class_docstr: Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
  weighting in prompt.
  
  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 check_inputs(
      self,
      prompt,
      height,
      width,
      strength,
      callback_steps,
      negative_prompt=None,
      prompt_embeds=None,
      negative_prompt_embeds=None,
  ):
      if height % 8 != 0 or width % 8 != 0:
          raise ValueError(
              f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
          )
  
      if strength < 0 or strength > 1:
          raise ValueError(
              f"The value of strength should in [0.0, 1.0] but is {strength}"
          )
  
      if (callback_steps is None) or (
          callback_steps is not None
          and (
              not isinstance(callback_steps, int) or callback_steps <= 0
          )
      ):
          raise ValueError(
              f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
              f" {type(callback_steps)}."
          )
  
      if prompt is not None and prompt_embeds is not None:
          raise ValueError(
              f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
              " only forward one of the two."
          )
      elif prompt is None and prompt_embeds is None:
          raise ValueError(
              "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
          )
      elif prompt is not None and (
          not isinstance(prompt, str) and not isinstance(prompt, list)
      ):
          raise ValueError(
              f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
          )
  
      if (
          negative_prompt is not None
          and negative_prompt_embeds is not None
      ):
          raise ValueError(
              f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
              f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
          )
  
      if (
          prompt_embeds is not None
          and negative_prompt_embeds is not None
      ):
          if prompt_embeds.shape != negative_prompt_embeds.shape:
              raise ValueError(
                  "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                  f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                  f" {negative_prompt_embeds.shape}."
              )
  
imports: import inspect
  import re
  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 diffusers import DiffusionPipeline
  from diffusers.configuration_utils import FrozenDict
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import PIL_INTERPOLATION, deprecate, is_accelerate_available, is_accelerate_version, logging
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 10 for calling the StableDiffusionLongPromptWeightingPipeline obj's underlying member method code with example usage: obj.check_inputs(prompt, height, width, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) without return types