pipeline.py

# Copyright 2024 Smoretalk, Inc. 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 PIL.Image

import torch
import torch.nn.functional as F
from packaging import version

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, ControlNetModel, ImageProjection, UNet2DConditionModel
from diffusers.models.attention_processor import (
    Attention,
    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_invisible_watermark_available,
    is_torch_xla_available,
    logging,
    replace_example_docstring,
    scale_lora_layers,
    unscale_lora_layers,
)
from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput


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

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

    XLA_AVAILABLE = True
else:
    XLA_AVAILABLE = False

from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel


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

EXAMPLE_DOC_STRING = """

    Examples:

        ```py

        >>> # !pip install opencv-python transformers accelerate

        >>> from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL

        >>> from diffusers.utils import load_image

        >>> import numpy as np

        >>> import torch



        >>> import cv2

        >>> from PIL import Image



        >>> prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"

        >>> negative_prompt = "low quality, bad quality, sketches"



        >>> # download an image

        >>> image = load_image(

        ...     "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"

        ... )



        >>> # initialize the models and pipeline

        >>> controlnet_conditioning_scale = 0.5  # recommended for good generalization

        >>> controlnet = ControlNetModel.from_pretrained(

        ...     "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16

        ... )

        >>> vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)

        >>> pipe = StableDiffusionXLControlNetPipeline.from_pretrained(

        ...     "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16

        ... )

        >>> pipe.enable_model_cpu_offload()



        >>> # get canny image

        >>> image = np.array(image)

        >>> image = cv2.Canny(image, 100, 200)

        >>> image = image[:, :, None]

        >>> image = np.concatenate([image, image, image], axis=2)

        >>> canny_image = Image.fromarray(image)



        >>> # generate image

        >>> image = pipe(

        ...     prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image

        ... ).images[0]

        ```

"""


class PAGIdentitySelfAttnProcessor:
    r"""

    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).

    """

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

    def __call__(

        self,

        attn: Attention,

        hidden_states: torch.FloatTensor,

        encoder_hidden_states: Optional[torch.FloatTensor] = None,

        attention_mask: Optional[torch.FloatTensor] = None,

        temb: Optional[torch.FloatTensor] = None,

        *args,

        **kwargs,

    ) -> torch.FloatTensor:
        if len(args) > 0 or kwargs.get("scale", None) is not None:
            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
            deprecate("scale", "1.0.0", deprecation_message)
        
        residual = hidden_states
        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim
        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
        
        # chunk
        hidden_states_org, hidden_states_ptb = hidden_states.chunk(2)
        
        # original path
        batch_size, sequence_length, _ = hidden_states_org.shape

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states_org = attn.group_norm(hidden_states_org.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states_org)
        key = attn.to_k(hidden_states_org)
        value = attn.to_v(hidden_states_org)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states_org = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states_org = hidden_states_org.to(query.dtype)
        
        # linear proj
        hidden_states_org = attn.to_out[0](hidden_states_org)
        # dropout
        hidden_states_org = attn.to_out[1](hidden_states_org)

        if input_ndim == 4:
            hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)

        # perturbed path (identity attention)
        batch_size, sequence_length, _ = hidden_states_ptb.shape

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states_ptb = attn.group_norm(hidden_states_ptb.transpose(1, 2)).transpose(1, 2)

        value = attn.to_v(hidden_states_ptb)
        
        # hidden_states_ptb = torch.zeros(value.shape).to(value.get_device())
        hidden_states_ptb = value
        
        hidden_states_ptb = hidden_states_ptb.to(query.dtype)
        
        # linear proj
        hidden_states_ptb = attn.to_out[0](hidden_states_ptb)
        # dropout
        hidden_states_ptb = attn.to_out[1](hidden_states_ptb)

        if input_ndim == 4:
            hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)

        # cat
        hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class PAGCFGIdentitySelfAttnProcessor:
    r"""

    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).

    """

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

    def __call__(

        self,

        attn: Attention,

        hidden_states: torch.FloatTensor,

        encoder_hidden_states: Optional[torch.FloatTensor] = None,

        attention_mask: Optional[torch.FloatTensor] = None,

        temb: Optional[torch.FloatTensor] = None,

        *args,

        **kwargs,

    ) -> torch.FloatTensor:
        if len(args) > 0 or kwargs.get("scale", None) is not None:
            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
            deprecate("scale", "1.0.0", deprecation_message)
        
        residual = hidden_states
        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim
        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
        
        # chunk
        hidden_states_uncond, hidden_states_org, hidden_states_ptb = hidden_states.chunk(3)
        hidden_states_org = torch.cat([hidden_states_uncond, hidden_states_org])
        
        # original path
        batch_size, sequence_length, _ = hidden_states_org.shape

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states_org = attn.group_norm(hidden_states_org.transpose(1, 2)).transpose(1, 2)
        
        query = attn.to_q(hidden_states_org)
        key = attn.to_k(hidden_states_org)
        value = attn.to_v(hidden_states_org)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states_org = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states_org = hidden_states_org.to(query.dtype)
        
        # linear proj
        hidden_states_org = attn.to_out[0](hidden_states_org)
        # dropout
        hidden_states_org = attn.to_out[1](hidden_states_org)

        if input_ndim == 4:
            hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)

        # perturbed path (identity attention)
        batch_size, sequence_length, _ = hidden_states_ptb.shape

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states_ptb = attn.group_norm(hidden_states_ptb.transpose(1, 2)).transpose(1, 2)

        value = attn.to_v(hidden_states_ptb)
        hidden_states_ptb = value
        hidden_states_ptb = hidden_states_ptb.to(query.dtype)
        
        # linear proj
        hidden_states_ptb = attn.to_out[0](hidden_states_ptb)
        # dropout
        hidden_states_ptb = attn.to_out[1](hidden_states_ptb)

        if input_ndim == 4:
            hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)

        # cat
        hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


# # 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_img2img.retrieve_latents
# 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")


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,

    sigmas: Optional[List[float]] = 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 override the timestep spacing strategy of the scheduler. If `timesteps` is passed,

            `num_inference_steps` and `sigmas` must be `None`.

        sigmas (`List[float]`, *optional*):

            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,

            `num_inference_steps` and `timesteps` must be `None`.



    Returns:

        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the

        second element is the number of inference steps.

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


class StableDiffusionXLControlNetPipeline(
    DiffusionPipeline,
    StableDiffusionMixin,
    TextualInversionLoaderMixin,
    StableDiffusionXLLoraLoaderMixin,
    IPAdapterMixin,
    FromSingleFileMixin,
):
    r"""

    Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.



    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods

    implemented for all pipelines (downloading, 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.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights

        - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights

        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files

        - [`~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 ([`~transformers.CLIPTextModel`]):

            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).

        text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):

            Second frozen text-encoder

            ([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).

        tokenizer ([`~transformers.CLIPTokenizer`]):

            A `CLIPTokenizer` to tokenize text.

        tokenizer_2 ([`~transformers.CLIPTokenizer`]):

            A `CLIPTokenizer` to tokenize text.

        unet ([`UNet2DConditionModel`]):

            A `UNet2DConditionModel` to denoise the encoded image latents.

        controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):

            Provides additional conditioning to the `unet` during the denoising process. If you set multiple

            ControlNets as a list, the outputs from each ControlNet are added together to create one combined

            additional conditioning.

        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 should 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](https://github.com/ShieldMnt/invisible-watermark/) library to

            watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no

            watermarker is used.

    """

    # leave controlnet out on purpose because it iterates with unet
    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
    _optional_components = [
        "tokenizer",
        "tokenizer_2",
        "text_encoder",
        "text_encoder_2",
        "feature_extractor",
        "image_encoder",
    ]
    _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,

        force_zeros_for_empty_prompt: bool = True,

        add_watermarker: Optional[bool] = None,

        feature_extractor: CLIPImageProcessor = None,

        image_encoder: CLIPVisionModelWithProjection = None,

    ):
        super().__init__()

        if isinstance(controlnet, (list, tuple)):
            controlnet = MultiControlNetModel(controlnet)

        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,
            feature_extractor=feature_extractor,
            image_encoder=image_encoder,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
        self.control_image_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
        )
        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()

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

        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)

    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
    def encode_prompt(

        self,

        prompt: str,

        prompt_2: Optional[str] = None,

        device: Optional[torch.device] = None,

        num_images_per_prompt: int = 1,

        do_classifier_free_guidance: bool = True,

        negative_prompt: Optional[str] = None,

        negative_prompt_2: Optional[str] = None,

        prompt_embeds: Optional[torch.Tensor] = None,

        negative_prompt_embeds: Optional[torch.Tensor] = None,

        pooled_prompt_embeds: Optional[torch.Tensor] = None,

        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,

        lora_scale: Optional[float] = None,

        clip_skip: Optional[int] = None,

    ):
        r"""

        Encodes the prompt into text encoder hidden states.



        Args:

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

                prompt to be encoded

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

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

                used in both text-encoders

            device: (`torch.device`):

                torch device

            num_images_per_prompt (`int`):

                number of images that should be generated per prompt

            do_classifier_free_guidance (`bool`):

                whether to use classifier free guidance or not

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

                The prompt or prompts not to guide the image generation. If not defined, one has to pass

                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is

                less than `1`).

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

                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and

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

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

                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not

                provided, text embeddings will be generated from `prompt` input argument.

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

                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt

                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input

                argument.

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

                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.

                If not provided, pooled text embeddings will be generated from `prompt` input argument.

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

                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt

                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`

                input argument.

            lora_scale (`float`, *optional*):

                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.

            clip_skip (`int`, *optional*):

                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that

                the output of the pre-final layer will be used for computing the prompt embeddings.

        """
        device = device or self._execution_device

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

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

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

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

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

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

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

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

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

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

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

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

                # We are only ALWAYS interested in the pooled output of the final text encoder
                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,

        image,

        callback_steps,

        negative_prompt=None,

        negative_prompt_2=None,

        prompt_embeds=None,

        negative_prompt_embeds=None,

        pooled_prompt_embeds=None,

        ip_adapter_image=None,

        ip_adapter_image_embeds=None,

        negative_pooled_prompt_embeds=None,

        controlnet_conditioning_scale=1.0,

        control_guidance_start=0.0,

        control_guidance_end=1.0,

        callback_on_step_end_tensor_inputs=None,

    ):
        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 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_image(image, prompt, prompt_embeds)
        elif (
            isinstance(self.controlnet, MultiControlNetModel)
            or is_compiled
            and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
        ):
            if not isinstance(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 image):
                raise ValueError("A single batch of multiple conditionings are supported at the moment.")
            elif len(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(image)} images and {len(self.controlnet.nets)} ControlNets."
                )

            for image_ in image:
                self.check_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"
                )

    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
    def check_image(self, image, prompt, prompt_embeds):
        image_is_pil = isinstance(image, PIL.Image.Image)
        image_is_tensor = isinstance(image, torch.Tensor)
        image_is_np = isinstance(image, np.ndarray)
        image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
        image_is_np_list = isinstance(image, list) and isinstance(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(image)}"
            )

        if image_is_pil:
            image_batch_size = 1
        else:
            image_batch_size = len(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}"
            )

    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
    def prepare_image(

        self,

        image,

        width,

        height,

        batch_size,

        num_images_per_prompt,

        device,

        dtype,

        do_classifier_free_guidance=False,

        do_perturbed_attention_guidance=False,

        guess_mode=False,

    ):
        image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
        image_batch_size = image.shape[0]

        if 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

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

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

        if do_classifier_free_guidance and not do_perturbed_attention_guidance and not guess_mode:
            image = torch.cat([image] * 2)
        elif not do_classifier_free_guidance and do_perturbed_attention_guidance and not guess_mode:
            image = torch.cat([image] * 2)
        elif do_classifier_free_guidance and do_perturbed_attention_guidance and not guess_mode:
            image = torch.cat([image] * 3)

        return 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,
            int(height) // self.vae_scale_factor,
            int(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

    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids
    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

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
    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,
            ),
        )
        # if xformers or torch_2_0 is used attention block does not need
        # to be in float32 which can save lots of memory
        if use_torch_2_0_or_xformers:
            self.vae.post_quant_conv.to(dtype)
            self.vae.decoder.conv_in.to(dtype)
            self.vae.decoder.mid_block.to(dtype)

    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
    def get_guidance_scale_embedding(

        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32

    ) -> torch.Tensor:
        """

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



        Args:

            w (`torch.Tensor`):

                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.

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

                Dimension of the embeddings to generate.

            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):

                Data type of the generated embeddings.



        Returns:

            `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.

        """
        assert len(w.shape) == 1
        w = w * 1000.0

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

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

    @property
    def pag_scale(self):
        return self._pag_scale
    
    @property
    def do_adversarial_guidance(self):
        return self._pag_scale > 0
    
    @property
    def pag_adaptive_scaling(self):
        return self._pag_adaptive_scaling
    
    @property
    def do_pag_adaptive_scaling(self):
        return self._pag_adaptive_scaling > 0
    
    @property
    def pag_drop_rate(self):
        return self._pag_drop_rate
    
    @property
    def pag_applied_layers(self):
        return self._pag_applied_layers
    
    @property
    def pag_applied_layers_index(self):
        return self._pag_applied_layers_index

    @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,

        image: PipelineImageInput = None,

        height: Optional[int] = None,

        width: Optional[int] = None,

        num_inference_steps: int = 50,

        timesteps: List[int] = None,

        sigmas: List[float] = None,

        denoising_end: Optional[float] = None,

        guidance_scale: float = 5.0,

        pag_scale: float = 0.0,

        pag_adaptive_scaling: float = 0.0,

        pag_drop_rate: float = 0.5,

        pag_applied_layers: List[str] = ['mid'], #['down', 'mid', 'up']

        pag_applied_layers_index: List[str] = None, #['d4', 'd5', 'm0']

        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.Tensor] = None,

        prompt_embeds: Optional[torch.Tensor] = None,

        negative_prompt_embeds: Optional[torch.Tensor] = None,

        pooled_prompt_embeds: Optional[torch.Tensor] = None,

        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,

        ip_adapter_image: Optional[PipelineImageInput] = None,

        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = 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,

        guidance_rescale: float = 0.0,

        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[

            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]

        ] = None,

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

        **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.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:

                    `List[List[torch.Tensor]]`, `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.Tensor`, 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.

            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.

            sigmas (`List[float]`, *optional*):

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

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

                will be used.

            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.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor]`, *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.

            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.

            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`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):

                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of

                each denoising step during the inference. 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.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`",
            )

        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs

        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,
            image,
            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,
            controlnet_conditioning_scale,
            control_guidance_start,
            control_guidance_end,
            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
        
        self._pag_scale = pag_scale
        self._pag_adaptive_scaling = pag_adaptive_scaling
        self._pag_drop_rate = pag_drop_rate
        self._pag_applied_layers = pag_applied_layers
        self._pag_applied_layers_index = pag_applied_layers_index

        # 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,
            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,
        )

        # 3.2 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):
            image = self.prepare_image(
                image=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,
                do_perturbed_attention_guidance=self.do_perturbed_attention_guidance,
                guess_mode=guess_mode,
            )
            height, width = image.shape[-2:]
        elif isinstance(controlnet, MultiControlNetModel):
            images = []
            for image_ in image:
                image_ = self.prepare_image(
                    image=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,
                    do_perturbed_attention_guidance=self.do_perturbed_attention_guidance,
                    guess_mode=guess_mode,
                )
                images.append(image_)
            image = images
            height, width = image[0].shape[-2:]
        else:
            assert False

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

        # 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(image, list):
            original_size = original_size or image[0].shape[-2:]
        else:
            original_size = original_size or 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

        #cfg
        if self.do_classifier_free_guidance and not self.do_adversarial_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)
        #pag
        elif not self.do_classifier_free_guidance and self.do_adversarial_guidance:
            prompt_embeds = torch.cat([prompt_embeds, prompt_embeds], dim=0)
            add_text_embeds = torch.cat([add_text_embeds, add_text_embeds], dim=0)
            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
        #both
        elif self.do_classifier_free_guidance and self.do_adversarial_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds, prompt_embeds], dim=0)
            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds, add_text_embeds], dim=0)
            add_time_ids = torch.cat([negative_add_time_ids, 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 = 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]

        # 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)

        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")

        # 10. Create down mid and up layer lists
        if self.do_adversarial_guidance:
            down_layers = []
            mid_layers = []
            up_layers = []
            for name, module in self.unet.named_modules():
                if 'attn1' in name and 'to' not in name:
                    layer_type = name.split('.')[0].split('_')[0]
                    if layer_type == 'down':
                        down_layers.append(module)
                    elif layer_type == 'mid':
                        mid_layers.append(module)
                    elif layer_type == 'up':
                        up_layers.append(module)
                    else:
                        raise ValueError(f"Invalid layer type: {layer_type}")
            
        self._num_timesteps = len(timesteps)
        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()
                
                if self.interrupt:
                    continue
                
                # expand the latents if we are doing guidance
                #cfg
                if self.do_classifier_free_guidance and not self.do_adversarial_guidance:
                    latent_model_input = torch.cat([latents] * 2)
                #pag
                elif not self.do_classifier_free_guidance and self.do_adversarial_guidance:
                    latent_model_input = torch.cat([latents] * 2)
                #both
                elif self.do_classifier_free_guidance and self.do_adversarial_guidance:
                    latent_model_input = torch.cat([latents] * 3)
                #no
                else:
                    latent_model_input = latents
                    
                # change attention layer in UNet if use PAG
                if self.do_adversarial_guidance:
                    
                    if self.do_classifier_free_guidance:
                        replace_processor = PAGCFGIdentitySelfAttnProcessor()
                    else:
                        replace_processor = PAGIdentitySelfAttnProcessor()

                    if(self.pag_applied_layers_index):
                        drop_layers = self.pag_applied_layers_index
                        for drop_layer in drop_layers:
                            layer_number = int(drop_layer[1:])
                            try:
                                if drop_layer[0] == 'd':
                                    down_layers[layer_number].processor = replace_processor
                                elif drop_layer[0] == 'm':
                                    mid_layers[layer_number].processor = replace_processor
                                elif drop_layer[0] == 'u':
                                    up_layers[layer_number].processor = replace_processor
                                else:
                                    raise ValueError(f"Invalid layer type: {drop_layer[0]}")
                            except IndexError:
                                raise ValueError(
                                    f"Invalid layer index: {drop_layer}. Available layers: {len(down_layers)} down layers, {len(mid_layers)} mid layers, {len(up_layers)} up layers."
                                )
                    elif(self.pag_applied_layers):
                        drop_full_layers = self.pag_applied_layers
                        for drop_full_layer in drop_full_layers:
                            try:
                                if drop_full_layer == "down":
                                    for down_layer in down_layers:
                                        down_layer.processor = replace_processor
                                elif drop_full_layer == "mid":
                                    for mid_layer in mid_layers:
                                        mid_layer.processor = replace_processor
                                elif drop_full_layer == "up":
                                    for up_layer in up_layers:
                                        up_layer.processor = replace_processor
                                else:
                                    raise ValueError(f"Invalid layer type: {drop_full_layer}")
                            except IndexError:
                                raise ValueError(
                                    f"Invalid layer index: {drop_full_layer}. Available layers are: down, mid and up. If you need to specify each layer index, you can use `pag_applied_layers_index`"
                                )
                                
                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

                # 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=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 and not self.do_perturbed_attention_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])
                elif guess_mode and not self.do_classifier_free_guidance and self.do_perturbed_attention_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])
                elif guess_mode and self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:
                    raise ValueError(
                        "guess mode with both guidance is not supported."
                    )

                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 and not self.do_adversarial_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)
                # pag
                elif not self.do_classifier_free_guidance and self.do_adversarial_guidance:
                    noise_pred_original, noise_pred_perturb = noise_pred.chunk(2)
                    
                    signal_scale = self.pag_scale
                    if self.do_pag_adaptive_scaling:
                        signal_scale = self.pag_scale - self.pag_adaptive_scaling * (1000-t)
                        if signal_scale<0:
                            signal_scale = 0
                    
                    noise_pred = noise_pred_original + signal_scale * (noise_pred_original - noise_pred_perturb)
                    
                # both
                elif self.do_classifier_free_guidance and self.do_adversarial_guidance:
                    
                    noise_pred_uncond, noise_pred_text, noise_pred_text_perturb = noise_pred.chunk(3)
                    
                    signal_scale = self.pag_scale
                    if self.do_pag_adaptive_scaling:
                        signal_scale = self.pag_scale - self.pag_adaptive_scaling * (1000-t)
                        if signal_scale<0:
                            signal_scale = 0
                    
                    noise_pred = noise_pred_text + (self.guidance_scale-1.0) * (noise_pred_text - noise_pred_uncond) + signal_scale * (noise_pred_text - noise_pred_text_perturb)
                
                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 XLA_AVAILABLE:
                    xm.mark_step()

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

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

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

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

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

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

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

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image,)

        #Change the attention layers back to original ones after PAG was applied
        if self.do_adversarial_guidance:
            if(self.pag_applied_layers_index):
                drop_layers = self.pag_applied_layers_index
                for drop_layer in drop_layers:
                    layer_number = int(drop_layer[1:])
                    try:
                        if drop_layer[0] == 'd':
                            down_layers[layer_number].processor = AttnProcessor2_0()
                        elif drop_layer[0] == 'm':
                            mid_layers[layer_number].processor = AttnProcessor2_0()
                        elif drop_layer[0] == 'u':
                            up_layers[layer_number].processor = AttnProcessor2_0()
                        else:
                            raise ValueError(f"Invalid layer type: {drop_layer[0]}")
                    except IndexError:
                        raise ValueError(
                            f"Invalid layer index: {drop_layer}. Available layers: {len(down_layers)} down layers, {len(mid_layers)} mid layers, {len(up_layers)} up layers."
                        )
            elif(self.pag_applied_layers):
                            drop_full_layers = self.pag_applied_layers
                            for drop_full_layer in drop_full_layers:
                                try:
                                    if drop_full_layer == "down":
                                        for down_layer in down_layers:
                                            down_layer.processor = AttnProcessor2_0()
                                    elif drop_full_layer == "mid":
                                        for mid_layer in mid_layers:
                                            mid_layer.processor = AttnProcessor2_0()
                                    elif drop_full_layer == "up":
                                        for up_layer in up_layers:
                                            up_layer.processor = AttnProcessor2_0()
                                    else:
                                        raise ValueError(f"Invalid layer type: {drop_full_layer}")
                                except IndexError:
                                    raise ValueError(
                                        f"Invalid layer index: {drop_full_layer}. Available layers are: down, mid and up. If you need to specify each layer index, you can use `pag_applied_layers_index`"
                                    )
        return StableDiffusionXLPipelineOutput(images=image)