code: def run_modelmerger(
      id_task,
      primary_model_name,
      secondary_model_name,
      tertiary_model_name,
      interp_method,
      multiplier,
      save_as_half,
      custom_name,
      checkpoint_format,
      config_source,
      bake_in_vae,
      discard_weights,
      save_metadata,
      add_merge_recipe,
      copy_metadata_fields,
      metadata_json,
  ):
      shared.state.begin(job="model-merge")
  
      def fail(message):
          shared.state.textinfo = message
          shared.state.end()
          return [*[gr.update() for _ in range(4)], message]
  
      def weighted_sum(theta0, theta1, alpha):
          return ((1 - alpha) * theta0) + (alpha * theta1)
  
      def get_difference(theta1, theta2):
          return theta1 - theta2
  
      def add_difference(theta0, theta1_2_diff, alpha):
          return theta0 + (alpha * theta1_2_diff)
  
      def filename_weighted_sum():
          a = primary_model_info.model_name
          b = secondary_model_info.model_name
          Ma = round(1 - multiplier, 2)
          Mb = round(multiplier, 2)
  
          return f"{Ma}({a}) + {Mb}({b})"
  
      def filename_add_difference():
          a = primary_model_info.model_name
          b = secondary_model_info.model_name
          c = tertiary_model_info.model_name
          M = round(multiplier, 2)
  
          return f"{a} + {M}({b} - {c})"
  
      def filename_nothing():
          return primary_model_info.model_name
  
      theta_funcs = {
          "Weighted sum": (filename_weighted_sum, None, weighted_sum),
          "Add difference": (
              filename_add_difference,
              get_difference,
              add_difference,
          ),
          "No interpolation": (filename_nothing, None, None),
      }
      filename_generator, theta_func1, theta_func2 = theta_funcs[
          interp_method
      ]
      shared.state.job_count = (1 if theta_func1 else 0) + (
          1 if theta_func2 else 0
      )
  
      if not primary_model_name:
          return fail("Failed: Merging requires a primary model.")
  
      primary_model_info = sd_models.checkpoints_list[
          primary_model_name
      ]
  
      if theta_func2 and not secondary_model_name:
          return fail("Failed: Merging requires a secondary model.")
  
      secondary_model_info = (
          sd_models.checkpoints_list[secondary_model_name]
          if theta_func2
          else None
      )
  
      if theta_func1 and not tertiary_model_name:
          return fail(
              f"Failed: Interpolation method ({interp_method}) requires a tertiary model."
          )
  
      tertiary_model_info = (
          sd_models.checkpoints_list[tertiary_model_name]
          if theta_func1
          else None
      )
  
      result_is_inpainting_model = False
      result_is_instruct_pix2pix_model = False
  
      if theta_func2:
          shared.state.textinfo = "Loading B"
          print(f"Loading {secondary_model_info.filename}...")
          theta_1 = sd_models.read_state_dict(
              secondary_model_info.filename, map_location="cpu"
          )
      else:
          theta_1 = None
  
      if theta_func1:
          shared.state.textinfo = "Loading C"
          print(f"Loading {tertiary_model_info.filename}...")
          theta_2 = sd_models.read_state_dict(
              tertiary_model_info.filename, map_location="cpu"
          )
  
          shared.state.textinfo = "Merging B and C"
          shared.state.sampling_steps = len(theta_1.keys())
          for key in tqdm.tqdm(theta_1.keys()):
              if key in checkpoint_dict_skip_on_merge:
                  continue
  
              if "model" in key:
                  if key in theta_2:
                      t2 = theta_2.get(
                          key, torch.zeros_like(theta_1[key])
                      )
                      theta_1[key] = theta_func1(theta_1[key], t2)
                  else:
                      theta_1[key] = torch.zeros_like(theta_1[key])
  
              shared.state.sampling_step += 1
          del theta_2
  
          shared.state.nextjob()
  
      shared.state.textinfo = (
          f"Loading {primary_model_info.filename}..."
      )
      print(f"Loading {primary_model_info.filename}...")
      theta_0 = sd_models.read_state_dict(
          primary_model_info.filename, map_location="cpu"
      )
  
      print("Merging...")
      shared.state.textinfo = "Merging A and B"
      shared.state.sampling_steps = len(theta_0.keys())
      for key in tqdm.tqdm(theta_0.keys()):
          if theta_1 and "model" in key and key in theta_1:
              if key in checkpoint_dict_skip_on_merge:
                  continue
  
              a = theta_0[key]
              b = theta_1[key]
  
              # this enables merging an inpainting model (A) with another one (B);
              # where normal model would have 4 channels, for latenst space, inpainting model would
              # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
              if (
                  a.shape != b.shape
                  and a.shape[0:1] + a.shape[2:]
                  == b.shape[0:1] + b.shape[2:]
              ):
                  if a.shape[1] == 4 and b.shape[1] == 9:
                      raise RuntimeError(
                          "When merging inpainting model with a normal one, A must be the inpainting model."
                      )
                  if a.shape[1] == 4 and b.shape[1] == 8:
                      raise RuntimeError(
                          "When merging instruct-pix2pix model with a normal one, A must be the instruct-pix2pix model."
                      )
  
                  if (
                      a.shape[1] == 8 and b.shape[1] == 4
                  ):  # If we have an Instruct-Pix2Pix model...
                      theta_0[key][:, 0:4, :, :] = theta_func2(
                          a[:, 0:4, :, :], b, multiplier
                      )  # Merge only the vectors the models have in common.  Otherwise we get an error due to dimension mismatch.
                      result_is_instruct_pix2pix_model = True
                  else:
                      assert (
                          a.shape[1] == 9 and b.shape[1] == 4
                      ), f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
                      theta_0[key][:, 0:4, :, :] = theta_func2(
                          a[:, 0:4, :, :], b, multiplier
                      )
                      result_is_inpainting_model = True
              else:
                  theta_0[key] = theta_func2(a, b, multiplier)
  
              theta_0[key] = to_half(theta_0[key], save_as_half)
  
          shared.state.sampling_step += 1
  
      del theta_1
  
      bake_in_vae_filename = sd_vae.vae_dict.get(bake_in_vae, None)
      if bake_in_vae_filename is not None:
          print(f"Baking in VAE from {bake_in_vae_filename}")
          shared.state.textinfo = "Baking in VAE"
          vae_dict = sd_vae.load_vae_dict(
              bake_in_vae_filename, map_location="cpu"
          )
  
          for key in vae_dict.keys():
              theta_0_key = "first_stage_model." + key
              if theta_0_key in theta_0:
                  theta_0[theta_0_key] = to_half(
                      vae_dict[key], save_as_half
                  )
  
          del vae_dict
  
      if save_as_half and not theta_func2:
          for key in theta_0.keys():
              theta_0[key] = to_half(theta_0[key], save_as_half)
  
      if discard_weights:
          regex = re.compile(discard_weights)
          for key in list(theta_0):
              if re.search(regex, key):
                  theta_0.pop(key, None)
  
      ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
  
      filename = (
          filename_generator() if custom_name == "" else custom_name
      )
      filename += ".inpainting" if result_is_inpainting_model else ""
      filename += (
          ".instruct-pix2pix"
          if result_is_instruct_pix2pix_model
          else ""
      )
      filename += "." + checkpoint_format
  
      output_modelname = os.path.join(ckpt_dir, filename)
  
      shared.state.nextjob()
      shared.state.textinfo = "Saving"
      print(f"Saving to {output_modelname}...")
  
      metadata = {}
  
      if save_metadata and copy_metadata_fields:
          if primary_model_info:
              metadata.update(primary_model_info.metadata)
          if secondary_model_info:
              metadata.update(secondary_model_info.metadata)
          if tertiary_model_info:
              metadata.update(tertiary_model_info.metadata)
  
      if save_metadata:
          try:
              metadata.update(json.loads(metadata_json))
          except Exception as e:
              errors.display(e, "readin metadata from json")
  
          metadata["format"] = "pt"
  
      if save_metadata and add_merge_recipe:
          merge_recipe = {
              "type": "webui",  # indicate this model was merged with webui's built-in merger
              "primary_model_hash": primary_model_info.sha256,
              "secondary_model_hash": secondary_model_info.sha256
              if secondary_model_info
              else None,
              "tertiary_model_hash": tertiary_model_info.sha256
              if tertiary_model_info
              else None,
              "interp_method": interp_method,
              "multiplier": multiplier,
              "save_as_half": save_as_half,
              "custom_name": custom_name,
              "config_source": config_source,
              "bake_in_vae": bake_in_vae,
              "discard_weights": discard_weights,
              "is_inpainting": result_is_inpainting_model,
              "is_instruct_pix2pix": result_is_instruct_pix2pix_model,
          }
  
          sd_merge_models = {}
  
          def add_model_metadata(checkpoint_info):
              checkpoint_info.calculate_shorthash()
              sd_merge_models[checkpoint_info.sha256] = {
                  "name": checkpoint_info.name,
                  "legacy_hash": checkpoint_info.hash,
                  "sd_merge_recipe": checkpoint_info.metadata.get(
                      "sd_merge_recipe", None
                  ),
              }
  
              sd_merge_models.update(
                  checkpoint_info.metadata.get("sd_merge_models", {})
              )
  
          add_model_metadata(primary_model_info)
          if secondary_model_info:
              add_model_metadata(secondary_model_info)
          if tertiary_model_info:
              add_model_metadata(tertiary_model_info)
  
          metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
          metadata["sd_merge_models"] = json.dumps(sd_merge_models)
  
      _, extension = os.path.splitext(output_modelname)
      if extension.lower() == ".safetensors":
          safetensors.torch.save_file(
              theta_0,
              output_modelname,
              metadata=metadata if len(metadata) > 0 else None,
          )
      else:
          torch.save(theta_0, output_modelname)
  
      sd_models.list_models()
      created_model = next(
          (
              ckpt
              for ckpt in sd_models.checkpoints_list.values()
              if ckpt.name == filename
          ),
          None,
      )
      if created_model:
          created_model.calculate_shorthash()
  
      create_config(
          output_modelname,
          config_source,
          primary_model_info,
          secondary_model_info,
          tertiary_model_info,
      )
  
      print(f"Checkpoint saved to {output_modelname}.")
      shared.state.textinfo = "Checkpoint saved"
      shared.state.end()
  
      return [
          *[
              gr.Dropdown.update(choices=sd_models.checkpoint_tiles())
              for _ in range(4)
          ],
          "Checkpoint saved to " + output_modelname,
      ]
  
file_path: stable-diffusion-webui/modules/extras.py
imports: import os
  import re
  import shutil
  import json
  import torch
  import tqdm
  from modules import shared, images, sd_models, sd_vae, sd_models_config, errors
  from modules.ui_common import plaintext_to_html
  import gradio
  import safetensors.torch
label_desc: Use image node_id 5 for calling a global function with example usage: run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata, add_merge_recipe, copy_metadata_fields, metadata_json) and returns: list, list, unknown, unknown, unknown, Ma, str, a, str, Mb, str, b, str, a, str, M, str, b, str, c, str, primary_model_info, fail, fail, fail