class_docstr: ImprovedSR model fine-tunes the pretrained DDPM-based SR model by using adversarial and perceptual losses.
  In specific, the low-resolution sample is iteratively recovered by 6 steps with the frozen pretrained SR model.
  In the following additional one step, a seperate fine-tuned model recovers high-frequency details.
  This approach greatly improves the fidelity of images of 256x256px, even with small number of reverse steps.
code: def __init__(self, config):
      super().__init__()
  
      self._config = config
      self._diffusion_kwargs = dict(
          steps=config.diffusion.steps,
          learn_sigma=config.diffusion.learn_sigma,
          sigma_small=config.diffusion.sigma_small,
          noise_schedule=config.diffusion.noise_schedule,
          use_kl=config.diffusion.use_kl,
          predict_xstart=config.diffusion.predict_xstart,
          rescale_learned_sigmas=config.diffusion.rescale_learned_sigmas,
      )
  
      self.model_first_steps = SuperResUNetModel(
          in_channels=3,  # auto-changed to 6 inside the model
          model_channels=config.model.hparams.channels,
          out_channels=3,
          num_res_blocks=config.model.hparams.depth,
          attention_resolutions=(),  # no attention
          dropout=config.model.hparams.dropout,
          channel_mult=config.model.hparams.channels_multiple,
          resblock_updown=True,
          use_middle_attention=False,
      )
      self.model_last_step = SuperResUNetModel(
          in_channels=3,  # auto-changed to 6 inside the model
          model_channels=config.model.hparams.channels,
          out_channels=3,
          num_res_blocks=config.model.hparams.depth,
          attention_resolutions=(),  # no attention
          dropout=config.model.hparams.dropout,
          channel_mult=config.model.hparams.channels_multiple,
          resblock_updown=True,
          use_middle_attention=False,
      )
  
file_path: StableDiffusion/ldm/modules/karlo/kakao/models/sr_64_256.py
imports: import copy
  import torch
  from ldm.modules.karlo.kakao.modules.unet import SuperResUNetModel
  from ldm.modules.karlo.kakao.modules import create_gaussian_diffusion
label_desc: Use image node_id 1 to create a new ImprovedSupRes64to256ProgressiveModel object from inherited base classes: torch.nn with example: obj = ImprovedSupRes64to256ProgressiveModel(config)