class_docstr: Pipeline for text-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.).
  
  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 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 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 pred_epsilon(self, sample, model_output, timestep):
      alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
  
      beta_prod_t = 1 - alpha_prod_t
      if self.scheduler.config.prediction_type == "epsilon":
          pred_eps = model_output
      elif self.scheduler.config.prediction_type == "sample":
          pred_eps = (sample - (alpha_prod_t**0.5) * model_output) / (
              beta_prod_t**0.5
          )
      elif self.scheduler.config.prediction_type == "v_prediction":
          pred_eps = (beta_prod_t**0.5) * sample + (
              alpha_prod_t**0.5
          ) * model_output
      else:
          raise ValueError(
              f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,"
              " or `v_prediction`"
          )
  
      return pred_eps
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  import torch.nn.functional
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from .None import StableDiffusionPipelineOutput
  from .safety_checker import StableDiffusionSafetyChecker
label_desc: Use image node_id 14 for calling the StableDiffusionSAGPipeline obj's underlying member method code with example usage: obj.pred_epsilon(sample, model_output, timestep) and returns: pred_eps