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`.
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code: def __init__(
      self,
      vae: AutoencoderKL,
      text_encoder: CLIPTextModel,
      tokenizer: CLIPTokenizer,
      unet: UNet2DConditionModel,
      scheduler: DDIMScheduler,
      safety_checker: StableDiffusionSafetyChecker,
      feature_extractor: CLIPImageProcessor,
      requires_safety_checker: bool = True,
  ):
      super().__init__()
  
      if (
          hasattr(scheduler.config, "steps_offset")
          and scheduler.config.steps_offset != 1
      ):
          deprecation_message = (
              f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
              f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
              "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
              " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
              " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
              " file"
          )
          deprecate(
              "steps_offset!=1",
              "1.0.0",
              deprecation_message,
              standard_warn=False,
          )
          new_config = dict(scheduler.config)
          new_config["steps_offset"] = 1
          scheduler._internal_dict = FrozenDict(new_config)
  
      if safety_checker is None and requires_safety_checker:
          logger.warning(
              f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
              " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
              " results in services or applications open to the public. Both the diffusers team and Hugging Face"
              " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
              " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
              " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
          )
  
      if safety_checker is not None and feature_extractor is None:
          raise ValueError(
              "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
              " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
          )
      is_unet_version_less_0_9_0 = hasattr(
          unet.config, "_diffusers_version"
      ) and version.parse(
          version.parse(unet.config._diffusers_version).base_version
      ) < version.parse(
          "0.9.0.dev0"
      )
      is_unet_sample_size_less_64 = (
          hasattr(unet.config, "sample_size")
          and unet.config.sample_size < 64
      )
      if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
          deprecation_message = (
              "The configuration file of the unet has set the default `sample_size` to smaller than"
              " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
              " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
              " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
              " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
              " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
              " in the config might lead to incorrect results in future versions. If you have downloaded this"
              " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
              " the `unet/config.json` file"
          )
          deprecate(
              "sample_size<64",
              "1.0.0",
              deprecation_message,
              standard_warn=False,
          )
          new_config = dict(unet.config)
          new_config["sample_size"] = 64
          unet._internal_dict = FrozenDict(new_config)
  
      self.register_modules(
          vae=vae,
          text_encoder=text_encoder,
          tokenizer=tokenizer,
          unet=unet,
          scheduler=scheduler,
          safety_checker=safety_checker,
          feature_extractor=feature_extractor,
      )
      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.register_to_config(
          requires_safety_checker=requires_safety_checker
      )
  
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 1 to create a new CycleDiffusionPipeline object from inherited base classes: DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin with example: obj = CycleDiffusionPipeline(vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker)