class_docstr: *This is an empty wrapper class around the [U-Net](model/unet.html).
  We keep this to have the same model structure as
  [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion)
  so that we do not have to map the checkpoint weights explicitly*.
class_docstr_tok: ["*","This","is","an","empty","wrapper","class","around","the","[","U-Net","]","(","model\/unet.html",")",".","We","keep","this","to","have","the","same","model","structure","as","[","CompVis\/stable-diffusion","]","(","https",":","\/\/github.com\/CompVis\/stable-diffusion",")","so","that","we","do","not","have","to","map","the","checkpoint","weights","explicitly","*","."]
code: def __init__(self, diffusion_model: UNetModel):
      super().__init__()
      self.diffusion_model = diffusion_model
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/diffusion/stable_diffusion/latent_diffusion.py
imports: from typing import List
  import torch
  import torch.nn
  from labml_nn.diffusion.stable_diffusion.model.autoencoder import Autoencoder
  from labml_nn.diffusion.stable_diffusion.model.clip_embedder import CLIPTextEmbedder
  from labml_nn.diffusion.stable_diffusion.model.unet import UNetModel
label_desc: Use image node_id 1 to create a new DiffusionWrapper object from inherited base classes: nn with example: obj = DiffusionWrapper(diffusion_model)