class_docstr: Pipeline for text-to-image generation using Alt 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.).
  
  The pipeline also inherits the following loading methods:
      - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
      - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
      - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
      - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
      - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
      text_encoder ([`~transformers.RobertaSeriesModelWithTransformation`]):
          Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      tokenizer ([`~transformers.XLMRobertaTokenizer`]):
          A `XLMRobertaTokenizer` 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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docstr: Disables the FreeU mechanism if enabled.
docstr_tok: ["Disables","the","FreeU","mechanism","if","enabled","."]
code: def disable_freeu(self):
      """Disables the FreeU mechanism if enabled."""
      self.unet.disable_freeu()
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  from packaging import version
  from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, XLMRobertaTokenizer
  from ....configuration_utils import FrozenDict
  from ....image_processor import PipelineImageInput, VaeImageProcessor
  from ....loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from ....models import AutoencoderKL, ImageProjection, UNet2DConditionModel
  from ....models.attention_processor import FusedAttnProcessor2_0
  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 ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from .modeling_roberta_series import RobertaSeriesModelWithTransformation
  from .pipeline_output import AltDiffusionPipelineOutput
label_desc: Use image node_id 15 for calling the AltDiffusionPipeline obj's underlying member method code with example usage: obj.disable_freeu() without return types