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 get_sample_fn(self, timestep_respacing):
      diffusion_kwargs = copy.deepcopy(self._diffusion_kwargs)
      diffusion_kwargs.update(timestep_respacing=timestep_respacing)
      diffusion = create_gaussian_diffusion(**diffusion_kwargs)
      return diffusion.p_sample_loop_progressive_for_improved_sr
  
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 3 for calling the ImprovedSupRes64to256ProgressiveModel obj's underlying member method code with example usage: obj.get_sample_fn(timestep_respacing) and returns: diffusion