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`.
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__()
      self.register_modules(
          vae=vae,
          text_encoder=text_encoder,
          tokenizer=tokenizer,
          unet=unet,
          scheduler=scheduler,
          safety_checker=safety_checker,
          feature_extractor=feature_extractor,
      )
  
file_path: llmga/llmga/diffusers/examples/community/seed_resize_stable_diffusion.py
imports: import inspect
  from typing import Callable, List, Optional, Union
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers import DiffusionPipeline
  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 logging
label_desc: Use image node_id 1 to create a new SeedResizeStableDiffusionPipeline object from inherited base classes: DiffusionPipeline with example: obj = SeedResizeStableDiffusionPipeline(vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor)