variables
        mask, masked_image_latents = self.prepare_mask_latents(
            mask,
            masked_image,
            batch_size * num_images_per_prompt,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            self.do_classifier_free_guidance,
        )

        # 8. Check that sizes of mask, masked image and latents match
        if num_channels_unet == 9:
            # default case for runwayml/stable-diffusion-inpainting
            num_channels_mask = mask.shape[1]
            num_channels_masked_image = masked_image_latents.shape[1]
            if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
                raise ValueError(
                    f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
                    f" {self.unet.config.in_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."
                )
        elif num_channels_unet != 4:
            raise ValueError(
                f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
            )
        # 8.1 Prepare extra step kwargs.
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 8.2 Create tensor stating which controlnets to keep
        controlnet_keep = []
        for i in range(len(timesteps)):
            keeps = [
                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
                for s, e in zip(control_guidance_start, control_guidance_end)
            ]
            if isinstance(self.controlnet, MultiControlNetModel):
                controlnet_keep.append(keeps)
            else:
                controlnet_keep.append(keeps[0])

        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        height, width = latents.shape[-2:]
        height = height * self.vae_scale_factor
        width = width * self.vae_scale_factor

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

        # 10. Prepare added time ids & embeddings
        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, add_neg_time_ids = self._get_inpaint_add_time_ids(
            original_size,
            crops_coords_top_left,
            target_size,
            aesthetic_score,
            negative_aesthetic_score,
            dtype=prompt_embeds.dtype,
            text_encoder_projection_dim=text_encoder_projection_dim,
        )
        add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)

        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_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)
            add_time_ids = torch.cat([add_neg_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)

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

        if (
                denoising_end is not None
                and denoising_start is not None
                and denoising_value_valid(denoising_end)
                and denoising_value_valid(denoising_start)
                and denoising_start >= denoising_end
        ):
            raise ValueError(
                f"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: "
                + f" {denoising_end} when using type float."
            )
        elif denoising_end is not None and denoising_value_valid(denoising_end):
            discrete_timestep_cutoff = int(
                round(
                    self.scheduler.config.num_train_timesteps
                    - (denoising_end * self.scheduler.config.num_train_timesteps)
                )
            )
            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
            timesteps = timesteps[:num_inference_steps]

        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents

                # concat latents, mask, masked_image_latents in the channel dimension
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}

                # controlnet(s) inference
                if guess_mode and self.do_classifier_free_guidance:
                    # Infer ControlNet only for the conditional batch.
                    control_model_input = latents
                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
                    controlnet_added_cond_kwargs = {
                        "text_embeds": add_text_embeds.chunk(2)[1],
                        "time_ids": add_time_ids.chunk(2)[1],
                    }
                else:
                    control_model_input = latent_model_input
                    controlnet_prompt_embeds = prompt_embeds
                    controlnet_added_cond_kwargs = added_cond_kwargs

                if isinstance(controlnet_keep[i], list):
                    cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
                else:
                    controlnet_cond_scale = controlnet_conditioning_scale
                    if isinstance(controlnet_cond_scale, list):
                        controlnet_cond_scale = controlnet_cond_scale[0]
                    cond_scale = controlnet_cond_scale * controlnet_keep[i]

                # # Resize control_image to match the size of the input to the controlnet
                # if control_image.shape[-2:] != control_model_input.shape[-2:]:
                #     control_image = F.interpolate(control_image, size=control_model_input.shape[-2:], mode="bilinear", align_corners=False)

                down_block_res_samples, mid_block_res_sample = self.controlnet(
                    control_model_input,
                    t,
                    encoder_hidden_states=controlnet_prompt_embeds,
                    controlnet_cond=control_image,
                    conditioning_scale=cond_scale,
                    guess_mode=guess_mode,
                    added_cond_kwargs=controlnet_added_cond_kwargs,
                    return_dict=False,
                )

                if guess_mode and self.do_classifier_free_guidance:
                    # Infered ControlNet only for the conditional batch.
                    # To apply the output of ControlNet to both the unconditional and conditional batches,
                    # add 0 to the unconditional batch to keep it unchanged.
                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])

                if ip_adapter_image is not None:
                    added_cond_kwargs["image_embeds"] = image_embeds

                if num_channels_unet == 9:
                    latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)

                # predict the noise residual
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=self.cross_attention_kwargs,
                    down_block_additional_residuals=down_block_res_samples,
                    mid_block_additional_residual=mid_block_res_sample,
                    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)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                if self.do_classifier_free_guidance and guidance_rescale > 0.0:
                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

                if num_channels_unet == 4:
                    init_latents_proper = image_latents
                    if self.do_classifier_free_guidance:
                        init_mask, _ = mask.chunk(2)
                    else:
                        init_mask = mask

                    if i < len(timesteps) - 1:
                        noise_timestep = timesteps[i + 1]
                        init_latents_proper = self.scheduler.add_noise(
                            init_latents_proper, noise, torch.tensor([noise_timestep])
                        )

                    latents = (1 - init_mask) * init_latents_proper + init_mask * latents

                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs:
                        callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                    latents = callback_outputs.pop("latents", latents)
                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

                if gradio_progress is not None:
                    gradio_progress((i + 1) / int(self._num_timesteps),
                                    "Brainstorming...",
                                    total=int(self._num_timesteps))

        # make sure the VAE is in float32 mode, as it overflows in float16
        if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
            self.upcast_vae()
            latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)

        # If we do sequential model offloading, let's offload unet and controlnet
        # manually for max memory savings
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.unet.to("cpu")
            self.controlnet.to("cpu")
            torch.cuda.empty_cache()

        if not output_type == "latent":
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
        else:
            return StableDiffusionXLPipelineOutput(images=latents)

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

        if padding_mask_crop is not None:
            image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]
        else:
            mask_image = to_pil_image(mask_image[0].cpu())
            original_image = to_pil_image(original_image[0].cpu())
            image = [self.image_processor.apply_overlay(mask_image, original_image, i) for i in image]

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return image

        return StableDiffusionXLPipelineOutput(images=image)

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def inpaint(

            self,

            prompt: Union[str, List[str]] = None,

            prompt_2: Optional[Union[str, List[str]]] = None,

            image: PipelineImageInput = None,

            mask_image: PipelineImageInput = None,

            masked_image_latents: torch.FloatTensor = None,

            height: Optional[int] = None,

            width: Optional[int] = None,

            padding_mask_crop: Optional[int] = None,

            strength: float = 0.9999,

            num_inference_steps: int = 50,

            timesteps: List[int] = None,

            denoising_start: Optional[float] = None,

            denoising_end: Optional[float] = None,

            guidance_scale: float = 7.5,

            negative_prompt: Optional[Union[str, List[str]]] = None,

            negative_prompt_2: 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,

            latents: Optional[torch.FloatTensor] = None,

            prompt_embeds: Optional[torch.FloatTensor] = None,

            negative_prompt_embeds: Optional[torch.FloatTensor] = None,

            pooled_prompt_embeds: Optional[torch.FloatTensor] = None,

            negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,

            ip_adapter_image: Optional[PipelineImageInput] = None,

            ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,

            output_type: Optional[str] = "pil",

            return_dict: bool = True,

            cross_attention_kwargs: Optional[Dict[str, Any]] = None,

            guidance_rescale: float = 0.0,

            original_size: Tuple[int, int] = None,

            crops_coords_top_left: Tuple[int, int] = (0, 0),

            target_size: Tuple[int, int] = None,

            negative_original_size: Optional[Tuple[int, int]] = None,

            negative_crops_coords_top_left: Tuple[int, int] = (0, 0),

            negative_target_size: Optional[Tuple[int, int]] = None,

            aesthetic_score: float = 6.0,

            negative_aesthetic_score: float = 2.5,

            clip_skip: Optional[int] = None,

            callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,

            callback_on_step_end_tensor_inputs: List[str] = ["latents"],

            gradio_progress=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.

            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

            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 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.

            padding_mask_crop (`int`, *optional*, defaults to `None`):

                The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If

                `padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ration of the image and

                contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on

                the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large

                and contain information inreleant for inpainging, such as background.

            strength (`float`, *optional*, defaults to 0.9999):

                Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be

                between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the

                `strength`. The number of denoising steps depends on the amount of noise initially added. When

                `strength` is 1, added noise will be maximum and the denoising process will run for the full number of

                iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked

                portion of the reference `image`. Note that in the case of `denoising_start` being declared as an

                integer, the value of `strength` will be ignored.

            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.

            timesteps (`List[int]`, *optional*):

                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument

                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is

                passed will be used. Must be in descending order.

            denoising_start (`float`, *optional*):

                When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be

                bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and

                it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,

                strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline

                is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image

                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).

            denoising_end (`float`, *optional*):

                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be

                completed before it is intentionally prematurely terminated. As a result, the returned sample will

                still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be

                denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the

                final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline

                forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image

                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).

            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`).

            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.FloatTensor`, *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.FloatTensor`, *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.FloatTensor`, *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.FloatTensor`, *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.

            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.

            ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):

                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters.

                Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding

                if `do_classifier_free_guidance` is set to `True`.

                If not provided, embeddings are computed from the `ip_adapter_image` input argument.

            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`, *optional*):

                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)

                to make generation deterministic.

            latents (`torch.FloatTensor`, *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`.

            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.

            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 (`Tuple[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 (`Tuple[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.

            aesthetic_score (`float`, *optional*, defaults to 6.0):

                Used to simulate an aesthetic score of the generated image by influencing the positive text condition.

                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_aesthetic_score (`float`, *optional*, defaults to 2.5):

                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). Can be used to

                simulate an aesthetic score of the generated image by influencing the negative text condition.

            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.

            callback_on_step_end (`Callable`, *optional*):

                A function that calls at the end of each denoising steps during the inference. The function is called

                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,

                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by

                `callback_on_step_end_tensor_inputs`.

            callback_on_step_end_tensor_inputs (`List`, *optional*):

                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list

                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the

                `._callback_tensor_inputs` attribute of your pipeline class.



        Examples:



        Returns:

            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:

            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a

            `tuple. `tuple. When returning a tuple, the first element is a list with the generated images.

        """

        callback = kwargs.pop("callback", None)
        callback_steps = kwargs.pop("callback_steps", None)

        if callback is not None:
            deprecate(
                "callback",
                "1.0.0",
                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )
        if callback_steps is not None:
            deprecate(
                "callback_steps",
                "1.0.0",
                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )

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

        # 1. Check inputs
        self.check_inputs(
            prompt,
            prompt_2,
            height,
            width,
            callback_steps,
            negative_prompt,
            negative_prompt_2,
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
            ip_adapter_image,
            ip_adapter_image_embeds,
            callback_on_step_end_tensor_inputs,

            None,
            None,
            None,
            None,
            padding_mask_crop,

            strength,
            mask_image,
            None
        )

        self._guidance_scale = guidance_scale
        self._guidance_rescale = guidance_rescale
        self._clip_skip = clip_skip
        self._cross_attention_kwargs = cross_attention_kwargs
        self._denoising_end = denoising_end
        self._denoising_start = denoising_start
        self._interrupt = False

        # 2. 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]

        device = self._execution_device

        # 3. Encode input prompt
        text_encoder_lora_scale = (
            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
        )

        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = self.encode_prompt(
            prompt=prompt,
            prompt_2=prompt_2,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=self.do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            negative_prompt_2=negative_prompt_2,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
            clip_skip=self.clip_skip,
        )

        # 4. set timesteps
        def denoising_value_valid(dnv):
            return isinstance(dnv, float) and 0 < dnv < 1

        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
        timesteps, num_inference_steps = self.get_timesteps(
            num_inference_steps,
            strength,
            device,
            denoising_start=denoising_start if denoising_value_valid(denoising_start) else None,
        )
        # check that number of inference steps is not < 1 - as this doesn't make sense
        if num_inference_steps < 1:
            raise ValueError(
                f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
                f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
            )
        # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
        # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
        is_strength_max = strength == 1.0

        # 5. Preprocess mask and image
        if padding_mask_crop is not None:
            crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)
            resize_mode = "fill"
        else:
            crops_coords = None
            resize_mode = "default"

        original_image = image
        init_image = self.image_processor.preprocess(
            image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode
        )
        init_image = init_image.to(dtype=torch.float32)

        mask = self.mask_processor.preprocess(
            mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
        )

        if masked_image_latents is not None:
            masked_image = masked_image_latents
        elif init_image.shape[1] == 4:
            # if images are in latent space, we can't mask it
            masked_image = None
        else:
            masked_image = init_image * (mask < 0.5)

        # 6. Prepare latent variables
        num_channels_latents = self.vae.config.latent_channels
        num_channels_unet = self.unet.config.in_channels
        return_image_latents = num_channels_unet == 4

        add_noise = True if denoising_start is None else False
        latents_outputs = self.prepare_inpaint_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
            image=init_image,
            timestep=latent_timestep,
            is_strength_max=is_strength_max,
            add_noise=add_noise,
            return_noise=True,
            return_image_latents=return_image_latents,
        )

        if return_image_latents:
            latents, noise, image_latents = latents_outputs
        else:
            latents, noise = latents_outputs

        # 7. Prepare mask latent variables
        mask, masked_image_latents = self.prepare_mask_latents(
            mask,
            masked_image,
            batch_size * num_images_per_prompt,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            self.do_classifier_free_guidance,
        )

        # 8. Check that sizes of mask, masked image and latents match
        if num_channels_unet == 9:
            # default case for runwayml/stable-diffusion-inpainting
            num_channels_mask = mask.shape[1]
            num_channels_masked_image = masked_image_latents.shape[1]
            if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
                raise ValueError(
                    f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
                    f" {self.unet.config.in_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."
                )
        elif num_channels_unet != 4:
            raise ValueError(
                f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
            )
        # 8.1 Prepare extra step kwargs.
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        height, width = latents.shape[-2:]
        height = height * self.vae_scale_factor
        width = width * self.vae_scale_factor

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

        # 10. Prepare added time ids & embeddings
        if negative_original_size is None:
            negative_original_size = original_size
        if negative_target_size is None:
            negative_target_size = target_size

        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, add_neg_time_ids = self._get_inpaint_add_time_ids(
            original_size,
            crops_coords_top_left,
            target_size,
            aesthetic_score,
            negative_aesthetic_score,
            dtype=prompt_embeds.dtype,
            text_encoder_projection_dim=text_encoder_projection_dim,
        )
        add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)

        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_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)
            add_time_ids = torch.cat([add_neg_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)

        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
            image_embeds = self.prepare_ip_adapter_image_embeds(
                ip_adapter_image,
                ip_adapter_image_embeds,
                device,
                batch_size * num_images_per_prompt,
                self.do_classifier_free_guidance,
            )

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

        if (
                self.denoising_end is not None
                and self.denoising_start is not None
                and denoising_value_valid(self.denoising_end)
                and denoising_value_valid(self.denoising_start)
                and self.denoising_start >= self.denoising_end
        ):
            raise ValueError(
                f"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: "
                + f" {self.denoising_end} when using type float."
            )
        elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):
            discrete_timestep_cutoff = int(
                round(
                    self.scheduler.config.num_train_timesteps
                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
                )
            )
            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
            timesteps = timesteps[:num_inference_steps]

        # 11.1 Optionally get Guidance Scale Embedding
        timestep_cond = None
        if self.unet.config.time_cond_proj_dim is not None:
            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
            timestep_cond = self.get_guidance_scale_embedding(
                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
            ).to(device=device, dtype=latents.dtype)

        self._num_timesteps = len(timesteps)
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if self.interrupt:
                    continue
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents

                # concat latents, mask, masked_image_latents in the channel dimension
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                if num_channels_unet == 9:
                    latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)

                # predict the noise residual
                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
                    added_cond_kwargs["image_embeds"] = image_embeds
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    timestep_cond=timestep_cond,
                    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)
                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)

                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

                if num_channels_unet == 4:
                    init_latents_proper = image_latents
                    if self.do_classifier_free_guidance:
                        init_mask, _ = mask.chunk(2)
                    else:
                        init_mask = mask

                    if i < len(timesteps) - 1:
                        noise_timestep = timesteps[i + 1]
                        init_latents_proper = self.scheduler.add_noise(
                            init_latents_proper, noise, torch.tensor([noise_timestep])
                        )

                    latents = (1 - init_mask) * init_latents_proper + init_mask * latents

                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs:
                        callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                    latents = callback_outputs.pop("latents", latents)
                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
                    negative_pooled_prompt_embeds = callback_outputs.pop(
                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
                    )
                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
                    add_neg_time_ids = callback_outputs.pop("add_neg_time_ids", add_neg_time_ids)
                    mask = callback_outputs.pop("mask", mask)
                    masked_image_latents = callback_outputs.pop("masked_image_latents", masked_image_latents)

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

                if gradio_progress is not None:
                    gradio_progress((i + 1) / int(self._num_timesteps),
                                    "Brainstorming...",
                                    total=int(self._num_timesteps))

                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)

            # 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:
            return StableDiffusionXLPipelineOutput(images=latents)

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

        if padding_mask_crop is not None:
            image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]
        else:
            mask_image = to_pil_image(mask_image[0].cpu())
            original_image = to_pil_image(original_image[0].cpu())
            image = [self.image_processor.apply_overlay(mask_image, original_image, i) for i in image]

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return image

        return StableDiffusionXLPipelineOutput(images=image)