diff --git a/modules/postprocessing.py b/modules/postprocessing.py
new file mode 100644
index 000000000..385430dc7
--- /dev/null
+++ b/modules/postprocessing.py
@@ -0,0 +1,466 @@
+from __future__ import annotations
+import math
+import os
+import re
+import sys
+import traceback
+import shutil
+
+import numpy as np
+from PIL import Image
+
+import torch
+import tqdm
+
+from typing import Callable, List, OrderedDict, Tuple
+from functools import partial
+from dataclasses import dataclass
+
+from modules import processing, shared, images, devices, sd_models, sd_samplers, sd_vae
+from modules.shared import opts
+import modules.gfpgan_model
+from modules.ui import plaintext_to_html
+import modules.codeformer_model
+import gradio as gr
+import safetensors.torch
+
+class LruCache(OrderedDict):
+    @dataclass(frozen=True)
+    class Key:
+        image_hash: int
+        info_hash: int
+        args_hash: int
+
+    @dataclass
+    class Value:
+        image: Image.Image
+        info: str
+
+    def __init__(self, max_size: int = 5, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._max_size = max_size
+
+    def get(self, key: LruCache.Key) -> LruCache.Value:
+        ret = super().get(key)
+        if ret is not None:
+            self.move_to_end(key)  # Move to end of eviction list
+        return ret
+
+    def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
+        self[key] = value
+        while len(self) > self._max_size:
+            self.popitem(last=False)
+
+
+cached_images: LruCache = LruCache(max_size=5)
+
+
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
+    devices.torch_gc()
+
+    shared.state.begin()
+    shared.state.job = 'extras'
+
+    imageArr = []
+    # Also keep track of original file names
+    imageNameArr = []
+    outputs = []
+
+    if extras_mode == 1:
+        #convert file to pillow image
+        for img in image_folder:
+            image = Image.open(img)
+            imageArr.append(image)
+            imageNameArr.append(os.path.splitext(img.orig_name)[0])
+    elif extras_mode == 2:
+        assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
+
+        if input_dir == '':
+            return outputs, "Please select an input directory.", ''
+        image_list = shared.listfiles(input_dir)
+        for img in image_list:
+            try:
+                image = Image.open(img)
+            except Exception:
+                continue
+            imageArr.append(image)
+            imageNameArr.append(img)
+    else:
+        imageArr.append(image)
+        imageNameArr.append(None)
+
+    if extras_mode == 2 and output_dir != '':
+        outpath = output_dir
+    else:
+        outpath = opts.outdir_samples or opts.outdir_extras_samples
+
+    # Extra operation definitions
+
+    def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        shared.state.job = 'extras-gfpgan'
+        restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
+        res = Image.fromarray(restored_img)
+
+        if gfpgan_visibility < 1.0:
+            res = Image.blend(image, res, gfpgan_visibility)
+
+        info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
+        return (res, info)
+
+    def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        shared.state.job = 'extras-codeformer'
+        restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
+        res = Image.fromarray(restored_img)
+
+        if codeformer_visibility < 1.0:
+            res = Image.blend(image, res, codeformer_visibility)
+
+        info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
+        return (res, info)
+
+    def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
+        shared.state.job = 'extras-upscale'
+        upscaler = shared.sd_upscalers[scaler_index]
+        res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+        if mode == 1 and crop:
+            cropped = Image.new("RGB", (resize_w, resize_h))
+            cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))
+            res = cropped
+        return res
+
+    def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        # Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
+        nonlocal upscaling_resize
+        if resize_mode == 1:
+            upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
+            crop_info = " (crop)" if upscaling_crop else ""
+            info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
+        return (image, info)
+
+    @dataclass
+    class UpscaleParams:
+        upscaler_idx: int
+        blend_alpha: float
+
+    def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        blended_result: Image.Image = None
+        image_hash: str = hash(np.array(image.getdata()).tobytes())
+        for upscaler in params:
+            upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
+                            upscaling_resize_w, upscaling_resize_h, upscaling_crop)
+            cache_key = LruCache.Key(image_hash=image_hash,
+                                     info_hash=hash(info),
+                                     args_hash=hash(upscale_args))
+            cached_entry = cached_images.get(cache_key)
+            if cached_entry is None:
+                res = upscale(image, *upscale_args)
+                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
+                cached_images.put(cache_key, LruCache.Value(image=res, info=info))
+            else:
+                res, info = cached_entry.image, cached_entry.info
+
+            if blended_result is None:
+                blended_result = res
+            else:
+                blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)
+        return (blended_result, info)
+
+    # Build a list of operations to run
+    facefix_ops: List[Callable] = []
+    facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []
+    facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []
+
+    upscale_ops: List[Callable] = []
+    upscale_ops += [run_prepare_crop] if resize_mode == 1 else []
+
+    if upscaling_resize != 0:
+        step_params: List[UpscaleParams] = []
+        step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))
+        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
+            step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))
+
+        upscale_ops.append(partial(run_upscalers_blend, step_params))
+
+    extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
+
+    for image, image_name in zip(imageArr, imageNameArr):
+        if image is None:
+            return outputs, "Please select an input image.", ''
+
+        shared.state.textinfo = f'Processing image {image_name}'
+        
+        existing_pnginfo = image.info or {}
+
+        image = image.convert("RGB")
+        info = ""
+        # Run each operation on each image
+        for op in extras_ops:
+            image, info = op(image, info)
+
+        if opts.use_original_name_batch and image_name is not None:
+            basename = os.path.splitext(os.path.basename(image_name))[0]
+        else:
+            basename = ''
+
+        if opts.enable_pnginfo: # append info before save
+            image.info = existing_pnginfo
+            image.info["extras"] = info
+
+        if save_output:
+            # Add upscaler name as a suffix.
+            suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
+            # Add second upscaler if applicable.
+            if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:
+                suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
+
+            images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                            no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
+
+        if extras_mode != 2 or show_extras_results :
+            outputs.append(image)
+
+    devices.torch_gc()
+
+    return outputs, plaintext_to_html(info), ''
+
+def clear_cache():
+    cached_images.clear()
+
+
+def run_pnginfo(image):
+    if image is None:
+        return '', '', ''
+
+    geninfo, items = images.read_info_from_image(image)
+    items = {**{'parameters': geninfo}, **items}
+
+    info = ''
+    for key, text in items.items():
+        info += f"""
+<div>
+<p><b>{plaintext_to_html(str(key))}</b></p>
+<p>{plaintext_to_html(str(text))}</p>
+</div>
+""".strip()+"\n"
+
+    if len(info) == 0:
+        message = "Nothing found in the image."
+        info = f"<div><p>{message}<p></div>"
+
+    return '', geninfo, info
+
+
+def create_config(ckpt_result, config_source, a, b, c):
+    def config(x):
+        res = sd_models.find_checkpoint_config(x) if x else None
+        return res if res != shared.sd_default_config else None
+
+    if config_source == 0:
+        cfg = config(a) or config(b) or config(c)
+    elif config_source == 1:
+        cfg = config(b)
+    elif config_source == 2:
+        cfg = config(c)
+    else:
+        cfg = None
+
+    if cfg is None:
+        return
+
+    filename, _ = os.path.splitext(ckpt_result)
+    checkpoint_filename = filename + ".yaml"
+
+    print("Copying config:")
+    print("   from:", cfg)
+    print("     to:", checkpoint_filename)
+    shutil.copyfile(cfg, checkpoint_filename)
+
+
+checkpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"]
+
+
+def to_half(tensor, enable):
+    if enable and tensor.dtype == torch.float:
+        return tensor.half()
+
+    return tensor
+
+
+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):
+    shared.state.begin()
+    shared.state.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
+
+    if theta_func2:
+        shared.state.textinfo = f"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 = f"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.")
+
+                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 += "." + checkpoint_format
+
+    output_modelname = os.path.join(ckpt_dir, filename)
+
+    shared.state.nextjob()
+    shared.state.textinfo = "Saving"
+    print(f"Saving to {output_modelname}...")
+
+    _, extension = os.path.splitext(output_modelname)
+    if extension.lower() == ".safetensors":
+        safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
+    else:
+        torch.save(theta_0, output_modelname)
+
+    sd_models.list_models()
+
+    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]