class_docstr: Combined Pipeline for text-to-image generation using Wuerstchen
  
  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:
      tokenizer (`CLIPTokenizer`):
          The decoder tokenizer to be used for text inputs.
      text_encoder (`CLIPTextModel`):
          The decoder text encoder to be used for text inputs.
      decoder (`WuerstchenDiffNeXt`):
          The decoder model to be used for decoder image generation pipeline.
      scheduler (`DDPMWuerstchenScheduler`):
          The scheduler to be used for decoder image generation pipeline.
      vqgan (`PaellaVQModel`):
          The VQGAN model to be used for decoder image generation pipeline.
      prior_tokenizer (`CLIPTokenizer`):
          The prior tokenizer to be used for text inputs.
      prior_text_encoder (`CLIPTextModel`):
          The prior text encoder to be used for text inputs.
      prior_prior (`WuerstchenPrior`):
          The prior model to be used for prior pipeline.
      prior_scheduler (`DDPMWuerstchenScheduler`):
          The scheduler to be used for prior pipeline.
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docstr: Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗
  Accelerate, significantly reducing memory usage. Models are moved to a `torch.device('meta')` and loaded on a
  GPU only when their specific submodule's `forward` method is called. Offloading happens on a submodule basis.
  Memory savings are higher than using `enable_model_cpu_offload`, but performance is lower.
docstr_tok: ["Offloads","all","models","(","`","unet","`",",","`","text_encoder","`",",","`","vae","`",",","and","`","safety","checker","`","state","dicts",")","to","CPU","using","\ud83e\udd17","Accelerate",",","significantly","reducing","memory","usage",".","Models","are","moved","to","a","`","torch.device","(","'meta","'",")","`","and","loaded","on","a","GPU","only","when","their","specific","submodule","'s","`","forward","`","method","is","called",".","Offloading","happens","on","a","submodule","basis",".","Memory","savings","are","higher","than","using","`","enable_model_cpu_offload","`",",","but","performance","is","lower","."]
code: def enable_sequential_cpu_offload(self, gpu_id=0):
      r"""
      Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗
      Accelerate, significantly reducing memory usage. Models are moved to a `torch.device('meta')` and loaded on a
      GPU only when their specific submodule's `forward` method is called. Offloading happens on a submodule basis.
      Memory savings are higher than using `enable_model_cpu_offload`, but performance is lower.
      """
      self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
      self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
  
file_path: llmga/llmga/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py
imports: from typing import Callable, List, Optional, Union
  import torch
  from transformers import CLIPTextModel, CLIPTokenizer
  from ...schedulers import DDPMWuerstchenScheduler
  from ...utils import replace_example_docstring
  from ..pipeline_utils import DiffusionPipeline
  from .modeling_paella_vq_model import PaellaVQModel
  from .modeling_wuerstchen_diffnext import WuerstchenDiffNeXt
  from .modeling_wuerstchen_prior import WuerstchenPrior
  from .pipeline_wuerstchen import WuerstchenDecoderPipeline
  from .pipeline_wuerstchen_prior import WuerstchenPriorPipeline
label_desc: Use image node_id 4 for calling the WuerstchenCombinedPipeline obj's underlying member method code with example usage: obj.enable_sequential_cpu_offload(gpu_id) without return types