content: import inspect
  import warnings
  from typing import Callable, List, Optional, Union, Dict, Any
  import PIL
  import torch
  from packaging import version
  from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, CLIPFeatureExtractor, CLIPTokenizer, CLIPTextModel
  from diffusers.utils.import_utils import is_accelerate_available
  from diffusers.configuration_utils import FrozenDict
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.models.embeddings import get_timestep_embedding
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import deprecate, logging
  from diffusers.utils.torch_utils import randn_tensor
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
  import os
  import torchvision.transforms.functional as TF
  from einops import rearrange
  import comfy.utils
  logger = logging.get_logger(__name__)
  
  class StableUnCLIPImg2ImgPipeline(DiffusionPipeline):
      """
      Pipeline for text-guided image to image generation using stable unCLIP.
  
      This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
      library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
  
      Args:
          feature_extractor ([`CLIPFeatureExtractor`]):
              Feature extractor for image pre-processing before being encoded.
          image_encoder ([`CLIPVisionModelWithProjection`]):
              CLIP vision model for encoding images.
          image_normalizer ([`StableUnCLIPImageNormalizer`]):
              Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image
              embeddings after the noise has been applied.
          image_noising_scheduler ([`KarrasDiffusionSchedulers`]):
              Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined
              by `noise_level` in `StableUnCLIPPipeline.__call__`.
          tokenizer (`CLIPTokenizer`):
              Tokenizer of class
              [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
          text_encoder ([`CLIPTextModel`]):
              Frozen text-encoder.
          unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
          scheduler ([`KarrasDiffusionSchedulers`]):
              A scheduler to be used in combination with `unet` to denoise the encoded image latents.
          vae ([`AutoencoderKL`]):
              Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
      """
      # image encoding components
      feature_extractor: CLIPFeatureExtractor
      image_encoder: CLIPVisionModelWithProjection
      # image noising components
      image_normalizer: StableUnCLIPImageNormalizer
      image_noising_scheduler: KarrasDiffusionSchedulers
      # regular denoising components
      tokenizer: CLIPTokenizer
      text_encoder: CLIPTextModel
      unet: UNet2DConditionModel
      scheduler: KarrasDiffusionSchedulers
      vae: AutoencoderKL
  
      def __init__(
          self,
          # image encoding components
          feature_extractor: CLIPFeatureExtractor,
          image_encoder: CLIPVisionModelWithProjection,
          # image noising components
          image_normalizer: StableUnCLIPImageNormalizer,
          image_noising_scheduler: KarrasDiffusionSchedulers,
          # regular denoising components
          tokenizer: CLIPTokenizer,
          text_encoder: CLIPTextModel,
          unet: UNet2DConditionModel,
          scheduler: KarrasDiffusionSchedulers,
          # vae
          vae: AutoencoderKL,
          num_views: int = 4,
      ):
          super().__init__()
  
          self.register_modules(
              feature_extractor=feature_extractor,
              image_encoder=image_encoder,
              image_normalizer=image_normalizer,
              image_noising_scheduler=image_noising_scheduler,
              tokenizer=tokenizer,
              text_encoder=text_encoder,
              unet=unet,
              scheduler=scheduler,
              vae=vae,
          )
          self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
          self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
          self.num_views: int = num_views
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
      def enable_vae_slicing(self):
          r"""
          Enable sliced VAE decoding.
  
          When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
          steps. This is useful to save some memory and allow larger batch sizes.
          """
          self.vae.enable_slicing()
  
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
      def disable_vae_slicing(self):
          r"""
          Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
          computing decoding in one step.
          """
          self.vae.disable_slicing()
  
      def enable_sequential_cpu_offload(self, gpu_id=0):
          r"""
          Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
          models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
          when their specific submodule has its `forward` method called.
          """
          if is_accelerate_available():
              from accelerate import cpu_offload
          else:
              raise ImportError("Please install accelerate via `pip install accelerate`")
  
          device = torch.device(f"cuda:{gpu_id}")
  
          # TODO: self.image_normalizer.{scale,unscale} are not covered by the offload hooks, so they fails if added to the list
          models = [
              self.image_encoder,
              self.text_encoder,
              self.unet,
              self.vae,
          ]
          for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                  cpu_offload(cpu_offloaded_model, device)
  
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def _execution_device(self):
          r"""
          Returns the device on which the pipeline's models will be executed. After calling
          `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
          hooks.
          """
          if not hasattr(self.unet, "_hf_hook"):
              return self.device
          for module in self.unet.modules():
              if (
                  hasattr(module, "_hf_hook")
                  and hasattr(module._hf_hook, "execution_device")
                  and module._hf_hook.execution_device is not None
              ):
                  return torch.device(module._hf_hook.execution_device)
          return self.device
  
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
      def _encode_prompt(
          self,
          prompt,
          device,
          num_images_per_prompt,
          do_classifier_free_guidance,
          negative_prompt=None,
          prompt_embeds: Optional[torch.FloatTensor] = None,
          negative_prompt_embeds: Optional[torch.FloatTensor] = None,
          lora_scale: Optional[float] = None,
      ):
          r"""
          Encodes the prompt into text encoder hidden states.
  
          Args:
               prompt (`str` or `List[str]`, *optional*):
                  prompt to be encoded
              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. 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`).
              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.
          """
          prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
  
          if do_classifier_free_guidance:
              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              normal_prompt_embeds, color_prompt_embeds = torch.chunk(prompt_embeds, 2, dim=0)
              
              prompt_embeds = torch.cat([normal_prompt_embeds, normal_prompt_embeds, color_prompt_embeds, color_prompt_embeds], 0)
  
          return prompt_embeds
  
      def _encode_image(
          self,
          image_pil,
          device,
          num_images_per_prompt,
          do_classifier_free_guidance,
          noise_level: int=0,
          generator: Optional[torch.Generator] = None
      ):
          dtype = next(self.image_encoder.parameters()).dtype
          # ______________________________clip image embedding______________________________ 
          image = self.feature_extractor(images=image_pil, return_tensors="pt").pixel_values
          image = image.to(device=device, dtype=dtype)
          image_embeds = self.image_encoder(image).image_embeds
          
          image_embeds = self.noise_image_embeddings(
              image_embeds=image_embeds,
              noise_level=noise_level,
              generator=generator,
              )
          # duplicate image embeddings for each generation per prompt, using mps friendly method
          # image_embeds = image_embeds.unsqueeze(1)
          # note: the condition input is same
          image_embeds = image_embeds.repeat(num_images_per_prompt, 1)
  
          if do_classifier_free_guidance:
              normal_image_embeds, color_image_embeds = torch.chunk(image_embeds, 2, dim=0)
              negative_prompt_embeds = torch.zeros_like(normal_image_embeds)
  
              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              image_embeds = torch.cat([negative_prompt_embeds, normal_image_embeds, negative_prompt_embeds, color_image_embeds], 0)
              
          # _____________________________vae input latents__________________________________________________
          image_pt = torch.stack([TF.to_tensor(img) for img in image_pil], dim=0).to(device)
          image_pt = image_pt * 2.0 - 1.0
          image_latents = self.vae.encode(image_pt).latent_dist.mode() * self.vae.config.scaling_factor
          # Note: repeat differently from official pipelines     
          image_latents = image_latents.repeat(num_images_per_prompt, 1, 1, 1)
  
          if do_classifier_free_guidance:
              normal_image_latents, color_image_latents = torch.chunk(image_latents, 2, dim=0)
              image_latents = torch.cat([torch.zeros_like(normal_image_latents), normal_image_latents, 
                                         torch.zeros_like(color_image_latents), color_image_latents], 0)
  
          return image_embeds, image_latents
  
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
      def decode_latents(self, latents):
          latents = 1 / self.vae.config.scaling_factor * latents
          image = self.vae.decode(latents).sample
          image = (image / 2 + 0.5).clamp(0, 1)
          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          image = image.cpu().permute(0, 2, 3, 1).float().numpy()
          return image
  
      # 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,
          image,
          height,
          width,
          callback_steps,
          noise_level,
      ):
          if height % 8 != 0 or width % 8 != 0:
              raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
  
          if (callback_steps is None) or (
              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 prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
              raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
  
  
          if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps:
              raise ValueError(
                  f"`noise_level` must be between 0 and {self.image_noising_scheduler.config.num_train_timesteps - 1}, inclusive."
              )
  
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
      def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
          shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
          if isinstance(generator, list) and len(generator) != batch_size:
              raise ValueError(
                  f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                  f" size of {batch_size}. Make sure the batch size matches the length of the generators."
              )
  
          if latents is None:
              latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
          else:
              latents = latents.to(device)
  
          # scale the initial noise by the standard deviation required by the scheduler
          latents = latents * self.scheduler.init_noise_sigma
          return latents
  
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_unclip.StableUnCLIPPipeline.noise_image_embeddings
      def noise_image_embeddings(
          self,
          image_embeds: torch.Tensor,
          noise_level: int,
          noise: Optional[torch.FloatTensor] = None,
          generator: Optional[torch.Generator] = None,
      ):
          """
          Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher
          `noise_level` increases the variance in the final un-noised images.
  
          The noise is applied in two ways
          1. A noise schedule is applied directly to the embeddings
          2. A vector of sinusoidal time embeddings are appended to the output.
  
          In both cases, the amount of noise is controlled by the same `noise_level`.
  
          The embeddings are normalized before the noise is applied and un-normalized after the noise is applied.
          """
          if noise is None:
              noise = randn_tensor(
                  image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype
              )
  
          noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device)
  
          image_embeds = self.image_normalizer.scale(image_embeds)
  
          image_embeds = self.image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise)
  
          image_embeds = self.image_normalizer.unscale(image_embeds)
  
          noise_level = get_timestep_embedding(
              timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0
          )
  
          # `get_timestep_embeddings` does not contain any weights and will always return f32 tensors,
          # but we might actually be running in fp16. so we need to cast here.
          # there might be better ways to encapsulate this.
          noise_level = noise_level.to(image_embeds.dtype)
  
          image_embeds = torch.cat((image_embeds, noise_level), 1)
  
          return image_embeds
  
      @torch.no_grad()
      # @replace_example_docstring(EXAMPLE_DOC_STRING)
      def __call__(
          self,
          image: Union[torch.FloatTensor, PIL.Image.Image],
          prompt: Union[str, List[str]],   
          prompt_embeds: torch.FloatTensor = None,
          dino_feature: torch.FloatTensor = None,
          height: Optional[int] = None,
          width: Optional[int] = None,
          num_inference_steps: int = 20,
          guidance_scale: float = 10,
          negative_prompt: Optional[Union[str, List[str]]] = None,
          num_images_per_prompt: Optional[int] = 1,
          eta: float = 0.0,
          generator: Optional[torch.Generator] = None,
          latents: Optional[torch.FloatTensor] = None,
          negative_prompt_embeds: Optional[torch.FloatTensor] = None,
          output_type: Optional[str] = "pil",
          return_dict: bool = True,
          callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
          callback_steps: int = 1,
          cross_attention_kwargs: Optional[Dict[str, Any]] = None,
          noise_level: int = 0,
          image_embeds: Optional[torch.FloatTensor] = None,
          return_elevation_focal: Optional[bool] = False,
          gt_img_in: Optional[torch.FloatTensor] = None,
      ):
          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.
              image (`torch.FloatTensor` or `PIL.Image.Image`):
                  `Image`, or tensor representing an image batch. The image will be encoded to its CLIP embedding which
                  the unet will be conditioned on. Note that the image is _not_ encoded by the vae and then used as the
                  latents in the denoising process such as in the standard stable diffusion text guided image variation
                  process.
              height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                  The height in pixels of the generated image.
              width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                  The width in pixels of the generated image.
              num_inference_steps (`int`, *optional*, defaults to 20):
                  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                  expense of slower inference.
              guidance_scale (`float`, *optional*, defaults to 10.0):
                  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. If not defined, one has to pass `negative_prompt_embeds`. instead.
                  Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
              num_images_per_prompt (`int`, *optional*, defaults to 1):
                  The number of images to generate per prompt.
              eta (`float`, *optional*, defaults to 0.0):
                  Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                  [`schedulers.DDIMScheduler`], will be ignored for others.
              generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                  One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                  to make generation deterministic.
              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.
              output_type (`str`, *optional*, defaults to `"pil"`):
                  The output format of the generate image. Choose between
                  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
              return_dict (`bool`, *optional*, defaults to `True`):
                  Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                  plain tuple.
              callback (`Callable`, *optional*):
                  A function that will be called every `callback_steps` steps during inference. The function will be
                  called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
              callback_steps (`int`, *optional*, defaults to 1):
                  The frequency at which the `callback` function will be called. If not specified, the callback will be
                  called at every step.
              cross_attention_kwargs (`dict`, *optional*):
                  A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under
                  `self.processor` in
                  [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
              noise_level (`int`, *optional*, defaults to `0`):
                  The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in
                  the final un-noised images. See `StableUnCLIPPipeline.noise_image_embeddings` for details.
              image_embeds (`torch.FloatTensor`, *optional*):
                  Pre-generated CLIP embeddings to condition the unet on. Note that these are not latents to be used in
                  the denoising process. If you want to provide pre-generated latents, pass them to `__call__` as
                  `latents`.
  
          Examples:
  
          Returns:
              [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is
              True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images.
          """
          # 0. Default height and width to unet
          height = height or self.unet.config.sample_size * self.vae_scale_factor
          width = width or self.unet.config.sample_size * self.vae_scale_factor
  
          # 1. Check inputs. Raise error if not correct
          self.check_inputs(
              prompt=prompt,
              image=image,
              height=height,
              width=width,
              callback_steps=callback_steps,
              noise_level=noise_level
          )
  
          # 2. Define call parameters
          if isinstance(image, list):
              batch_size = len(image)
          elif isinstance(image, torch.Tensor):
              batch_size = image.shape[0]
              assert batch_size >= self.num_views and batch_size % self.num_views == 0
          elif isinstance(image, PIL.Image.Image):
              image = [image]*self.num_views*2
              batch_size = self.num_views*2
  
          if isinstance(prompt, str):
              prompt = [prompt] * self.num_views * 2
  
          device = self._execution_device
  
          # 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.
          do_classifier_free_guidance = guidance_scale != 1.0
  
          # 3. Encode input prompt
          text_encoder_lora_scale = (
              cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
          )
          prompt_embeds = self._encode_prompt(
              prompt=prompt,
              device=device,
              num_images_per_prompt=num_images_per_prompt,
              do_classifier_free_guidance=do_classifier_free_guidance,
              negative_prompt=negative_prompt,
              prompt_embeds=prompt_embeds,
              negative_prompt_embeds=negative_prompt_embeds,
              lora_scale=text_encoder_lora_scale,
          )
          
  
          # 4. Encoder input image
          if isinstance(image, list):
              image_pil = image
          elif isinstance(image, torch.Tensor):
              image_pil = [TF.to_pil_image(image[i]) for i in range(image.shape[0])]
          noise_level = torch.tensor([noise_level], device=device)
          image_embeds, image_latents = self._encode_image(
              image_pil=image_pil,
              device=device,
              num_images_per_prompt=num_images_per_prompt,
              do_classifier_free_guidance=do_classifier_free_guidance,
              noise_level=noise_level,
              generator=generator,
          )
  
          # 5. Prepare timesteps
          self.scheduler.set_timesteps(num_inference_steps, device=device)
          timesteps = self.scheduler.timesteps
  
          # 6. Prepare latent variables
          num_channels_latents = self.unet.config.out_channels
          if gt_img_in is not None:
              latents = gt_img_in * self.scheduler.init_noise_sigma
          else:
              latents = self.prepare_latents(
                  batch_size=batch_size,
                  num_channels_latents=num_channels_latents,
                  height=height,
                  width=width,
                  dtype=prompt_embeds.dtype,
                  device=device,
                  generator=generator,
                  latents=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)
  
          eles, focals = [], []
          # 8. Denoising loop
          comfy_pbar = comfy.utils.ProgressBar(num_inference_steps)
          for i, t in enumerate(self.progress_bar(timesteps)):
              if do_classifier_free_guidance:
                  normal_latents, color_latents = torch.chunk(latents, 2, dim=0)  
                  latent_model_input = torch.cat([normal_latents, normal_latents, color_latents, color_latents], 0)
              else:
                  latent_model_input = latents
              latent_model_input = torch.cat([
                      latent_model_input, image_latents
                  ], dim=1)
              latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
  
              # predict the noise residual
              unet_out = self.unet(
                  latent_model_input,
                  t,
                  encoder_hidden_states=prompt_embeds,
                  dino_feature=dino_feature,
                  class_labels=image_embeds,
                  cross_attention_kwargs=cross_attention_kwargs,
                  return_dict=False)
              
              noise_pred = unet_out[0]
              if return_elevation_focal:    
                  uncond_pose, pose  = torch.chunk(unet_out[1], 2, 0) 
                  pose = uncond_pose + guidance_scale * (pose - uncond_pose)
                  ele = pose[:, 0].detach().cpu().numpy() # b
                  eles.append(ele)
                  focal = pose[:, 1].detach().cpu().numpy()
                  focals.append(focal)
                  
              # perform guidance
              if do_classifier_free_guidance:
                  normal_noise_pred_uncond, normal_noise_pred_text, color_noise_pred_uncond, color_noise_pred_text = torch.chunk(noise_pred, 4, dim=0)
                  
                  noise_pred_uncond, noise_pred_text = torch.cat([normal_noise_pred_uncond, color_noise_pred_uncond], 0), torch.cat([normal_noise_pred_text, color_noise_pred_text], 0)
                  noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
              # compute the previous noisy sample x_t -> x_t-1
              latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
  
              if callback is not None and i % callback_steps == 0:
                  callback(i, t, latents)
  
              comfy_pbar.update_absolute(i + 1)
  
          # 9. Post-processing
          if not output_type == "latent":
              if num_channels_latents == 8:
                  latents = torch.cat([latents[:, :4], latents[:, 4:]], dim=0)
              with torch.no_grad():
                  image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
          else:
              image = latents
  
          image = self.image_processor.postprocess(image, output_type=output_type)
  
          # Offload last model to CPU
          # if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
          #     self.final_offload_hook.offload()
          if not return_dict:
              return (image, )
          if return_elevation_focal:
              return ImagePipelineOutput(images=image),  eles, focals
          else:
              return ImagePipelineOutput(images=image)
  