class_docstr: ## Latent diffusion model
  
  This contains following components:
  
  * [AutoEncoder](model/autoencoder.html)
  * [U-Net](model/unet.html) with [attention](model/unet_attention.html)
  * [CLIP embeddings generator](model/clip_embedder.html)
class_docstr_tok: ["#","#","Latent","diffusion","model","This","contains","following","components",":","*","[","AutoEncoder","]","(","model\/autoencoder.html",")","*","[","U-Net","]","(","model\/unet.html",")","with","[","attention","]","(","model\/unet_attention.html",")","*","[","CLIP","embeddings","generator","]","(","model\/clip_embedder.html",")"]
docstr: ### Get [CLIP embeddings](model/clip_embedder.html) for a list of text prompts
docstr_tok: ["#","#","#","Get","[","CLIP","embeddings","]","(","model\/clip_embedder.html",")","for","a","list","of","text","prompts"]
code: def get_text_conditioning(self, prompts: List[str]):
      """
      ### Get [CLIP embeddings](model/clip_embedder.html) for a list of text prompts
      """
      return self.cond_stage_model(prompts)
  
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 3 for calling the LatentDiffusion obj's underlying member method code with example usage: obj.get_text_conditioning(prompts) and returns: self