class_docstr: Pipeline for text-to-image generation using MultiDiffusion.
  
  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`.
code: def decode_latents_with_padding(self, latents, padding=8):
      # Add padding to latents for circular inference
      # padding is the number of latents to add on each side
      # it would slightly increase the memory usage, but remove the boundary artifacts
      latents = 1 / self.vae.config.scaling_factor * latents
      latents_left = latents[..., :padding]
      latents_right = latents[..., -padding:]
      latents = torch.cat(
          (latents_right, latents, latents_left), axis=-1
      )
      image = self.vae.decode(latents, return_dict=False)[0]
      padding_pix = self.vae_scale_factor * padding
      image = image[..., padding_pix:-padding_pix]
      return image
  
imports: import copy
  import inspect
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  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 DDIMScheduler
  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 8 for calling the StableDiffusionPanoramaPipeline obj's underlying member method code with example usage: obj.decode_latents_with_padding(latents, padding) and returns: image