class_docstr: Pipeline for text-guided image to image generation using Stable Diffusion.
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
  implemented for all pipelines (downloading, saving, running on a particular device, etc.).
  
  The pipeline also inherits the following loading methods:
      - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
      - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
      - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
      text_encoder ([`~transformers.CLIPTextModel`]):
          Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      tokenizer ([`~transformers.CLIPTokenizer`]):
          A `CLIPTokenizer` to tokenize text.
      unet ([`UNet2DConditionModel`]):
          A `UNet2DConditionModel` to denoise the encoded image latents.
      scheduler ([`SchedulerMixin`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can only be an
          instance of [`DDIMScheduler`].
      safety_checker ([`StableDiffusionSafetyChecker`]):
          Classification module that estimates whether generated images could be considered offensive or harmful.
          Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
          about a model's potential harms.
      feature_extractor ([`~transformers.CLIPImageProcessor`]):
          A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
class_docstr_tok: ["Pipeline","for","text-guided","image","to","image","generation","using","Stable","Diffusion",".","This","model","inherits","from","[","`","DiffusionPipeline","`","]",".","Check","the","superclass","documentation","for","the","generic","methods","implemented","for","all","pipelines","(","downloading",",","saving",",","running","on","a","particular","device",",","etc.",")",".","The","pipeline","also","inherits","the","following","loading","methods",":","-","[","`","~loaders.TextualInversionLoaderMixin.load_textual_inversion","`","]","for","loading","textual","inversion","embeddings","-","[","`","~loaders.LoraLoaderMixin.load_lora_weights","`","]","for","loading","LoRA","weights","-","[","`","~loaders.LoraLoaderMixin.save_lora_weights","`","]","for","saving","LoRA","weights","Args",":","vae","(","[","`","AutoencoderKL","`","]",")",":","Variational","Auto-Encoder","(","VAE",")","model","to","encode","and","decode","images","to","and","from","latent","representations",".","text_encoder","(","[","`","~transformers.CLIPTextModel","`","]",")",":","Frozen","text-encoder","(","[","clip-vit-large-patch14","]","(","https",":","\/\/huggingface.co\/openai\/clip-vit-large-patch14",")",")",".","tokenizer","(","[","`","~transformers.CLIPTokenizer","`","]",")",":","A","`","CLIPTokenizer","`","to","tokenize","text",".","unet","(","[","`","UNet2DConditionModel","`","]",")",":","A","`","UNet2DConditionModel","`","to","denoise","the","encoded","image","latents",".","scheduler","(","[","`","SchedulerMixin","`","]",")",":","A","scheduler","to","be","used","in","combination","with","`","unet","`","to","denoise","the","encoded","image","latents",".","Can","only","be","an","instance","of","[","`","DDIMScheduler","`","]",".","safety_checker","(","[","`","StableDiffusionSafetyChecker","`","]",")",":","Classification","module","that","estimates","whether","generated","images","could","be","considered","offensive","or","harmful",".","Please","refer","to","the","[","model","card","]","(","https",":","\/\/huggingface.co\/runwayml\/stable-diffusion-v1-5",")","for","more","details","about","a","model","'s","potential","harms",".","feature_extractor","(","[","`","~transformers.CLIPImageProcessor","`","]",")",":","A","`","CLIPImageProcessor","`","to","extract","features","from","generated","images",";","used","as","inputs","to","the","`","safety_checker","`","."]
docstr: The call function to the pipeline for generation.
  
  Args:
      prompt (`str` or `List[str]`):
          The prompt or prompts to guide the image generation.
      image (`torch.FloatTensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
          `Image` or tensor representing an image batch to be used as the starting point. Can also accept image
          latents as `image`, but if passing latents directly it is not encoded again.
      strength (`float`, *optional*, defaults to 0.8):
          Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
          starting point and more noise is added the higher the `strength`. The number of denoising steps depends
          on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
          process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
          essentially ignores `image`.
      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. This parameter is modulated by `strength`.
      guidance_scale (`float`, *optional*, defaults to 7.5):
          A higher guidance scale value encourages the model to generate images closely linked to the text
          `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
      source_guidance_scale (`float`, *optional*, defaults to 1):
          Guidance scale for the source prompt. This is useful to control the amount of influence the source
          prompt has for encoding.
      num_images_per_prompt (`int`, *optional*, defaults to 1):
          The number of images to generate per prompt.
      eta (`float`, *optional*, defaults to 0.0):
          Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
          to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
      generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
          A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
          generation deterministic.
      prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
          provided, text embeddings are generated from the `prompt` input argument.
      negative_prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
          not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
      output_type (`str`, *optional*, defaults to `"pil"`):
          The output format of the generated image. Choose between `PIL.Image` or `np.array`.
      return_dict (`bool`, *optional*, defaults to `True`):
          Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
          plain tuple.
      callback (`Callable`, *optional*):
          A function that calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at
          every step.
      cross_attention_kwargs (`dict`, *optional*):
          A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
          [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
      clip_skip (`int`, *optional*):
          Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
          the output of the pre-final layer will be used for computing the prompt embeddings.
  Example:
  
  ```py
  import requests
  import torch
  from PIL import Image
  from io import BytesIO
  
  from diffusers import CycleDiffusionPipeline, DDIMScheduler
  
  # load the pipeline
  # make sure you're logged in with `huggingface-cli login`
  model_id_or_path = "CompVis/stable-diffusion-v1-4"
  scheduler = DDIMScheduler.from_pretrained(model_id_or_path, subfolder="scheduler")
  pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler).to("cuda")
  
  # let's download an initial image
  url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/An%20astronaut%20riding%20a%20horse.png"
  response = requests.get(url)
  init_image = Image.open(BytesIO(response.content)).convert("RGB")
  init_image = init_image.resize((512, 512))
  init_image.save("horse.png")
  
  # let's specify a prompt
  source_prompt = "An astronaut riding a horse"
  prompt = "An astronaut riding an elephant"
  
  # call the pipeline
  image = pipe(
      prompt=prompt,
      source_prompt=source_prompt,
      image=init_image,
      num_inference_steps=100,
      eta=0.1,
      strength=0.8,
      guidance_scale=2,
      source_guidance_scale=1,
  ).images[0]
  
  image.save("horse_to_elephant.png")
  
  # let's try another example
  # See more samples at the original repo: https://github.com/ChenWu98/cycle-diffusion
  url = (
      "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/A%20black%20colored%20car.png"
  )
  response = requests.get(url)
  init_image = Image.open(BytesIO(response.content)).convert("RGB")
  init_image = init_image.resize((512, 512))
  init_image.save("black.png")
  
  source_prompt = "A black colored car"
  prompt = "A blue colored car"
  
  # call the pipeline
  torch.manual_seed(0)
  image = pipe(
      prompt=prompt,
      source_prompt=source_prompt,
      image=init_image,
      num_inference_steps=100,
      eta=0.1,
      strength=0.85,
      guidance_scale=3,
      source_guidance_scale=1,
  ).images[0]
  
  image.save("black_to_blue.png")
  ```
  
  Returns:
      [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
          If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
          otherwise a `tuple` is returned where the first element is a list with the generated images and the
          second element is a list of `bool`s indicating whether the corresponding generated image contains
          "not-safe-for-work" (nsfw) content.
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code: def __call__(
      self,
      prompt: Union[str, List[str]],
      source_prompt: Union[str, List[str]],
      image: PipelineImageInput = None,
      strength: float = 0.8,
      num_inference_steps: Optional[int] = 50,
      guidance_scale: Optional[float] = 7.5,
      source_guidance_scale: Optional[float] = 1,
      num_images_per_prompt: Optional[int] = 1,
      eta: Optional[float] = 0.1,
      generator: Optional[
          Union[torch.Generator, List[torch.Generator]]
      ] = None,
      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,
      clip_skip: Optional[int] = None,
  ):
      r"""
      The call function to the pipeline for generation.
  
      Args:
          prompt (`str` or `List[str]`):
              The prompt or prompts to guide the image generation.
          image (`torch.FloatTensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
              `Image` or tensor representing an image batch to be used as the starting point. Can also accept image
              latents as `image`, but if passing latents directly it is not encoded again.
          strength (`float`, *optional*, defaults to 0.8):
              Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
              starting point and more noise is added the higher the `strength`. The number of denoising steps depends
              on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
              process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
              essentially ignores `image`.
          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. This parameter is modulated by `strength`.
          guidance_scale (`float`, *optional*, defaults to 7.5):
              A higher guidance scale value encourages the model to generate images closely linked to the text
              `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
          source_guidance_scale (`float`, *optional*, defaults to 1):
              Guidance scale for the source prompt. This is useful to control the amount of influence the source
              prompt has for encoding.
          num_images_per_prompt (`int`, *optional*, defaults to 1):
              The number of images to generate per prompt.
          eta (`float`, *optional*, defaults to 0.0):
              Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
              to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
          generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
              A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
              generation deterministic.
          prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
              provided, text embeddings are generated from the `prompt` input argument.
          negative_prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
              not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
          output_type (`str`, *optional*, defaults to `"pil"`):
              The output format of the generated image. Choose between `PIL.Image` or `np.array`.
          return_dict (`bool`, *optional*, defaults to `True`):
              Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
              plain tuple.
          callback (`Callable`, *optional*):
              A function that calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at
              every step.
          cross_attention_kwargs (`dict`, *optional*):
              A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
              [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
          clip_skip (`int`, *optional*):
              Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
              the output of the pre-final layer will be used for computing the prompt embeddings.
      Example:
  
      ```py
      import requests
      import torch
      from PIL import Image
      from io import BytesIO
  
      from diffusers import CycleDiffusionPipeline, DDIMScheduler
  
      # load the pipeline
      # make sure you're logged in with `huggingface-cli login`
      model_id_or_path = "CompVis/stable-diffusion-v1-4"
      scheduler = DDIMScheduler.from_pretrained(model_id_or_path, subfolder="scheduler")
      pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler).to("cuda")
  
      # let's download an initial image
      url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/An%20astronaut%20riding%20a%20horse.png"
      response = requests.get(url)
      init_image = Image.open(BytesIO(response.content)).convert("RGB")
      init_image = init_image.resize((512, 512))
      init_image.save("horse.png")
  
      # let's specify a prompt
      source_prompt = "An astronaut riding a horse"
      prompt = "An astronaut riding an elephant"
  
      # call the pipeline
      image = pipe(
          prompt=prompt,
          source_prompt=source_prompt,
          image=init_image,
          num_inference_steps=100,
          eta=0.1,
          strength=0.8,
          guidance_scale=2,
          source_guidance_scale=1,
      ).images[0]
  
      image.save("horse_to_elephant.png")
  
      # let's try another example
      # See more samples at the original repo: https://github.com/ChenWu98/cycle-diffusion
      url = (
          "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/A%20black%20colored%20car.png"
      )
      response = requests.get(url)
      init_image = Image.open(BytesIO(response.content)).convert("RGB")
      init_image = init_image.resize((512, 512))
      init_image.save("black.png")
  
      source_prompt = "A black colored car"
      prompt = "A blue colored car"
  
      # call the pipeline
      torch.manual_seed(0)
      image = pipe(
          prompt=prompt,
          source_prompt=source_prompt,
          image=init_image,
          num_inference_steps=100,
          eta=0.1,
          strength=0.85,
          guidance_scale=3,
          source_guidance_scale=1,
      ).images[0]
  
      image.save("black_to_blue.png")
      ```
  
      Returns:
          [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
              If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
              otherwise a `tuple` is returned where the first element is a list with the generated images and the
              second element is a list of `bool`s indicating whether the corresponding generated image contains
              "not-safe-for-work" (nsfw) content.
      """
      # 1. Check inputs
      self.check_inputs(prompt, strength, callback_steps)
  
      # 2. Define call parameters
      batch_size = 1 if isinstance(prompt, str) else len(prompt)
      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_tuple = self.encode_prompt(
          prompt,
          device,
          num_images_per_prompt,
          do_classifier_free_guidance,
          prompt_embeds=prompt_embeds,
          lora_scale=text_encoder_lora_scale,
          clip_skip=clip_skip,
      )
      source_prompt_embeds_tuple = self.encode_prompt(
          source_prompt,
          device,
          num_images_per_prompt,
          do_classifier_free_guidance,
          None,
          clip_skip=clip_skip,
      )
      if prompt_embeds_tuple[1] is not None:
          prompt_embeds = torch.cat(
              [prompt_embeds_tuple[1], prompt_embeds_tuple[0]]
          )
      else:
          prompt_embeds = prompt_embeds_tuple[0]
      if source_prompt_embeds_tuple[1] is not None:
          source_prompt_embeds = torch.cat(
              [
                  source_prompt_embeds_tuple[1],
                  source_prompt_embeds_tuple[0],
              ]
          )
      else:
          source_prompt_embeds = source_prompt_embeds_tuple[0]
  
      # 4. Preprocess image
      image = self.image_processor.preprocess(image)
  
      # 5. Prepare timesteps
      self.scheduler.set_timesteps(num_inference_steps, device=device)
      timesteps, num_inference_steps = self.get_timesteps(
          num_inference_steps, strength, device
      )
      latent_timestep = timesteps[:1].repeat(
          batch_size * num_images_per_prompt
      )
  
      # 6. Prepare latent variables
      latents, clean_latents = self.prepare_latents(
          image,
          latent_timestep,
          batch_size,
          num_images_per_prompt,
          prompt_embeds.dtype,
          device,
          generator,
      )
      source_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)
      generator = extra_step_kwargs.pop("generator", None)
  
      # 8. Denoising loop
      num_warmup_steps = (
          len(timesteps) - num_inference_steps * self.scheduler.order
      )
      with self.progress_bar(total=num_inference_steps) as progress_bar:
          for i, t in enumerate(timesteps):
              # expand the latents if we are doing classifier free guidance
              latent_model_input = (
                  torch.cat([latents] * 2)
                  if do_classifier_free_guidance
                  else latents
              )
              source_latent_model_input = (
                  torch.cat([source_latents] * 2)
                  if do_classifier_free_guidance
                  else source_latents
              )
              latent_model_input = self.scheduler.scale_model_input(
                  latent_model_input, t
              )
              source_latent_model_input = (
                  self.scheduler.scale_model_input(
                      source_latent_model_input, t
                  )
              )
  
              # predict the noise residual
              if do_classifier_free_guidance:
                  concat_latent_model_input = torch.stack(
                      [
                          source_latent_model_input[0],
                          latent_model_input[0],
                          source_latent_model_input[1],
                          latent_model_input[1],
                      ],
                      dim=0,
                  )
                  concat_prompt_embeds = torch.stack(
                      [
                          source_prompt_embeds[0],
                          prompt_embeds[0],
                          source_prompt_embeds[1],
                          prompt_embeds[1],
                      ],
                      dim=0,
                  )
              else:
                  concat_latent_model_input = torch.cat(
                      [
                          source_latent_model_input,
                          latent_model_input,
                      ],
                      dim=0,
                  )
                  concat_prompt_embeds = torch.cat(
                      [
                          source_prompt_embeds,
                          prompt_embeds,
                      ],
                      dim=0,
                  )
  
              concat_noise_pred = self.unet(
                  concat_latent_model_input,
                  t,
                  cross_attention_kwargs=cross_attention_kwargs,
                  encoder_hidden_states=concat_prompt_embeds,
              ).sample
  
              # perform guidance
              if do_classifier_free_guidance:
                  (
                      source_noise_pred_uncond,
                      noise_pred_uncond,
                      source_noise_pred_text,
                      noise_pred_text,
                  ) = concat_noise_pred.chunk(4, dim=0)
  
                  noise_pred = noise_pred_uncond + guidance_scale * (
                      noise_pred_text - noise_pred_uncond
                  )
                  source_noise_pred = (
                      source_noise_pred_uncond
                      + source_guidance_scale
                      * (
                          source_noise_pred_text
                          - source_noise_pred_uncond
                      )
                  )
  
              else:
                  (
                      source_noise_pred,
                      noise_pred,
                  ) = concat_noise_pred.chunk(2, dim=0)
  
              # Sample source_latents from the posterior distribution.
              prev_source_latents = posterior_sample(
                  self.scheduler,
                  source_latents,
                  t,
                  clean_latents,
                  generator=generator,
                  **extra_step_kwargs,
              )
              # Compute noise.
              noise = compute_noise(
                  self.scheduler,
                  prev_source_latents,
                  source_latents,
                  t,
                  source_noise_pred,
                  **extra_step_kwargs,
              )
              source_latents = prev_source_latents
  
              # compute the previous noisy sample x_t -> x_t-1
              latents = self.scheduler.step(
                  noise_pred,
                  t,
                  latents,
                  variance_noise=noise,
                  **extra_step_kwargs,
              ).prev_sample
  
              # call the callback, if provided
              if i == len(timesteps) - 1 or (
                  (i + 1) > num_warmup_steps
                  and (i + 1) % self.scheduler.order == 0
              ):
                  progress_bar.update()
                  if callback is not None and i % callback_steps == 0:
                      step_idx = i // getattr(
                          self.scheduler, "order", 1
                      )
                      callback(step_idx, t, latents)
  
      # 9. Post-processing
      if not output_type == "latent":
          image = self.vae.decode(
              latents / self.vae.config.scaling_factor,
              return_dict=False,
          )[0]
          image, has_nsfw_concept = self.run_safety_checker(
              image, device, prompt_embeds.dtype
          )
      else:
          image = latents
          has_nsfw_concept = None
  
      if has_nsfw_concept is None:
          do_denormalize = [True] * image.shape[0]
      else:
          do_denormalize = [
              not has_nsfw for has_nsfw in has_nsfw_concept
          ]
  
      image = self.image_processor.postprocess(
          image, output_type=output_type, do_denormalize=do_denormalize
      )
      self.maybe_free_model_hooks()
  
      if not return_dict:
          return (image, has_nsfw_concept)
  
      return StableDiffusionPipelineOutput(
          images=image, nsfw_content_detected=has_nsfw_concept
      )
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from packaging import version
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from ....configuration_utils import FrozenDict
  from ....image_processor import PipelineImageInput, VaeImageProcessor
  from ....loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ....models import AutoencoderKL, UNet2DConditionModel
  from ....models.lora import adjust_lora_scale_text_encoder
  from ....schedulers import DDIMScheduler
  from ....utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
  from ....utils.torch_utils import randn_tensor
  from ...pipeline_utils import DiffusionPipeline
  from ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
  from ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker
label_desc: Use image node_id 10 for calling the CycleDiffusionPipeline obj's underlying member method code with example usage: obj.__call__(prompt, source_prompt, image, strength, num_inference_steps, guidance_scale, source_guidance_scale, num_images_per_prompt, eta, generator, prompt_embeds, output_type, return_dict, callback, callback_steps, cross_attention_kwargs, clip_skip) and returns: StableDiffusionPipelineOutput, image,  has_nsfw_concept