class_docstr: Pipeline for text-to-image generation using Stable Diffusion.
  
  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:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
      text_encoder ([`CLIPTextModel`]):
          Frozen text-encoder. Stable Diffusion uses the text portion of
          [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
          the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
      tokenizer (`CLIPTokenizer`):
          Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
      scheduler ([`SchedulerMixin`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
          [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
      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/CompVis/stable-diffusion-v1-4) for details.
      feature_extractor ([`CLIPImageProcessor`]):
          Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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code: def __init__(
      self,
      vae: AutoencoderKL,
      text_encoder: CLIPTextModel,
      tokenizer: CLIPTokenizer,
      unet: UNet2DConditionModel,
      scheduler: Union[
          DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler
      ],
      safety_checker: StableDiffusionSafetyChecker,
      feature_extractor: CLIPImageProcessor,
  ):
      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              f"The configuration file of this scheduler: {scheduler} is outdated.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:
          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 ."
          )
  
      self.register_modules(
          vae=vae,
          text_encoder=text_encoder,
          tokenizer=tokenizer,
          unet=unet,
          scheduler=scheduler,
          safety_checker=safety_checker,
          feature_extractor=feature_extractor,
      )
  
imports: import inspect
  import time
  from pathlib import Path
  from typing import Callable, List, Optional, Union
  import numpy
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers import DiffusionPipeline
  from diffusers.configuration_utils import FrozenDict
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.utils import deprecate, logging
label_desc: Use image node_id 1 to create a new StableDiffusionWalkPipeline object from inherited base classes: DiffusionPipeline with example: obj = StableDiffusionWalkPipeline(vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor)