class_docstr: Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
  that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
  instead of one. Originally, the second prompt is just an empty string, but we use non-empty
  negative prompt.
class_docstr_tok: ["Classifier","free","guidance","denoiser",".","A","wrapper","for","stable","diffusion","model","(","specifically","for","unet",")","that","can","take","a","noisy","picture","and","produce","a","noise-free","picture","using","two","guidances","(","prompts",")","instead","of","one",".","Originally",",","the","second","prompt","is","just","an","empty","string",",","but","we","use","non-empty","negative","prompt","."]
code: def combine_denoised_for_edit_model(self, x_out, cond_scale):
      out_cond, out_img_cond, out_uncond = x_out.chunk(3)
      denoised = (
          out_uncond
          + cond_scale * (out_cond - out_img_cond)
          + self.image_cfg_scale * (out_img_cond - out_uncond)
      )
  
      return denoised
  
file_path: stable-diffusion-webui/modules/sd_samplers_cfg_denoiser.py
imports: import torch
  from modules import prompt_parser, devices, sd_samplers_common
  from modules.shared import opts, state
  import modules.shared
  from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
  from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
  from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
label_desc: Use image node_id 4 for calling the CFGDenoiser obj's underlying member method code with example usage: obj.combine_denoised_for_edit_model(x_out, cond_scale) and returns: denoised