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.)
  
  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
  
  <Tip warning={true}>
  
      This is an experimental pipeline and is likely to change in the future.
  
  </Tip>
  
  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/runwayml/stable-diffusion-v1-5) 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 set_scheduler(self, scheduler_type: str):
      library = importlib.import_module("k_diffusion")
      sampling = getattr(library, "sampling")
      try:
          self.sampler = getattr(sampling, scheduler_type)
      except Exception:
          valid_samplers = []
          for s in dir(sampling):
              if "sample_" in s:
                  valid_samplers.append(s)
  
          raise ValueError(
              f"Invalid scheduler type {scheduler_type}. Please choose one of {valid_samplers}."
          )
  
imports: import importlib
  import inspect
  from typing import Callable, List, Optional, Union
  import torch
  from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
  from k_diffusion.sampling import BrownianTreeNoiseSampler, get_sigmas_karras
  from ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import LMSDiscreteScheduler
  from ...utils import 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 import StableDiffusionPipelineOutput
label_desc: Use image node_id 2 for calling the StableDiffusionKDiffusionPipeline obj's underlying member method code with example usage: obj.set_scheduler(scheduler_type) without return types