code: def cheap_approximation(sample):
      # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
  
      if shared.sd_model.is_sdxl:
          coeffs = [
              [0.3448, 0.4168, 0.4395],
              [-0.1953, -0.0290, 0.0250],
              [0.1074, 0.0886, -0.0163],
              [-0.3730, -0.2499, -0.2088],
          ]
      else:
          coeffs = [
              [0.298, 0.207, 0.208],
              [0.187, 0.286, 0.173],
              [-0.158, 0.189, 0.264],
              [-0.184, -0.271, -0.473],
          ]
  
      coefs = torch.tensor(coeffs).to(sample.device)
  
      x_sample = torch.einsum("...lxy,lr -> ...rxy", sample, coefs)
  
      return x_sample
  
file_path: stable-diffusion-webui/modules/sd_vae_approx.py
imports: import os
  import torch
  from torch import nn
  from modules import devices, paths, shared
label_desc: Use image node_id 3 for calling a global function with example usage: cheap_approximation(sample) and returns: x_sample