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.
  
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  
  import torch
  from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
  from diffusers.models import AutoencoderKL, 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 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
  
  
  class MVAdapterT2MVSDXLPipeline(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,
          )
  
      def prepare_control_image(
          self,
          image,
          width,
          height,
          batch_size,
          num_images_per_prompt,
          device,
          dtype,
          do_classifier_free_guidance=False,
      ):
          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)
          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,
          # Optional. controlnet
          controlnet_image: Optional[PipelineImageInput] = None,
          controlnet_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 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
  
          # Preprocess controlnet image if provided
          do_controlnet = controlnet_image is not None and hasattr(self, "controlnet")
          if do_controlnet:
              controlnet_image = self.prepare_control_image(
                  image=controlnet_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,
              )
              controlnet_image = controlnet_image.to(device=device, dtype=latents.dtype)
  
          # 8. Denoising loop
          num_warmup_steps = max(
              len(timesteps) - num_inference_steps * self.scheduler.order, 0
          )
  
          # 8.1 Apply denoising_end
          if (
              self.denoising_end is not None
              and isinstance(self.denoising_end, float)
              and self.denoising_end > 0
              and self.denoising_end < 1
          ):
              discrete_timestep_cutoff = int(
                  round(
                      self.scheduler.config.num_train_timesteps
                      - (self.denoising_end * self.scheduler.config.num_train_timesteps)
                  )
              )
              num_inference_steps = len(
                  list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))
              )
              timesteps = timesteps[:num_inference_steps]
  
          # 9. Optionally get Guidance Scale Embedding
          timestep_cond = None
          if self.unet.config.time_cond_proj_dim is not None:
              guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(
                  batch_size * num_images_per_prompt
              )
              timestep_cond = self.get_guidance_scale_embedding(
                  guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
              ).to(device=device, dtype=latents.dtype)
  
          self._num_timesteps = len(timesteps)
          with self.progress_bar(total=num_inference_steps) as progress_bar:
              for i, t in enumerate(timesteps):
                  if self.interrupt:
                      continue
  
                  # expand the latents if we are doing classifier free guidance
                  latent_model_input = (
                      torch.cat([latents] * 2)
                      if self.do_classifier_free_guidance
                      else latents
                  )
  
                  latent_model_input = self.scheduler.scale_model_input(
                      latent_model_input, t
                  )
  
                  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
  
                  if i < int(num_inference_steps * control_conditioning_factor):
                      down_intrablock_additional_residuals = [
                          state.clone() for state in adapter_state
                      ]
                  else:
                      down_intrablock_additional_residuals = None
  
                  unet_add_kwargs = {}
  
                  # Do controlnet if provided
                  if do_controlnet:
                      down_block_res_samples, mid_block_res_sample = self.controlnet(
                          latent_model_input,
                          t,
                          encoder_hidden_states=prompt_embeds,
                          controlnet_cond=controlnet_image,
                          conditioning_scale=controlnet_conditioning_scale,
                          guess_mode=False,
                          added_cond_kwargs=added_cond_kwargs,
                          return_dict=False,
                      )
                      unet_add_kwargs.update(
                          {
                              "down_block_additional_residuals": down_block_res_samples,
                              "mid_block_additional_residual": mid_block_res_sample,
                          }
                      )
  
                  # predict the noise residual
                  noise_pred = self.unet(
                      latent_model_input,
                      t,
                      encoder_hidden_states=prompt_embeds,
                      timestep_cond=timestep_cond,
                      cross_attention_kwargs={
                          "mv_scale": mv_scale,
                          "num_views": num_images_per_prompt,
                          **(self.cross_attention_kwargs or {}),
                      },
                      down_intrablock_additional_residuals=down_intrablock_additional_residuals,
                      added_cond_kwargs=added_cond_kwargs,
                      return_dict=False,
                      **unet_add_kwargs,
                  )[0]
  
                  # perform guidance
                  if self.do_classifier_free_guidance:
                      noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                      noise_pred = noise_pred_uncond + self.guidance_scale * (
                          noise_pred_text - noise_pred_uncond
                      )
  
                  if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
                      # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                      noise_pred = rescale_noise_cfg(
                          noise_pred,
                          noise_pred_text,
                          guidance_rescale=self.guidance_rescale,
                      )
  
                  # compute the previous noisy sample x_t -> x_t-1
                  latents_dtype = latents.dtype
                  latents = self.scheduler.step(
                      noise_pred, t, latents, **extra_step_kwargs, return_dict=False
                  )[0]
                  if latents.dtype != latents_dtype:
                      if torch.backends.mps.is_available():
                          # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
                          latents = latents.to(latents_dtype)
  
                  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)
  