code: def do_img2img(
      img,
      model,
      sampler,
      value_dict,
      num_samples,
      force_uc_zero_embeddings=[],
      additional_kwargs={},
      offset_noise_level: float = 0.0,
      return_latents=False,
      skip_encode=False,
      filter=None,
      device="cuda",
  ):
      with torch.no_grad():
          with autocast(device) as precision_scope:
              with model.ema_scope():
                  batch, batch_uc = get_batch(
                      get_unique_embedder_keys_from_conditioner(
                          model.conditioner
                      ),
                      value_dict,
                      [num_samples],
                  )
                  (
                      c,
                      uc,
                  ) = model.conditioner.get_unconditional_conditioning(
                      batch,
                      batch_uc=batch_uc,
                      force_uc_zero_embeddings=force_uc_zero_embeddings,
                  )
  
                  for k in c:
                      c[k], uc[k] = map(
                          lambda y: y[k][:num_samples].to(device),
                          (c, uc),
                      )
  
                  for k in additional_kwargs:
                      c[k] = uc[k] = additional_kwargs[k]
                  if skip_encode:
                      z = img
                  else:
                      z = model.encode_first_stage(img)
                  noise = torch.randn_like(z)
                  sigmas = sampler.discretization(sampler.num_steps)
                  sigma = sigmas[0].to(z.device)
  
                  if offset_noise_level > 0.0:
                      noise = noise + offset_noise_level * append_dims(
                          torch.randn(z.shape[0], device=z.device),
                          z.ndim,
                      )
                  noised_z = z + noise * append_dims(sigma, z.ndim)
                  noised_z = noised_z / torch.sqrt(
                      1.0 + sigmas[0] ** 2.0
                  )  # Note: hardcoded to DDPM-like scaling. need to generalize later.
  
                  def denoiser(x, sigma, c):
                      return model.denoiser(model.model, x, sigma, c)
  
                  samples_z = sampler(denoiser, noised_z, cond=c, uc=uc)
                  samples_x = model.decode_first_stage(samples_z)
                  samples = torch.clamp(
                      (samples_x + 1.0) / 2.0, min=0.0, max=1.0
                  )
  
                  if filter is not None:
                      samples = filter(samples)
  
                  if return_latents:
                      return samples, samples_z
                  return samples
  
imports: import math
  import os
  from typing import List, Optional, Union
  import numpy
  import torch
  from einops import rearrange
  from imwatermark import WatermarkEncoder
  from omegaconf import ListConfig
  from PIL import Image
  from torch import autocast
  from sgm.util import append_dims
label_desc: Use image node_id 6 for calling a global function with example usage: do_img2img(img, model, sampler, value_dict, num_samples, force_uc_zero_embeddings, additional_kwargs, offset_noise_level, return_latents, skip_encode, filter, device) and returns: samples, model, samples,  samples_z