content: # Copyright 2024 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import inspect
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  
  import numpy as np
  import PIL
  import torch
  import torch.nn as nn
  from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
  from diffusers.models import (
      AutoencoderKL,
      ImageProjection,
      T2IAdapter,
      UNet2DConditionModel,
  )
  from diffusers.pipelines.stable_diffusion_xl.pipeline_output import (
      StableDiffusionXLPipelineOutput,
  )
  from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import (
      StableDiffusionXLPipeline,
      rescale_noise_cfg,
      retrieve_timesteps,
  )
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import deprecate, logging
  from diffusers.utils.torch_utils import randn_tensor
  from einops import rearrange
  from transformers import (
      CLIPImageProcessor,
      CLIPTextModel,
      CLIPTextModelWithProjection,
      CLIPTokenizer,
      CLIPVisionModelWithProjection,
  )
  
  from ..loaders import CustomAdapterMixin
  from ..models.attention_processor import (
      DecoupledMVRowSelfAttnProcessor2_0,
      set_unet_2d_condition_attn_processor,
  )
  
  logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  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")
  
  
  class MVAdapterI2MVSDXLPipeline(StableDiffusionXLPipeline, CustomAdapterMixin):
      def __init__(
          self,
          vae: AutoencoderKL,
          text_encoder: CLIPTextModel,
          text_encoder_2: CLIPTextModelWithProjection,
          tokenizer: CLIPTokenizer,
          tokenizer_2: CLIPTokenizer,
          unet: UNet2DConditionModel,
          scheduler: KarrasDiffusionSchedulers,
          image_encoder: CLIPVisionModelWithProjection = None,
          feature_extractor: CLIPImageProcessor = None,
          force_zeros_for_empty_prompt: bool = True,
          add_watermarker: Optional[bool] = None,
      ):
          super().__init__(
              vae=vae,
              text_encoder=text_encoder,
              text_encoder_2=text_encoder_2,
              tokenizer=tokenizer,
              tokenizer_2=tokenizer_2,
              unet=unet,
              scheduler=scheduler,
              image_encoder=image_encoder,
              feature_extractor=feature_extractor,
              force_zeros_for_empty_prompt=force_zeros_for_empty_prompt,
              add_watermarker=add_watermarker,
          )
  
          self.control_image_processor = VaeImageProcessor(
              vae_scale_factor=self.vae_scale_factor,
              do_convert_rgb=True,
              do_normalize=False,
          )
  
      # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.prepare_latents
      def prepare_image_latents(
          self,
          image,
          timestep,
          batch_size,
          num_images_per_prompt,
          dtype,
          device,
          generator=None,
          add_noise=True,
      ):
          if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
              raise ValueError(
                  f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
              )
  
          latents_mean = latents_std = None
          if (
              hasattr(self.vae.config, "latents_mean")
              and self.vae.config.latents_mean is not None
          ):
              latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
          if (
              hasattr(self.vae.config, "latents_std")
              and self.vae.config.latents_std is not None
          ):
              latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
  
          # Offload text encoder if `enable_model_cpu_offload` was enabled
          if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
              self.text_encoder_2.to("cpu")
              torch.cuda.empty_cache()
  
          image = image.to(device=device, dtype=dtype)
  
          batch_size = batch_size * num_images_per_prompt
  
          if image.shape[1] == 4:
              init_latents = image
  
          else:
              # make sure the VAE is in float32 mode, as it overflows in float16
              if self.vae.config.force_upcast:
                  image = image.float()
                  self.vae.to(dtype=torch.float32)
  
              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."
                  )
  
              elif isinstance(generator, list):
                  if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
                      image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
                  elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
                      raise ValueError(
                          f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
                      )
  
                  init_latents = [
                      retrieve_latents(
                          self.vae.encode(image[i : i + 1]), generator=generator[i]
                      )
                      for i in range(batch_size)
                  ]
                  init_latents = torch.cat(init_latents, dim=0)
              else:
                  init_latents = retrieve_latents(
                      self.vae.encode(image), generator=generator
                  )
  
              if self.vae.config.force_upcast:
                  self.vae.to(dtype)
  
              init_latents = init_latents.to(dtype)
              if latents_mean is not None and latents_std is not None:
                  latents_mean = latents_mean.to(device=device, dtype=dtype)
                  latents_std = latents_std.to(device=device, dtype=dtype)
                  init_latents = (
                      (init_latents - latents_mean)
                      * self.vae.config.scaling_factor
                      / latents_std
                  )
              else:
                  init_latents = self.vae.config.scaling_factor * init_latents
  
          if (
              batch_size > init_latents.shape[0]
              and batch_size % init_latents.shape[0] == 0
          ):
              # expand init_latents for batch_size
              additional_image_per_prompt = batch_size // init_latents.shape[0]
              init_latents = torch.cat(
                  [init_latents] * additional_image_per_prompt, dim=0
              )
          elif (
              batch_size > init_latents.shape[0]
              and batch_size % init_latents.shape[0] != 0
          ):
              raise ValueError(
                  f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
              )
          else:
              init_latents = torch.cat([init_latents], dim=0)
  
          if add_noise:
              shape = init_latents.shape
              noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
              # get latents
              init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
  
          latents = init_latents
  
          return latents
  
      def prepare_control_image(
          self,
          image,
          width,
          height,
          batch_size,
          num_images_per_prompt,
          device,
          dtype,
          do_classifier_free_guidance=False,
          num_empty_images=0,  # for concat in batch like ImageDream
      ):
          assert hasattr(
              self, "control_image_processor"
          ), "control_image_processor is not initialized"
  
          image = self.control_image_processor.preprocess(
              image, height=height, width=width
          ).to(dtype=torch.float32)
  
          if num_empty_images > 0:
              image = torch.cat(
                  [image, torch.zeros_like(image[:num_empty_images])], dim=0
              )
  
          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  # always 1 for control image
  
          image = image.repeat_interleave(repeat_by, dim=0)
  
          image = image.to(device=device, dtype=dtype)
  
          if do_classifier_free_guidance:
              image = torch.cat([image] * 2)
  
          return image
  
      @torch.no_grad()
      def __call__(
          self,
          prompt: Union[str, List[str]] = None,
          prompt_2: Optional[Union[str, List[str]]] = None,
          height: Optional[int] = None,
          width: Optional[int] = None,
          num_inference_steps: int = 50,
          timesteps: List[int] = None,
          denoising_end: Optional[float] = None,
          guidance_scale: float = 5.0,
          negative_prompt: Optional[Union[str, List[str]]] = None,
          negative_prompt_2: Optional[Union[str, List[str]]] = None,
          num_images_per_prompt: Optional[int] = 1,
          eta: float = 0.0,
          generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
          latents: Optional[torch.FloatTensor] = None,
          prompt_embeds: Optional[torch.FloatTensor] = None,
          negative_prompt_embeds: Optional[torch.FloatTensor] = None,
          pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
          negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
          ip_adapter_image: Optional[PipelineImageInput] = None,
          ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
          output_type: Optional[str] = "pil",
          return_dict: bool = True,
          cross_attention_kwargs: Optional[Dict[str, Any]] = None,
          guidance_rescale: float = 0.0,
          original_size: Optional[Tuple[int, int]] = None,
          crops_coords_top_left: Tuple[int, int] = (0, 0),
          target_size: Optional[Tuple[int, int]] = None,
          negative_original_size: Optional[Tuple[int, int]] = None,
          negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
          negative_target_size: Optional[Tuple[int, int]] = None,
          clip_skip: Optional[int] = None,
          callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
          callback_on_step_end_tensor_inputs: List[str] = ["latents"],
          # NEW
          mv_scale: float = 1.0,
          # Camera or geometry condition
          control_image: Optional[PipelineImageInput] = None,
          control_conditioning_scale: Optional[float] = 1.0,
          control_conditioning_factor: float = 1.0,
          # Image condition
          reference_image: Optional[PipelineImageInput] = None,
          reference_conditioning_scale: Optional[float] = 1.0,
          **kwargs,
      ):
          r"""
          Function invoked when calling the pipeline for generation.
  
          Args:
              prompt (`str` or `List[str]`, *optional*):
                  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                  instead.
              prompt_2 (`str` or `List[str]`, *optional*):
                  The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
                  used in both text-encoders
              height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                  The height in pixels of the generated image. This is set to 1024 by default for the best results.
                  Anything below 512 pixels won't work well for
                  [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
                  and checkpoints that are not specifically fine-tuned on low resolutions.
              width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                  The width in pixels of the generated image. This is set to 1024 by default for the best results.
                  Anything below 512 pixels won't work well for
                  [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
                  and checkpoints that are not specifically fine-tuned on low resolutions.
              num_inference_steps (`int`, *optional*, defaults to 50):
                  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                  expense of slower inference.
              timesteps (`List[int]`, *optional*):
                  Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
                  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
                  passed will be used. Must be in descending order.
              denoising_end (`float`, *optional*):
                  When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
                  completed before it is intentionally prematurely terminated. As a result, the returned sample will
                  still retain a substantial amount of noise as determined by the discrete timesteps selected by the
                  scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
                  "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
                  Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
              guidance_scale (`float`, *optional*, defaults to 5.0):
                  Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                  `guidance_scale` is defined as `w` of equation 2. of [Imagen
                  Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                  1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                  usually at the expense of lower image quality.
              negative_prompt (`str` or `List[str]`, *optional*):
                  The prompt or prompts not to guide the image generation. If not defined, one has to pass
                  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                  less than `1`).
              negative_prompt_2 (`str` or `List[str]`, *optional*):
                  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
                  `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
              num_images_per_prompt (`int`, *optional*, defaults to 1):
                  The number of images to generate per prompt.
              eta (`float`, *optional*, defaults to 0.0):
                  Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                  [`schedulers.DDIMScheduler`], will be ignored for others.
              generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                  One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                  to make generation deterministic.
              latents (`torch.FloatTensor`, *optional*):
                  Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                  generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                  tensor will ge generated by sampling using the supplied random `generator`.
              prompt_embeds (`torch.FloatTensor`, *optional*):
                  Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                  provided, text embeddings will be generated from `prompt` input argument.
              negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                  Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                  weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                  argument.
              pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                  If not provided, pooled text embeddings will be generated from `prompt` input argument.
              negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
                  input argument.
              ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
              ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
                  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
                  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
                  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
                  provided, embeddings are computed from the `ip_adapter_image` input argument.
              output_type (`str`, *optional*, defaults to `"pil"`):
                  The output format of the generate image. Choose between
                  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
              return_dict (`bool`, *optional*, defaults to `True`):
                  Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
                  of a plain tuple.
              cross_attention_kwargs (`dict`, *optional*):
                  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                  `self.processor` in
                  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
              guidance_rescale (`float`, *optional*, defaults to 0.0):
                  Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
                  Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
                  [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
                  Guidance rescale factor should fix overexposure when using zero terminal SNR.
              original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                  If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
                  `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
                  explained in section 2.2 of
                  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
              crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
                  `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
                  `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
                  `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
                  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
              target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                  For most cases, `target_size` should be set to the desired height and width of the generated image. If
                  not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
                  section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
              negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                  To negatively condition the generation process based on a specific image resolution. Part of SDXL's
                  micro-conditioning as explained in section 2.2 of
                  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
                  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
              negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
                  To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
                  micro-conditioning as explained in section 2.2 of
                  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
                  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
              negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                  To negatively condition the generation process based on a target image resolution. It should be as same
                  as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
                  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
                  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
              callback_on_step_end (`Callable`, *optional*):
                  A function that calls at the end of each denoising steps during the inference. The function is called
                  with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                  callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                  `callback_on_step_end_tensor_inputs`.
              callback_on_step_end_tensor_inputs (`List`, *optional*):
                  The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                  will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                  `._callback_tensor_inputs` attribute of your pipeline class.
  
          Examples:
  
          Returns:
              [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
              [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
              `tuple`. When returning a tuple, the first element is a list with the generated images.
          """
  
          callback = kwargs.pop("callback", None)
          callback_steps = kwargs.pop("callback_steps", None)
  
          if callback is not None:
              deprecate(
                  "callback",
                  "1.0.0",
                  "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
              )
          if callback_steps is not None:
              deprecate(
                  "callback_steps",
                  "1.0.0",
                  "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
              )
  
          # 0. Default height and width to unet
          height = height or self.default_sample_size * self.vae_scale_factor
          width = width or self.default_sample_size * self.vae_scale_factor
  
          original_size = original_size or (height, width)
          target_size = target_size or (height, width)
  
          # 1. Check inputs. Raise error if not correct
          self.check_inputs(
              prompt,
              prompt_2,
              height,
              width,
              callback_steps,
              negative_prompt,
              negative_prompt_2,
              prompt_embeds,
              negative_prompt_embeds,
              pooled_prompt_embeds,
              negative_pooled_prompt_embeds,
              ip_adapter_image,
              ip_adapter_image_embeds,
              callback_on_step_end_tensor_inputs,
          )
  
          self._guidance_scale = guidance_scale
          self._guidance_rescale = guidance_rescale
          self._clip_skip = clip_skip
          self._cross_attention_kwargs = cross_attention_kwargs
          self._denoising_end = denoising_end
          self._interrupt = False
  
          # 2. Define call parameters
          if prompt is not None and isinstance(prompt, str):
              batch_size = 1
          elif prompt is not None and isinstance(prompt, list):
              batch_size = len(prompt)
          else:
              batch_size = prompt_embeds.shape[0]
  
          device = self._execution_device
  
          # 3. Encode input prompt
          lora_scale = (
              self.cross_attention_kwargs.get("scale", None)
              if self.cross_attention_kwargs is not None
              else None
          )
  
          (
              prompt_embeds,
              negative_prompt_embeds,
              pooled_prompt_embeds,
              negative_pooled_prompt_embeds,
          ) = self.encode_prompt(
              prompt=prompt,
              prompt_2=prompt_2,
              device=device,
              num_images_per_prompt=num_images_per_prompt,
              do_classifier_free_guidance=self.do_classifier_free_guidance,
              negative_prompt=negative_prompt,
              negative_prompt_2=negative_prompt_2,
              prompt_embeds=prompt_embeds,
              negative_prompt_embeds=negative_prompt_embeds,
              pooled_prompt_embeds=pooled_prompt_embeds,
              negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
              lora_scale=lora_scale,
              clip_skip=self.clip_skip,
          )
  
          # 4. Prepare timesteps
          timesteps, num_inference_steps = retrieve_timesteps(
              self.scheduler, num_inference_steps, device, timesteps
          )
  
          # 5. Prepare latent variables
          num_channels_latents = self.unet.config.in_channels
          latents = self.prepare_latents(
              batch_size * num_images_per_prompt,
              num_channels_latents,
              height,
              width,
              prompt_embeds.dtype,
              device,
              generator,
              latents,
          )
  
          # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
          extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
  
          # 7. Prepare added time ids & embeddings
          add_text_embeds = pooled_prompt_embeds
          if self.text_encoder_2 is None:
              text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
          else:
              text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
  
          add_time_ids = self._get_add_time_ids(
              original_size,
              crops_coords_top_left,
              target_size,
              dtype=prompt_embeds.dtype,
              text_encoder_projection_dim=text_encoder_projection_dim,
          )
          if negative_original_size is not None and negative_target_size is not None:
              negative_add_time_ids = self._get_add_time_ids(
                  negative_original_size,
                  negative_crops_coords_top_left,
                  negative_target_size,
                  dtype=prompt_embeds.dtype,
                  text_encoder_projection_dim=text_encoder_projection_dim,
              )
          else:
              negative_add_time_ids = add_time_ids
  
          if self.do_classifier_free_guidance:
              prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
              add_text_embeds = torch.cat(
                  [negative_pooled_prompt_embeds, add_text_embeds], dim=0
              )
              add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
  
          prompt_embeds = prompt_embeds.to(device)
          add_text_embeds = add_text_embeds.to(device)
          add_time_ids = add_time_ids.to(device).repeat(
              batch_size * num_images_per_prompt, 1
          )
  
          if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
              image_embeds = self.prepare_ip_adapter_image_embeds(
                  ip_adapter_image,
                  ip_adapter_image_embeds,
                  device,
                  batch_size * num_images_per_prompt,
                  self.do_classifier_free_guidance,
              )
  
          # Preprocess reference image
          reference_image = self.image_processor.preprocess(reference_image)
          reference_latents = self.prepare_image_latents(
              reference_image,
              timesteps[:1].repeat(batch_size * num_images_per_prompt),  # no use
              batch_size,
              1,
              prompt_embeds.dtype,
              device,
              generator,
              add_noise=False,
          )
  
          with torch.no_grad():
              ref_timesteps = torch.zeros_like(timesteps[0])
              ref_hidden_states = {}
  
              self.unet(
                  reference_latents,
                  ref_timesteps,
                  encoder_hidden_states=prompt_embeds[-1:],
                  added_cond_kwargs={
                      "text_embeds": add_text_embeds[-1:],
                      "time_ids": add_time_ids[-1:],
                  },
                  cross_attention_kwargs={
                      "cache_hidden_states": ref_hidden_states,
                      "use_mv": False,
                      "use_ref": False,
                  },
                  return_dict=False,
              )
              ref_hidden_states = {
                  k: v.repeat_interleave(num_images_per_prompt, dim=0)
                  for k, v in ref_hidden_states.items()
              }
          if self.do_classifier_free_guidance:
              ref_hidden_states = {
                  k: torch.cat([torch.zeros_like(v), v], dim=0)
                  for k, v in ref_hidden_states.items()
              }
  
          cross_attention_kwargs = {
              "mv_scale": mv_scale,
              "ref_hidden_states": {k: v.clone() for k, v in ref_hidden_states.items()},
              "ref_scale": reference_conditioning_scale,
              "num_views": num_images_per_prompt,
              **(self.cross_attention_kwargs or {}),
          }
  
          # Preprocess control image
          control_image_feature = self.prepare_control_image(
              image=control_image,
              width=width,
              height=height,
              batch_size=batch_size * num_images_per_prompt,
              num_images_per_prompt=1,  # NOTE: always 1 for control images
              device=device,
              dtype=latents.dtype,
              do_classifier_free_guidance=self.do_classifier_free_guidance,
          )
          control_image_feature = control_image_feature.to(
              device=device, dtype=latents.dtype
          )
  
          adapter_state = self.cond_encoder(control_image_feature)
          for i, state in enumerate(adapter_state):
              adapter_state[i] = state * control_conditioning_scale
  
          # 8. Denoising loop
          num_warmup_steps = max(
              len(timesteps) - num_inference_steps * self.scheduler.order, 0
          )
  
          # 8.1 Apply denoising_end
          if (
              self.denoising_end is not None
              and isinstance(self.denoising_end, float)
              and self.denoising_end > 0
  