class_docstr: A text2im model that performs super-resolution.
  Expects an extra kwarg `low_res` to condition on a low-resolution image.
code: def __init__(self, *args, **kwargs):
      if "in_channels" in kwargs:
          kwargs = dict(kwargs)
          kwargs["in_channels"] = kwargs["in_channels"] * 2
      else:
          # Curse you, Python. Or really, just curse positional arguments :|.
          args = list(args)
          args[1] = args[1] * 2
      super().__init__(*args, **kwargs)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .nn import timestep_embedding
  from .unet import UNetModel
  from .xf import LayerNorm, Transformer, convert_module_to_f16
label_desc: Use image node_id 1 to create a new SuperResText2ImUNet object from inherited base classes: Text2ImUNet with example: obj = SuperResText2ImUNet()