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.gitignore

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+__pycache__/
+*$py.class
+.vscode/
+example
+input

LICENSE

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

__init__.py

@@ -0,0 +1,39 @@
+import sys
+import os
+
+repo_dir = os.path.dirname(os.path.realpath(__file__))
+sys.path.insert(0, repo_dir)
+original_modules = sys.modules.copy()
+
+# Place aside existing modules if using a1111 web ui
+modules_used = [
+    "modules",
+    "modules.images",
+    "modules.processing",
+    "modules.scripts_postprocessing",
+    "modules.scripts",
+    "modules.shared",
+]
+original_webui_modules = {}
+for module in modules_used:
+    if module in sys.modules:
+        original_webui_modules[module] = sys.modules.pop(module)
+
+# Proceed with node setup
+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
+
+# Clean up imports
+# Remove repo directory from path
+sys.path.remove(repo_dir)
+# Remove any new modules
+modules_to_remove = []
+for module in sys.modules:
+    if module not in original_modules and not module.startswith("google.protobuf") and not module.startswith("onnx") and not module.startswith("cv2"):
+        modules_to_remove.append(module)
+for module in modules_to_remove:
+    del sys.modules[module]
+
+# Restore original modules
+sys.modules.update(original_webui_modules)

install.bat

@@ -0,0 +1,37 @@
+@echo off
+setlocal enabledelayedexpansion
+
+:: Try to use embedded python first
+if exist ..\..\..\python_embeded\python.exe (
+    :: Use the embedded python
+    set PYTHON=..\..\..\python_embeded\python.exe
+) else (
+    :: Embedded python not found, check for python in the PATH
+    for /f "tokens=* USEBACKQ" %%F in (`python --version 2^>^&1`) do (
+        set PYTHON_VERSION=%%F
+    )
+    if errorlevel 1 (
+        echo I couldn't find an embedded version of Python, nor one in the Windows PATH. Please install manually.
+        pause
+        exit /b 1
+    ) else (
+        :: Use python from the PATH (if it's the right version and the user agrees)
+        echo I couldn't find an embedded version of Python, but I did find !PYTHON_VERSION! in your Windows PATH.
+        echo Would you like to proceed with the install using that version? (Y/N^)
+        set /p USE_PYTHON=
+        if /i "!USE_PYTHON!"=="Y" (
+            set PYTHON=python
+        ) else (
+            echo Okay. Please install manually.
+            pause
+            exit /b 1
+        )
+    )
+)
+
+:: Install the package
+echo Installing...
+%PYTHON% install.py
+echo Done^!
+
+@pause

install.py

@@ -0,0 +1,104 @@
+import warnings
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import subprocess
+import os, sys
+try:
+    from pkg_resources import get_distribution as distributions
+except:
+    from importlib_metadata import distributions
+from tqdm import tqdm
+import urllib.request
+from packaging import version as pv
+try:
+    from folder_paths import models_dir
+except:
+    from pathlib import Path
+    models_dir = os.path.join(Path(__file__).parents[2], "models")
+
+sys.path.append(os.path.dirname(os.path.realpath(__file__)))
+
+req_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), "requirements.txt")
+
+model_url = "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/inswapper_128.onnx"
+model_name = os.path.basename(model_url)
+models_dir_path = os.path.join(models_dir, "insightface")
+model_path = os.path.join(models_dir_path, model_name)
+
+def run_pip(*args):
+    subprocess.run([sys.executable, "-m", "pip", "install", "--no-warn-script-location", *args])
+
+def is_installed (
+        package: str, version: str = None, strict: bool = True
+):
+    has_package = None
+    try:
+        has_package = distributions(package)
+        if has_package is not None:
+            if version is not None:
+                installed_version = has_package.version
+                if (installed_version != version and strict == True) or (pv.parse(installed_version) < pv.parse(version) and strict == False):
+                    return False
+                else:
+                    return True
+            else:
+                return True
+        else:
+            return False
+    except Exception as e:
+        print(f"Status: {e}")
+        return False
+    
+def download(url, path, name):
+    request = urllib.request.urlopen(url)
+    total = int(request.headers.get('Content-Length', 0))
+    with tqdm(total=total, desc=f'[ReActor] Downloading {name} to {path}', unit='B', unit_scale=True, unit_divisor=1024) as progress:
+        urllib.request.urlretrieve(url, path, reporthook=lambda count, block_size, total_size: progress.update(block_size))
+
+if not os.path.exists(models_dir_path):
+    os.makedirs(models_dir_path)
+
+if not os.path.exists(model_path):
+    download(model_url, model_path, model_name)
+
+with open(req_file) as file:
+    try:
+        ort = "onnxruntime-gpu"
+        import torch
+        cuda_version = None
+        if torch.cuda.is_available():
+            cuda_version = torch.version.cuda
+            print(f"CUDA {cuda_version}")
+        elif torch.backends.mps.is_available() or hasattr(torch,'dml') or hasattr(torch,'privateuseone'):
+            ort = "onnxruntime"
+        if cuda_version is not None and float(cuda_version)>=12 and torch.torch_version.__version__ <= "2.2.0": # CU12.x and torch<=2.2.0
+            print(f"Torch: {torch.torch_version.__version__}")
+            if not is_installed(ort,"1.17.0",False):
+                run_pip(ort,"--extra-index-url", "https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/")
+        elif cuda_version is not None and float(cuda_version)>=12 and torch.torch_version.__version__ >= "2.4.0" : # CU12.x and latest torch
+            print(f"Torch: {torch.torch_version.__version__}")
+            if not is_installed(ort,"1.20.1",False): # latest ort-gpu
+                run_pip(ort,"-U")
+        elif not is_installed(ort,"1.16.1",False):
+            run_pip(ort, "-U")
+    except Exception as e:
+        print(e)
+        print(f"Warning: Failed to install {ort}, ReActor will not work.")
+        raise e
+    strict = True
+    for package in file:
+        package_version = None
+        try:
+            package = package.strip()
+            if "==" in package:
+                package_version = package.split('==')[1]
+            elif ">=" in package:
+                package_version = package.split('>=')[1]
+                strict = False
+            if not is_installed(package,package_version,strict):
+                run_pip(package)
+        except Exception as e:
+            print(e)
+            print(f"Warning: Failed to install {package}, ReActor will not work.")
+            raise e
+print("Ok")

nodes.py

@@ -0,0 +1,1237 @@
+import os, glob, sys
+import logging
+
+import torch
+import torch.nn.functional as torchfn
+from torchvision.transforms.functional import normalize
+from torchvision.ops import masks_to_boxes
+
+import numpy as np
+import cv2
+import math
+from typing import List
+from PIL import Image
+from scipy import stats
+from insightface.app.common import Face
+from segment_anything import sam_model_registry
+
+from modules.processing import StableDiffusionProcessingImg2Img
+from modules.shared import state
+# from comfy_extras.chainner_models import model_loading
+import comfy.model_management as model_management
+import comfy.utils
+import folder_paths
+
+import scripts.reactor_version
+from r_chainner import model_loading
+from scripts.reactor_faceswap import (
+    FaceSwapScript,
+    get_models,
+    get_current_faces_model,
+    analyze_faces,
+    half_det_size,
+    providers
+)
+from scripts.reactor_swapper import (
+    unload_all_models,
+)
+from scripts.reactor_logger import logger
+from reactor_utils import (
+    batch_tensor_to_pil,
+    batched_pil_to_tensor,
+    tensor_to_pil,
+    img2tensor,
+    tensor2img,
+    save_face_model,
+    load_face_model,
+    download,
+    set_ort_session,
+    prepare_cropped_face,
+    normalize_cropped_face,
+    add_folder_path_and_extensions,
+    rgba2rgb_tensor
+)
+from reactor_patcher import apply_patch
+from r_facelib.utils.face_restoration_helper import FaceRestoreHelper
+from r_basicsr.utils.registry import ARCH_REGISTRY
+import scripts.r_archs.codeformer_arch
+import scripts.r_masking.subcore as subcore
+import scripts.r_masking.core as core
+import scripts.r_masking.segs as masking_segs
+
+
+models_dir = folder_paths.models_dir
+REACTOR_MODELS_PATH = os.path.join(models_dir, "reactor")
+FACE_MODELS_PATH = os.path.join(REACTOR_MODELS_PATH, "faces")
+
+if not os.path.exists(REACTOR_MODELS_PATH):
+    os.makedirs(REACTOR_MODELS_PATH)
+    if not os.path.exists(FACE_MODELS_PATH):
+        os.makedirs(FACE_MODELS_PATH)
+
+dir_facerestore_models = os.path.join(models_dir, "facerestore_models")
+os.makedirs(dir_facerestore_models, exist_ok=True)
+folder_paths.folder_names_and_paths["facerestore_models"] = ([dir_facerestore_models], folder_paths.supported_pt_extensions)
+
+BLENDED_FACE_MODEL = None
+FACE_SIZE: int = 512
+FACE_HELPER = None
+
+if "ultralytics" not in folder_paths.folder_names_and_paths:
+    add_folder_path_and_extensions("ultralytics_bbox", [os.path.join(models_dir, "ultralytics", "bbox")], folder_paths.supported_pt_extensions)
+    add_folder_path_and_extensions("ultralytics_segm", [os.path.join(models_dir, "ultralytics", "segm")], folder_paths.supported_pt_extensions)
+    add_folder_path_and_extensions("ultralytics", [os.path.join(models_dir, "ultralytics")], folder_paths.supported_pt_extensions)
+if "sams" not in folder_paths.folder_names_and_paths:
+    add_folder_path_and_extensions("sams", [os.path.join(models_dir, "sams")], folder_paths.supported_pt_extensions)
+
+def get_facemodels():
+    models_path = os.path.join(FACE_MODELS_PATH, "*")
+    models = glob.glob(models_path)
+    models = [x for x in models if x.endswith(".safetensors")]
+    return models
+
+def get_restorers():
+    models_path = os.path.join(models_dir, "facerestore_models/*")
+    models = glob.glob(models_path)
+    models = [x for x in models if (x.endswith(".pth") or x.endswith(".onnx"))]
+    if len(models) == 0:
+        fr_urls = [
+            "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/facerestore_models/GFPGANv1.3.pth",
+            "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/facerestore_models/GFPGANv1.4.pth",
+            "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/facerestore_models/codeformer-v0.1.0.pth",
+            "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/facerestore_models/GPEN-BFR-512.onnx",
+            "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/facerestore_models/GPEN-BFR-1024.onnx",
+            "https://huggingface.co/datasets/Gourieff/ReActor/resolve/main/models/facerestore_models/GPEN-BFR-2048.onnx",
+        ]
+        for model_url in fr_urls:
+            model_name = os.path.basename(model_url)
+            model_path = os.path.join(dir_facerestore_models, model_name)
+            download(model_url, model_path, model_name)
+        models = glob.glob(models_path)
+        models = [x for x in models if (x.endswith(".pth") or x.endswith(".onnx"))]
+    return models
+
+def get_model_names(get_models):
+    models = get_models()
+    names = []
+    for x in models:
+        names.append(os.path.basename(x))
+    names.sort(key=str.lower)
+    names.insert(0, "none")
+    return names
+
+def model_names():
+    models = get_models()
+    return {os.path.basename(x): x for x in models}
+
+
+class reactor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "enabled": ("BOOLEAN", {"default": True, "label_off": "OFF", "label_on": "ON"}),
+                "input_image": ("IMAGE",),
+                "swap_model": (list(model_names().keys()),),
+                "facedetection": (["retinaface_resnet50", "retinaface_mobile0.25", "YOLOv5l", "YOLOv5n"],),
+                "face_restore_model": (get_model_names(get_restorers),),
+                "face_restore_visibility": ("FLOAT", {"default": 1, "min": 0.1, "max": 1, "step": 0.05}),
+                "codeformer_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1, "step": 0.05}),
+                "detect_gender_input": (["no","female","male"], {"default": "no"}),
+                "detect_gender_source": (["no","female","male"], {"default": "no"}),
+                "input_faces_index": ("STRING", {"default": "0"}),
+                "source_faces_index": ("STRING", {"default": "0"}),
+                "console_log_level": ([0, 1, 2], {"default": 1}),
+            },
+            "optional": {
+                "source_image": ("IMAGE",),
+                "face_model": ("FACE_MODEL",),
+                "face_boost": ("FACE_BOOST",),
+            },
+            "hidden": {"faces_order": "FACES_ORDER"},
+        }
+
+    RETURN_TYPES = ("IMAGE","FACE_MODEL")
+    FUNCTION = "execute"
+    CATEGORY = "🌌 ReActor"
+
+    def __init__(self):
+        # self.face_helper = None
+        self.faces_order = ["large-small", "large-small"]
+        # self.face_size = FACE_SIZE
+        self.face_boost_enabled = False
+        self.restore = True
+        self.boost_model = None
+        self.interpolation = "Bicubic"
+        self.boost_model_visibility = 1
+        self.boost_cf_weight = 0.5
+
+    def restore_face(
+            self,
+            input_image,
+            face_restore_model,
+            face_restore_visibility,
+            codeformer_weight,
+            facedetection,
+        ):
+
+        result = input_image
+
+        if face_restore_model != "none" and not model_management.processing_interrupted():
+
+            global FACE_SIZE, FACE_HELPER
+
+            self.face_helper = FACE_HELPER
+            
+            faceSize = 512
+            if "1024" in face_restore_model.lower():
+                faceSize = 1024
+            elif "2048" in face_restore_model.lower():
+                faceSize = 2048
+
+            logger.status(f"Restoring with {face_restore_model} | Face Size is set to {faceSize}")
+
+            model_path = folder_paths.get_full_path("facerestore_models", face_restore_model)
+
+            device = model_management.get_torch_device()
+
+            if "codeformer" in face_restore_model.lower():
+
+                codeformer_net = ARCH_REGISTRY.get("CodeFormer")(
+                    dim_embd=512,
+                    codebook_size=1024,
+                    n_head=8,
+                    n_layers=9,
+                    connect_list=["32", "64", "128", "256"],
+                ).to(device)
+                checkpoint = torch.load(model_path)["params_ema"]
+                codeformer_net.load_state_dict(checkpoint)
+                facerestore_model = codeformer_net.eval()
+
+            elif ".onnx" in face_restore_model:
+
+                ort_session = set_ort_session(model_path, providers=providers)
+                ort_session_inputs = {}
+                facerestore_model = ort_session
+
+            else:
+
+                sd = comfy.utils.load_torch_file(model_path, safe_load=True)
+                facerestore_model = model_loading.load_state_dict(sd).eval()
+                facerestore_model.to(device)
+            
+            if faceSize != FACE_SIZE or self.face_helper is None:
+                self.face_helper = FaceRestoreHelper(1, face_size=faceSize, crop_ratio=(1, 1), det_model=facedetection, save_ext='png', use_parse=True, device=device)
+                FACE_SIZE = faceSize
+                FACE_HELPER = self.face_helper
+
+            image_np = 255. * result.numpy()
+
+            total_images = image_np.shape[0]
+
+            out_images = []
+
+            for i in range(total_images):
+
+                if total_images > 1:
+                    logger.status(f"Restoring {i+1}")
+
+                cur_image_np = image_np[i,:, :, ::-1]
+
+                original_resolution = cur_image_np.shape[0:2]
+
+                if facerestore_model is None or self.face_helper is None:
+                    return result
+
+                self.face_helper.clean_all()
+                self.face_helper.read_image(cur_image_np)
+                self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
+                self.face_helper.align_warp_face()
+
+                restored_face = None
+
+                for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
+
+                    # if ".pth" in face_restore_model:
+                    cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+                    normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+                    cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
+
+                    try:
+
+                        with torch.no_grad():
+
+                            if ".onnx" in face_restore_model: # ONNX models
+
+                                for ort_session_input in ort_session.get_inputs():
+                                    if ort_session_input.name == "input":
+                                        cropped_face_prep = prepare_cropped_face(cropped_face)
+                                        ort_session_inputs[ort_session_input.name] = cropped_face_prep
+                                    if ort_session_input.name == "weight":
+                                        weight = np.array([ 1 ], dtype = np.double)
+                                        ort_session_inputs[ort_session_input.name] = weight
+
+                                output = ort_session.run(None, ort_session_inputs)[0][0]
+                                restored_face = normalize_cropped_face(output)
+
+                            else: # PTH models
+
+                                output = facerestore_model(cropped_face_t, w=codeformer_weight)[0] if "codeformer" in face_restore_model.lower() else facerestore_model(cropped_face_t)[0]
+                                restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
+
+                        del output
+                        torch.cuda.empty_cache()
+
+                    except Exception as error:
+
+                        print(f"\tFailed inference: {error}", file=sys.stderr)
+                        restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
+
+                    if face_restore_visibility < 1:
+                        restored_face = cropped_face * (1 - face_restore_visibility) + restored_face * face_restore_visibility
+
+                    restored_face = restored_face.astype("uint8")
+                    self.face_helper.add_restored_face(restored_face)
+
+                self.face_helper.get_inverse_affine(None)
+
+                restored_img = self.face_helper.paste_faces_to_input_image()
+                restored_img = restored_img[:, :, ::-1]
+
+                if original_resolution != restored_img.shape[0:2]:
+                    restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_AREA)
+
+                self.face_helper.clean_all()
+
+                # out_images[i] = restored_img
+                out_images.append(restored_img)
+
+                if state.interrupted or model_management.processing_interrupted():
+                    logger.status("Interrupted by User")
+                    return input_image
+
+            restored_img_np = np.array(out_images).astype(np.float32) / 255.0
+            restored_img_tensor = torch.from_numpy(restored_img_np)
+
+            result = restored_img_tensor
+
+        return result
+    
+    def execute(self, enabled, input_image, swap_model, detect_gender_source, detect_gender_input, source_faces_index, input_faces_index, console_log_level, face_restore_model,face_restore_visibility, codeformer_weight, facedetection, source_image=None, face_model=None, faces_order=None, face_boost=None):
+
+        if face_boost is not None:
+            self.face_boost_enabled = face_boost["enabled"]
+            self.boost_model = face_boost["boost_model"]
+            self.interpolation = face_boost["interpolation"]
+            self.boost_model_visibility = face_boost["visibility"]
+            self.boost_cf_weight = face_boost["codeformer_weight"]
+            self.restore = face_boost["restore_with_main_after"]
+        else:
+            self.face_boost_enabled = False
+
+        if faces_order is None:
+            faces_order = self.faces_order
+
+        apply_patch(console_log_level)
+
+        if not enabled:
+            return (input_image,face_model)
+        elif source_image is None and face_model is None:
+            logger.error("Please provide 'source_image' or `face_model`")
+            return (input_image,face_model)
+
+        if face_model == "none":
+            face_model = None
+        
+        script = FaceSwapScript()
+        pil_images = batch_tensor_to_pil(input_image)
+        if source_image is not None:
+            source = tensor_to_pil(source_image)
+        else:
+            source = None
+        p = StableDiffusionProcessingImg2Img(pil_images)
+        script.process(
+            p=p,
+            img=source,
+            enable=True,
+            source_faces_index=source_faces_index,
+            faces_index=input_faces_index,
+            model=swap_model,
+            swap_in_source=True,
+            swap_in_generated=True,
+            gender_source=detect_gender_source,
+            gender_target=detect_gender_input,
+            face_model=face_model,
+            faces_order=faces_order,
+            # face boost:
+            face_boost_enabled=self.face_boost_enabled,
+            face_restore_model=self.boost_model,
+            face_restore_visibility=self.boost_model_visibility,
+            codeformer_weight=self.boost_cf_weight,
+            interpolation=self.interpolation,
+        )
+        result = batched_pil_to_tensor(p.init_images)
+
+        if face_model is None:
+            current_face_model = get_current_faces_model()
+            face_model_to_provide = current_face_model[0] if (current_face_model is not None and len(current_face_model) > 0) else face_model
+        else:
+            face_model_to_provide = face_model
+
+        if self.restore or not self.face_boost_enabled:
+            result = reactor.restore_face(self,result,face_restore_model,face_restore_visibility,codeformer_weight,facedetection)
+
+        return (result,face_model_to_provide)
+
+
+class ReActorPlusOpt:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "enabled": ("BOOLEAN", {"default": True, "label_off": "OFF", "label_on": "ON"}),
+                "input_image": ("IMAGE",),               
+                "swap_model": (list(model_names().keys()),),
+                "facedetection": (["retinaface_resnet50", "retinaface_mobile0.25", "YOLOv5l", "YOLOv5n"],),
+                "face_restore_model": (get_model_names(get_restorers),),
+                "face_restore_visibility": ("FLOAT", {"default": 1, "min": 0.1, "max": 1, "step": 0.05}),
+                "codeformer_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1, "step": 0.05}),
+            },
+            "optional": {
+                "source_image": ("IMAGE",),
+                "face_model": ("FACE_MODEL",),
+                "options": ("OPTIONS",),
+                "face_boost": ("FACE_BOOST",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE","FACE_MODEL")
+    FUNCTION = "execute"
+    CATEGORY = "🌌 ReActor"
+
+    def __init__(self):
+        # self.face_helper = None
+        self.faces_order = ["large-small", "large-small"]
+        self.detect_gender_input = "no"
+        self.detect_gender_source = "no"
+        self.input_faces_index = "0"
+        self.source_faces_index = "0"
+        self.console_log_level = 1
+        # self.face_size = 512
+        self.face_boost_enabled = False
+        self.restore = True
+        self.boost_model = None
+        self.interpolation = "Bicubic"
+        self.boost_model_visibility = 1
+        self.boost_cf_weight = 0.5
+    
+    def execute(self, enabled, input_image, swap_model, facedetection, face_restore_model, face_restore_visibility, codeformer_weight, source_image=None, face_model=None, options=None, face_boost=None):
+
+        if options is not None:
+            self.faces_order = [options["input_faces_order"], options["source_faces_order"]]
+            self.console_log_level = options["console_log_level"]
+            self.detect_gender_input = options["detect_gender_input"]
+            self.detect_gender_source = options["detect_gender_source"]
+            self.input_faces_index = options["input_faces_index"]
+            self.source_faces_index = options["source_faces_index"]
+        
+        if face_boost is not None:
+            self.face_boost_enabled = face_boost["enabled"]
+            self.restore = face_boost["restore_with_main_after"]
+        else:
+            self.face_boost_enabled = False
+        
+        result = reactor.execute(
+            self,enabled,input_image,swap_model,self.detect_gender_source,self.detect_gender_input,self.source_faces_index,self.input_faces_index,self.console_log_level,face_restore_model,face_restore_visibility,codeformer_weight,facedetection,source_image,face_model,self.faces_order, face_boost=face_boost
+        )
+
+        return result
+
+
+class LoadFaceModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "face_model": (get_model_names(get_facemodels),),
+            }
+        }
+    
+    RETURN_TYPES = ("FACE_MODEL",)
+    FUNCTION = "load_model"
+    CATEGORY = "🌌 ReActor"
+
+    def load_model(self, face_model):
+        self.face_model = face_model
+        self.face_models_path = FACE_MODELS_PATH
+        if self.face_model != "none":
+            face_model_path = os.path.join(self.face_models_path, self.face_model)
+            out = load_face_model(face_model_path)
+        else:
+            out = None
+        return (out, )
+
+
+class BuildFaceModel:
+    def __init__(self):
+        self.output_dir = FACE_MODELS_PATH
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "save_mode": ("BOOLEAN", {"default": True, "label_off": "OFF", "label_on": "ON"}),
+                "send_only": ("BOOLEAN", {"default": False, "label_off": "NO", "label_on": "YES"}),
+                "face_model_name": ("STRING", {"default": "default"}),
+                "compute_method": (["Mean", "Median", "Mode"], {"default": "Mean"}),
+            },
+            "optional": {
+                "images": ("IMAGE",),
+                "face_models": ("FACE_MODEL",),
+            }
+        }
+
+    RETURN_TYPES = ("FACE_MODEL",)
+    FUNCTION = "blend_faces"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "🌌 ReActor"
+
+    def build_face_model(self, image: Image.Image, det_size=(640, 640)):
+        logging.StreamHandler.terminator = "\n"
+        if image is None:
+            error_msg = "Please load an Image"
+            logger.error(error_msg)
+            return error_msg
+        image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+        face_model = analyze_faces(image, det_size)
+
+        if len(face_model) == 0:
+            print("")
+            det_size_half = half_det_size(det_size)
+            face_model = analyze_faces(image, det_size_half)
+            if face_model is not None and len(face_model) > 0:
+                print("...........................................................", end=" ")
+        
+        if face_model is not None and len(face_model) > 0:
+            return face_model[0]
+        else:
+            no_face_msg = "No face found, please try another image"
+            # logger.error(no_face_msg)
+            return no_face_msg
+    
+    def blend_faces(self, save_mode, send_only, face_model_name, compute_method, images=None, face_models=None):
+        global BLENDED_FACE_MODEL
+        blended_face: Face = BLENDED_FACE_MODEL
+
+        if send_only and blended_face is None:
+            send_only = False
+
+        if (images is not None or face_models is not None) and not send_only:
+
+            faces = []
+            embeddings = []
+
+            apply_patch(1)
+
+            if images is not None:
+                images_list: List[Image.Image] = batch_tensor_to_pil(images)
+
+                n = len(images_list)
+
+                for i,image in enumerate(images_list):
+                    logging.StreamHandler.terminator = " "
+                    logger.status(f"Building Face Model {i+1} of {n}...")
+                    face = self.build_face_model(image)
+                    if isinstance(face, str):
+                        logger.error(f"No faces found in image {i+1}, skipping")
+                        continue
+                    else:
+                        print(f"{int(((i+1)/n)*100)}%")
+                    faces.append(face)
+                    embeddings.append(face.embedding)
+            
+            elif face_models is not None:
+
+                n = len(face_models)
+
+                for i,face_model in enumerate(face_models):
+                    logging.StreamHandler.terminator = " "
+                    logger.status(f"Extracting Face Model {i+1} of {n}...")
+                    face = face_model
+                    if isinstance(face, str):
+                        logger.error(f"No faces found for face_model {i+1}, skipping")
+                        continue
+                    else:
+                        print(f"{int(((i+1)/n)*100)}%")
+                    faces.append(face)
+                    embeddings.append(face.embedding)
+
+            logging.StreamHandler.terminator = "\n"
+            if len(faces) > 0:
+                # compute_method_name = "Mean" if compute_method == 0 else "Median" if compute_method == 1 else "Mode"
+                logger.status(f"Blending with Compute Method '{compute_method}'...")
+                blended_embedding = np.mean(embeddings, axis=0) if compute_method == "Mean" else np.median(embeddings, axis=0) if compute_method == "Median" else stats.mode(embeddings, axis=0)[0].astype(np.float32)
+                blended_face = Face(
+                    bbox=faces[0].bbox,
+                    kps=faces[0].kps,
+                    det_score=faces[0].det_score,
+                    landmark_3d_68=faces[0].landmark_3d_68,
+                    pose=faces[0].pose,
+                    landmark_2d_106=faces[0].landmark_2d_106,
+                    embedding=blended_embedding,
+                    gender=faces[0].gender,
+                    age=faces[0].age
+                )
+                if blended_face is not None:
+                    BLENDED_FACE_MODEL = blended_face
+                    if save_mode:
+                        face_model_path = os.path.join(FACE_MODELS_PATH, face_model_name + ".safetensors")
+                        save_face_model(blended_face,face_model_path)
+                        # done_msg = f"Face model has been saved to '{face_model_path}'"
+                        # logger.status(done_msg)
+                    logger.status("--Done!--")
+                    # return (blended_face,)
+                else:
+                    no_face_msg = "Something went wrong, please try another set of images"
+                    logger.error(no_face_msg)
+                    # return (blended_face,)
+            # logger.status("--Done!--")
+        if images is None and face_models is None:
+            logger.error("Please provide `images` or `face_models`")
+        return (blended_face,)
+
+
+class SaveFaceModel:
+    def __init__(self):
+        self.output_dir = FACE_MODELS_PATH
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "save_mode": ("BOOLEAN", {"default": True, "label_off": "OFF", "label_on": "ON"}),
+                "face_model_name": ("STRING", {"default": "default"}),
+                "select_face_index": ("INT", {"default": 0, "min": 0}),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+                "face_model": ("FACE_MODEL",),
+            }
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_model"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "🌌 ReActor"
+
+    def save_model(self, save_mode, face_model_name, select_face_index, image=None, face_model=None, det_size=(640, 640)):
+        if save_mode and image is not None:
+            source = tensor_to_pil(image)
+            source = cv2.cvtColor(np.array(source), cv2.COLOR_RGB2BGR)
+            apply_patch(1)
+            logger.status("Building Face Model...")
+            face_model_raw = analyze_faces(source, det_size)
+            if len(face_model_raw) == 0:
+                det_size_half = half_det_size(det_size)
+                face_model_raw = analyze_faces(source, det_size_half)
+            try:
+                face_model = face_model_raw[select_face_index]
+            except:
+                logger.error("No face(s) found")
+                return face_model_name
+            logger.status("--Done!--")
+        if save_mode and (face_model != "none" or face_model is not None):
+            face_model_path = os.path.join(self.output_dir, face_model_name + ".safetensors")
+            save_face_model(face_model,face_model_path)
+        if image is None and face_model is None:
+            logger.error("Please provide `face_model` or `image`")
+        return face_model_name
+
+
+class RestoreFace:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),               
+                "facedetection": (["retinaface_resnet50", "retinaface_mobile0.25", "YOLOv5l", "YOLOv5n"],),
+                "model": (get_model_names(get_restorers),),
+                "visibility": ("FLOAT", {"default": 1, "min": 0.0, "max": 1, "step": 0.05}),
+                "codeformer_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1, "step": 0.05}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "🌌 ReActor"
+
+    # def __init__(self):
+    #     self.face_helper = None
+    #     self.face_size = 512
+
+    def execute(self, image, model, visibility, codeformer_weight, facedetection):
+        result = reactor.restore_face(self,image,model,visibility,codeformer_weight,facedetection)
+        return (result,)
+
+
+class MaskHelper:
+    def __init__(self):
+        # self.threshold = 0.5
+        # self.dilation = 10
+        # self.crop_factor = 3.0
+        # self.drop_size = 1
+        self.labels = "all"
+        self.detailer_hook = None
+        self.device_mode = "AUTO"
+        self.detection_hint = "center-1"
+        # self.sam_dilation = 0
+        # self.sam_threshold = 0.93
+        # self.bbox_expansion = 0
+        # self.mask_hint_threshold = 0.7
+        # self.mask_hint_use_negative = "False"
+        # self.force_resize_width = 0
+        # self.force_resize_height = 0
+        # self.resize_behavior = "source_size"
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        bboxs = ["bbox/"+x for x in folder_paths.get_filename_list("ultralytics_bbox")]
+        segms = ["segm/"+x for x in folder_paths.get_filename_list("ultralytics_segm")]
+        sam_models = [x for x in folder_paths.get_filename_list("sams") if 'hq' not in x]
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "swapped_image": ("IMAGE",),
+                "bbox_model_name": (bboxs + segms, ),
+                "bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "bbox_dilation": ("INT", {"default": 10, "min": -512, "max": 512, "step": 1}),
+                "bbox_crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
+                "bbox_drop_size": ("INT", {"min": 1, "max": 8192, "step": 1, "default": 10}),
+                "sam_model_name": (sam_models, ),
+                "sam_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
+                "sam_threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
+                "mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "mask_hint_use_negative": (["False", "Small", "Outter"], ),
+                "morphology_operation": (["dilate", "erode", "open", "close"],),
+                "morphology_distance": ("INT", {"default": 0, "min": 0, "max": 128, "step": 1}),
+                "blur_radius": ("INT", {"default": 9, "min": 0, "max": 48, "step": 1}),
+                "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 3., "step": 0.01}),
+            },
+            "optional": {
+                "mask_optional": ("MASK",),
+            }
+        }
+    
+    RETURN_TYPES = ("IMAGE","MASK","IMAGE","IMAGE")
+    RETURN_NAMES = ("IMAGE","MASK","MASK_PREVIEW","SWAPPED_FACE")
+    FUNCTION = "execute"
+    CATEGORY = "🌌 ReActor"
+
+    def execute(self, image, swapped_image, bbox_model_name, bbox_threshold, bbox_dilation, bbox_crop_factor, bbox_drop_size, sam_model_name, sam_dilation, sam_threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative, morphology_operation, morphology_distance, blur_radius, sigma_factor, mask_optional=None):
+
+        # images = [image[i:i + 1, ...] for i in range(image.shape[0])]
+
+        images = image
+
+        if mask_optional is None:
+
+            bbox_model_path = folder_paths.get_full_path("ultralytics", bbox_model_name)
+            bbox_model = subcore.load_yolo(bbox_model_path)
+            bbox_detector = subcore.UltraBBoxDetector(bbox_model)
+
+            segs = bbox_detector.detect(images, bbox_threshold, bbox_dilation, bbox_crop_factor, bbox_drop_size, self.detailer_hook)
+
+            if isinstance(self.labels, list):
+                self.labels = str(self.labels[0])
+
+            if self.labels is not None and self.labels != '':
+                self.labels = self.labels.split(',')
+                if len(self.labels) > 0:
+                    segs, _ = masking_segs.filter(segs, self.labels)
+            # segs, _ = masking_segs.filter(segs, "all")
+            
+            sam_modelname = folder_paths.get_full_path("sams", sam_model_name)
+
+            if 'vit_h' in sam_model_name:
+                model_kind = 'vit_h'
+            elif 'vit_l' in sam_model_name:
+                model_kind = 'vit_l'
+            else:
+                model_kind = 'vit_b'
+
+            sam = sam_model_registry[model_kind](checkpoint=sam_modelname)
+            size = os.path.getsize(sam_modelname)
+            sam.safe_to = core.SafeToGPU(size)
+
+            device = model_management.get_torch_device()
+
+            sam.safe_to.to_device(sam, device)
+
+            sam.is_auto_mode = self.device_mode == "AUTO"
+
+            combined_mask, _ = core.make_sam_mask_segmented(sam, segs, images, self.detection_hint, sam_dilation, sam_threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative)
+        
+        else:
+            combined_mask = mask_optional
+
+        # *** MASK TO IMAGE ***:
+        
+        mask_image = combined_mask.reshape((-1, 1, combined_mask.shape[-2], combined_mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
+
+        # *** MASK MORPH ***:
+
+        mask_image = core.tensor2mask(mask_image)
+
+        if morphology_operation == "dilate":
+            mask_image = self.dilate(mask_image, morphology_distance)
+        elif morphology_operation == "erode":
+            mask_image = self.erode(mask_image, morphology_distance)
+        elif morphology_operation == "open":
+            mask_image = self.erode(mask_image, morphology_distance)
+            mask_image = self.dilate(mask_image, morphology_distance)
+        elif morphology_operation == "close":
+            mask_image = self.dilate(mask_image, morphology_distance)
+            mask_image = self.erode(mask_image, morphology_distance)
+        
+        # *** MASK BLUR ***:
+        
+        if len(mask_image.size()) == 3:
+            mask_image = mask_image.unsqueeze(3)
+        
+        mask_image = mask_image.permute(0, 3, 1, 2)
+        kernel_size = blur_radius * 2 + 1
+        sigma = sigma_factor * (0.6 * blur_radius - 0.3)
+        mask_image_final = self.gaussian_blur(mask_image, kernel_size, sigma).permute(0, 2, 3, 1)
+        if mask_image_final.size()[3] == 1:
+            mask_image_final = mask_image_final[:, :, :, 0]
+
+        # *** CUT BY MASK ***:
+        
+        if len(swapped_image.shape) < 4:
+            C = 1
+        else:
+            C = swapped_image.shape[3]
+
+        # We operate on RGBA to keep the code clean and then convert back after
+        swapped_image = core.tensor2rgba(swapped_image)
+        mask = core.tensor2mask(mask_image_final)
+
+        # Scale the mask to be a matching size if it isn't
+        B, H, W, _ = swapped_image.shape
+        mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(H, W), mode='nearest')[:,0,:,:]
+        MB, _, _ = mask.shape
+
+        if MB < B:
+            assert(B % MB == 0)
+            mask = mask.repeat(B // MB, 1, 1)
+
+        # masks_to_boxes errors if the tensor is all zeros, so we'll add a single pixel and zero it out at the end
+        is_empty = ~torch.gt(torch.max(torch.reshape(mask,[MB, H * W]), dim=1).values, 0.)
+        mask[is_empty,0,0] = 1.
+        boxes = masks_to_boxes(mask)
+        mask[is_empty,0,0] = 0.
+
+        min_x = boxes[:,0]
+        min_y = boxes[:,1]
+        max_x = boxes[:,2]
+        max_y = boxes[:,3]
+
+        width = max_x - min_x + 1
+        height = max_y - min_y + 1
+
+        use_width = int(torch.max(width).item())
+        use_height = int(torch.max(height).item())
+
+        # if self.force_resize_width > 0:
+        #     use_width = self.force_resize_width
+
+        # if self.force_resize_height > 0:
+        #     use_height = self.force_resize_height
+
+        alpha_mask = torch.ones((B, H, W, 4))
+        alpha_mask[:,:,:,3] = mask
+
+        swapped_image = swapped_image * alpha_mask
+
+        cutted_image = torch.zeros((B, use_height, use_width, 4))
+        for i in range(0, B):
+            if not is_empty[i]:
+                ymin = int(min_y[i].item())
+                ymax = int(max_y[i].item())
+                xmin = int(min_x[i].item())
+                xmax = int(max_x[i].item())
+                single = (swapped_image[i, ymin:ymax+1, xmin:xmax+1,:]).unsqueeze(0)
+                resized = torch.nn.functional.interpolate(single.permute(0, 3, 1, 2), size=(use_height, use_width), mode='bicubic').permute(0, 2, 3, 1)
+                cutted_image[i] = resized[0]
+        
+        # Preserve our type unless we were previously RGB and added non-opaque alpha due to the mask size
+        if C == 1:
+            cutted_image = core.tensor2mask(cutted_image)
+        elif C == 3 and torch.min(cutted_image[:,:,:,3]) == 1:
+            cutted_image = core.tensor2rgb(cutted_image)
+
+        # *** PASTE BY MASK ***:
+
+        image_base = core.tensor2rgba(images)
+        image_to_paste = core.tensor2rgba(cutted_image)
+        mask = core.tensor2mask(mask_image_final)
+
+        # Scale the mask to be a matching size if it isn't
+        B, H, W, C = image_base.shape
+        MB = mask.shape[0]
+        PB = image_to_paste.shape[0]
+
+        if B < PB:
+            assert(PB % B == 0)
+            image_base = image_base.repeat(PB // B, 1, 1, 1)
+        B, H, W, C = image_base.shape
+        if MB < B:
+            assert(B % MB == 0)
+            mask = mask.repeat(B // MB, 1, 1)
+        elif B < MB:
+            assert(MB % B == 0)
+            image_base = image_base.repeat(MB // B, 1, 1, 1)
+        if PB < B:
+            assert(B % PB == 0)
+            image_to_paste = image_to_paste.repeat(B // PB, 1, 1, 1)
+
+        mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(H, W), mode='nearest')[:,0,:,:]
+        MB, MH, MW = mask.shape
+
+        # masks_to_boxes errors if the tensor is all zeros, so we'll add a single pixel and zero it out at the end
+        is_empty = ~torch.gt(torch.max(torch.reshape(mask,[MB, MH * MW]), dim=1).values, 0.)
+        mask[is_empty,0,0] = 1.
+        boxes = masks_to_boxes(mask)
+        mask[is_empty,0,0] = 0.
+
+        min_x = boxes[:,0]
+        min_y = boxes[:,1]
+        max_x = boxes[:,2]
+        max_y = boxes[:,3]
+        mid_x = (min_x + max_x) / 2
+        mid_y = (min_y + max_y) / 2
+
+        target_width = max_x - min_x + 1
+        target_height = max_y - min_y + 1
+
+        result = image_base.detach().clone()
+        face_segment = mask_image_final
+        
+        for i in range(0, MB):
+            if is_empty[i]:
+                continue
+            else:
+                image_index = i
+                source_size = image_to_paste.size()
+                SB, SH, SW, _ = image_to_paste.shape
+
+                # Figure out the desired size
+                width = int(target_width[i].item())
+                height = int(target_height[i].item())
+                # if self.resize_behavior == "keep_ratio_fill":
+                #     target_ratio = width / height
+                #     actual_ratio = SW / SH
+                #     if actual_ratio > target_ratio:
+                #         width = int(height * actual_ratio)
+                #     elif actual_ratio < target_ratio:
+                #         height = int(width / actual_ratio)
+                # elif self.resize_behavior == "keep_ratio_fit":
+                #     target_ratio = width / height
+                #     actual_ratio = SW / SH
+                #     if actual_ratio > target_ratio:
+                #         height = int(width / actual_ratio)
+                #     elif actual_ratio < target_ratio:
+                #         width = int(height * actual_ratio)
+                # elif self.resize_behavior == "source_size" or self.resize_behavior == "source_size_unmasked":
+
+                width = SW
+                height = SH
+
+                # Resize the image we're pasting if needed
+                resized_image = image_to_paste[i].unsqueeze(0)
+                # if SH != height or SW != width:
+                #     resized_image = torch.nn.functional.interpolate(resized_image.permute(0, 3, 1, 2), size=(height,width), mode='bicubic').permute(0, 2, 3, 1)
+
+                pasting = torch.ones([H, W, C])
+                ymid = float(mid_y[i].item())
+                ymin = int(math.floor(ymid - height / 2)) + 1
+                ymax = int(math.floor(ymid + height / 2)) + 1
+                xmid = float(mid_x[i].item())
+                xmin = int(math.floor(xmid - width / 2)) + 1
+                xmax = int(math.floor(xmid + width / 2)) + 1
+
+                _, source_ymax, source_xmax, _ = resized_image.shape
+                source_ymin, source_xmin = 0, 0
+
+                if xmin < 0:
+                    source_xmin = abs(xmin)
+                    xmin = 0
+                if ymin < 0:
+                    source_ymin = abs(ymin)
+                    ymin = 0
+                if xmax > W:
+                    source_xmax -= (xmax - W)
+                    xmax = W
+                if ymax > H:
+                    source_ymax -= (ymax - H)
+                    ymax = H
+
+                pasting[ymin:ymax, xmin:xmax, :] = resized_image[0, source_ymin:source_ymax, source_xmin:source_xmax, :]
+                pasting[:, :, 3] = 1.
+
+                pasting_alpha = torch.zeros([H, W])
+                pasting_alpha[ymin:ymax, xmin:xmax] = resized_image[0, source_ymin:source_ymax, source_xmin:source_xmax, 3]
+
+                # if self.resize_behavior == "keep_ratio_fill" or self.resize_behavior == "source_size_unmasked":
+                #     # If we explicitly want to fill the area, we are ok with extending outside
+                #     paste_mask = pasting_alpha.unsqueeze(2).repeat(1, 1, 4)
+                # else:
+                #     paste_mask = torch.min(pasting_alpha, mask[i]).unsqueeze(2).repeat(1, 1, 4)
+                paste_mask = torch.min(pasting_alpha, mask[i]).unsqueeze(2).repeat(1, 1, 4)
+                result[image_index] = pasting * paste_mask + result[image_index] * (1. - paste_mask)
+
+                face_segment = result
+
+                face_segment[...,3] = mask[i]
+
+                result = rgba2rgb_tensor(result)
+        
+        return (result,combined_mask,mask_image_final,face_segment,)
+
+    def gaussian_blur(self, image, kernel_size, sigma):
+        kernel = torch.Tensor(kernel_size, kernel_size).to(device=image.device)
+        center = kernel_size // 2
+        variance = sigma**2
+        for i in range(kernel_size):
+            for j in range(kernel_size):
+                x = i - center
+                y = j - center
+                kernel[i, j] = math.exp(-(x**2 + y**2)/(2*variance))
+        kernel /= kernel.sum()
+
+        # Pad the input tensor
+        padding = (kernel_size - 1) // 2
+        input_pad = torch.nn.functional.pad(image, (padding, padding, padding, padding), mode='reflect')
+
+        # Reshape the padded input tensor for batched convolution
+        batch_size, num_channels, height, width = image.shape
+        input_reshaped = input_pad.reshape(batch_size*num_channels, 1, height+padding*2, width+padding*2)
+
+        # Perform batched convolution with the Gaussian kernel
+        output_reshaped = torch.nn.functional.conv2d(input_reshaped, kernel.unsqueeze(0).unsqueeze(0))
+
+        # Reshape the output tensor to its original shape
+        output_tensor = output_reshaped.reshape(batch_size, num_channels, height, width)
+
+        return output_tensor
+    
+    def erode(self, image, distance):
+        return 1. - self.dilate(1. - image, distance)
+
+    def dilate(self, image, distance):
+        kernel_size = 1 + distance * 2
+        # Add the channels dimension
+        image = image.unsqueeze(1)
+        out = torchfn.max_pool2d(image, kernel_size=kernel_size, stride=1, padding=kernel_size // 2).squeeze(1)
+        return out
+
+
+class ImageDublicator:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),               
+                "count": ("INT", {"default": 1, "min": 0}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("IMAGES",)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "execute"
+    CATEGORY = "🌌 ReActor"
+
+    def execute(self, image, count):
+        images = [image for i in range(count)]        
+        return (images,)
+
+
+class ImageRGBA2RGB:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "🌌 ReActor"
+
+    def execute(self, image):
+        out = rgba2rgb_tensor(image)       
+        return (out,)
+
+
+class MakeFaceModelBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "face_model1": ("FACE_MODEL",), 
+            },
+            "optional": {
+                "face_model2": ("FACE_MODEL",),
+                "face_model3": ("FACE_MODEL",),
+                "face_model4": ("FACE_MODEL",),
+                "face_model5": ("FACE_MODEL",),
+                "face_model6": ("FACE_MODEL",),
+                "face_model7": ("FACE_MODEL",),
+                "face_model8": ("FACE_MODEL",),
+                "face_model9": ("FACE_MODEL",),
+                "face_model10": ("FACE_MODEL",),
+            },
+        }
+
+    RETURN_TYPES = ("FACE_MODEL",)
+    RETURN_NAMES = ("FACE_MODELS",)
+    FUNCTION = "execute"
+
+    CATEGORY = "🌌 ReActor"
+
+    def execute(self, **kwargs):
+        if len(kwargs) > 0:
+            face_models = [value for value in kwargs.values()]
+            return (face_models,)
+        else:
+            logger.error("Please provide at least 1 `face_model`")
+            return (None,)
+
+
+class ReActorOptions:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_faces_order": (
+                    ["left-right","right-left","top-bottom","bottom-top","small-large","large-small"], {"default": "large-small"}
+                ),
+                "input_faces_index": ("STRING", {"default": "0"}),
+                "detect_gender_input": (["no","female","male"], {"default": "no"}),
+                "source_faces_order": (
+                    ["left-right","right-left","top-bottom","bottom-top","small-large","large-small"], {"default": "large-small"}
+                ),
+                "source_faces_index": ("STRING", {"default": "0"}),
+                "detect_gender_source": (["no","female","male"], {"default": "no"}),
+                "console_log_level": ([0, 1, 2], {"default": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("OPTIONS",)
+    FUNCTION = "execute"
+    CATEGORY = "🌌 ReActor"
+
+    def execute(self,input_faces_order, input_faces_index, detect_gender_input, source_faces_order, source_faces_index, detect_gender_source, console_log_level):
+        options: dict = {
+            "input_faces_order": input_faces_order,
+            "input_faces_index": input_faces_index,
+            "detect_gender_input": detect_gender_input,
+            "source_faces_order": source_faces_order,
+            "source_faces_index": source_faces_index,
+            "detect_gender_source": detect_gender_source,
+            "console_log_level": console_log_level,
+        }
+        return (options, )
+
+
+class ReActorFaceBoost:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "enabled": ("BOOLEAN", {"default": True, "label_off": "OFF", "label_on": "ON"}),
+                "boost_model": (get_model_names(get_restorers),),
+                "interpolation": (["Nearest","Bilinear","Bicubic","Lanczos"], {"default": "Bicubic"}),
+                "visibility": ("FLOAT", {"default": 1, "min": 0.1, "max": 1, "step": 0.05}),
+                "codeformer_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1, "step": 0.05}),
+                "restore_with_main_after": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    RETURN_TYPES = ("FACE_BOOST",)
+    FUNCTION = "execute"
+    CATEGORY = "🌌 ReActor"
+
+    def execute(self,enabled,boost_model,interpolation,visibility,codeformer_weight,restore_with_main_after):
+        face_boost: dict = {
+            "enabled": enabled,
+            "boost_model": boost_model,
+            "interpolation": interpolation,
+            "visibility": visibility,
+            "codeformer_weight": codeformer_weight,
+            "restore_with_main_after": restore_with_main_after,
+        }
+        return (face_boost, )
+    
+class ReActorUnload:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "trigger": ("IMAGE", ),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "🌌 ReActor"
+
+    def execute(self, trigger):
+        unload_all_models()
+        return (trigger,)
+
+
+NODE_CLASS_MAPPINGS = {
+    # --- MAIN NODES ---
+    "ReActorFaceSwap": reactor,
+    "ReActorFaceSwapOpt": ReActorPlusOpt,
+    "ReActorOptions": ReActorOptions,
+    "ReActorFaceBoost": ReActorFaceBoost,
+    "ReActorMaskHelper": MaskHelper,
+    # --- Operations with Face Models ---
+    "ReActorSaveFaceModel": SaveFaceModel,
+    "ReActorLoadFaceModel": LoadFaceModel,
+    "ReActorBuildFaceModel": BuildFaceModel,
+    "ReActorMakeFaceModelBatch": MakeFaceModelBatch,
+    # --- Additional Nodes ---
+    "ReActorRestoreFace": RestoreFace,
+    "ReActorImageDublicator": ImageDublicator,
+    "ImageRGBA2RGB": ImageRGBA2RGB,
+    "ReActorUnload": ReActorUnload,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    # --- MAIN NODES ---
+    "ReActorFaceSwap": "ReActor 🌌 Fast Face Swap",
+    "ReActorFaceSwapOpt": "ReActor 🌌 Fast Face Swap [OPTIONS]",
+    "ReActorOptions": "ReActor 🌌 Options",
+    "ReActorFaceBoost": "ReActor 🌌 Face Booster",
+    "ReActorMaskHelper": "ReActor 🌌 Masking Helper",
+    # --- Operations with Face Models ---
+    "ReActorSaveFaceModel": "Save Face Model 🌌 ReActor",
+    "ReActorLoadFaceModel": "Load Face Model 🌌 ReActor",
+    "ReActorBuildFaceModel": "Build Blended Face Model 🌌 ReActor",
+    "ReActorMakeFaceModelBatch": "Make Face Model Batch 🌌 ReActor",
+    # --- Additional Nodes ---
+    "ReActorRestoreFace": "Restore Face 🌌 ReActor",
+    "ReActorImageDublicator": "Image Dublicator (List) 🌌 ReActor",
+    "ImageRGBA2RGB": "Convert RGBA to RGB 🌌 ReActor",
+    "ReActorUnload": "Unload ReActor Models 🌌 ReActor",
+}

reactor_utils.py

@@ -0,0 +1,231 @@
+import os
+from PIL import Image
+import numpy as np
+import torch
+from torchvision.utils import make_grid
+import cv2
+import math
+import logging
+import hashlib
+from insightface.app.common import Face
+from safetensors.torch import save_file, safe_open
+from tqdm import tqdm
+import urllib.request
+import onnxruntime
+from typing import Any
+import folder_paths
+
+ORT_SESSION = None
+
+def tensor_to_pil(img_tensor, batch_index=0):
+    # Convert tensor of shape [batch_size, channels, height, width] at the batch_index to PIL Image
+    img_tensor = img_tensor[batch_index].unsqueeze(0)
+    i = 255. * img_tensor.cpu().numpy()
+    img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8).squeeze())
+    return img
+
+
+def batch_tensor_to_pil(img_tensor):
+    # Convert tensor of shape [batch_size, channels, height, width] to a list of PIL Images
+    return [tensor_to_pil(img_tensor, i) for i in range(img_tensor.shape[0])]
+
+
+def pil_to_tensor(image):
+    # Takes a PIL image and returns a tensor of shape [1, height, width, channels]
+    image = np.array(image).astype(np.float32) / 255.0
+    image = torch.from_numpy(image).unsqueeze(0)
+    if len(image.shape) == 3:  # If the image is grayscale, add a channel dimension
+        image = image.unsqueeze(-1)
+    return image
+
+
+def batched_pil_to_tensor(images):
+    # Takes a list of PIL images and returns a tensor of shape [batch_size, height, width, channels]
+    return torch.cat([pil_to_tensor(image) for image in images], dim=0)
+
+
+def img2tensor(imgs, bgr2rgb=True, float32=True):
+
+    def _totensor(img, bgr2rgb, float32):
+        if img.shape[2] == 3 and bgr2rgb:
+            if img.dtype == 'float64':
+                img = img.astype('float32')
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = torch.from_numpy(img.transpose(2, 0, 1))
+        if float32:
+            img = img.float()
+        return img
+
+    if isinstance(imgs, list):
+        return [_totensor(img, bgr2rgb, float32) for img in imgs]
+    else:
+        return _totensor(imgs, bgr2rgb, float32)
+
+
+def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
+
+    if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
+        raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
+
+    if torch.is_tensor(tensor):
+        tensor = [tensor]
+    result = []
+    for _tensor in tensor:
+        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
+        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
+
+        n_dim = _tensor.dim()
+        if n_dim == 4:
+            img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
+            img_np = img_np.transpose(1, 2, 0)
+            if rgb2bgr:
+                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+        elif n_dim == 3:
+            img_np = _tensor.numpy()
+            img_np = img_np.transpose(1, 2, 0)
+            if img_np.shape[2] == 1:  # gray image
+                img_np = np.squeeze(img_np, axis=2)
+            else:
+                if rgb2bgr:
+                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+        elif n_dim == 2:
+            img_np = _tensor.numpy()
+        else:
+            raise TypeError('Only support 4D, 3D or 2D tensor. ' f'But received with dimension: {n_dim}')
+        if out_type == np.uint8:
+            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
+            img_np = (img_np * 255.0).round()
+        img_np = img_np.astype(out_type)
+        result.append(img_np)
+    if len(result) == 1:
+        result = result[0]
+    return result
+
+
+def rgba2rgb_tensor(rgba):
+    r = rgba[...,0]
+    g = rgba[...,1]
+    b = rgba[...,2]
+    return torch.stack([r, g, b], dim=3)
+
+
+def download(url, path, name):
+    request = urllib.request.urlopen(url)
+    total = int(request.headers.get('Content-Length', 0))
+    with tqdm(total=total, desc=f'[ReActor] Downloading {name} to {path}', unit='B', unit_scale=True, unit_divisor=1024) as progress:
+        urllib.request.urlretrieve(url, path, reporthook=lambda count, block_size, total_size: progress.update(block_size))
+
+
+def move_path(old_path, new_path):
+    if os.path.exists(old_path):
+        try:
+            models = os.listdir(old_path)
+            for model in models:
+                move_old_path = os.path.join(old_path, model)
+                move_new_path = os.path.join(new_path, model)
+                os.rename(move_old_path, move_new_path)
+            os.rmdir(old_path)
+        except Exception as e:
+            print(f"Error: {e}")
+            new_path = old_path
+
+
+def addLoggingLevel(levelName, levelNum, methodName=None):
+    if not methodName:
+        methodName = levelName.lower()
+
+    def logForLevel(self, message, *args, **kwargs):
+        if self.isEnabledFor(levelNum):
+            self._log(levelNum, message, args, **kwargs)
+
+    def logToRoot(message, *args, **kwargs):
+        logging.log(levelNum, message, *args, **kwargs)
+
+    logging.addLevelName(levelNum, levelName)
+    setattr(logging, levelName, levelNum)
+    setattr(logging.getLoggerClass(), methodName, logForLevel)
+    setattr(logging, methodName, logToRoot)
+
+
+def get_image_md5hash(image: Image.Image):
+    md5hash = hashlib.md5(image.tobytes())
+    return md5hash.hexdigest()
+
+
+def save_face_model(face: Face, filename: str) -> None:
+    try:
+        tensors = {
+            "bbox": torch.tensor(face["bbox"]),
+            "kps": torch.tensor(face["kps"]),
+            "det_score": torch.tensor(face["det_score"]),
+            "landmark_3d_68": torch.tensor(face["landmark_3d_68"]),
+            "pose": torch.tensor(face["pose"]),
+            "landmark_2d_106": torch.tensor(face["landmark_2d_106"]),
+            "embedding": torch.tensor(face["embedding"]),
+            "gender": torch.tensor(face["gender"]),
+            "age": torch.tensor(face["age"]),
+        }
+        save_file(tensors, filename)
+        print(f"Face model has been saved to '{filename}'")
+    except Exception as e:
+        print(f"Error: {e}")
+
+
+def load_face_model(filename: str):
+    face = {}
+    with safe_open(filename, framework="pt") as f:
+        for k in f.keys():
+            face[k] = f.get_tensor(k).numpy()
+    return Face(face)
+
+
+def get_ort_session():
+    global ORT_SESSION
+    return ORT_SESSION
+
+def set_ort_session(model_path, providers) -> Any:
+    global ORT_SESSION
+    onnxruntime.set_default_logger_severity(3)
+    ORT_SESSION = onnxruntime.InferenceSession(model_path, providers=providers)
+    return ORT_SESSION
+
+def clear_ort_session() -> None:
+    global ORT_SESSION
+    ORT_SESSION = None
+
+def prepare_cropped_face(cropped_face):
+	cropped_face = cropped_face[:, :, ::-1] / 255.0
+	cropped_face = (cropped_face - 0.5) / 0.5
+	cropped_face = np.expand_dims(cropped_face.transpose(2, 0, 1), axis = 0).astype(np.float32)
+	return cropped_face
+
+def normalize_cropped_face(cropped_face):
+	cropped_face = np.clip(cropped_face, -1, 1)
+	cropped_face = (cropped_face + 1) / 2
+	cropped_face = cropped_face.transpose(1, 2, 0)
+	cropped_face = (cropped_face * 255.0).round()
+	cropped_face = cropped_face.astype(np.uint8)[:, :, ::-1]
+	return cropped_face
+
+
+# author: Trung0246 --->
+def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions):
+    # Iterate over the list of full folder paths
+    for full_folder_path in full_folder_paths:
+        # Use the provided function to add each model folder path
+        folder_paths.add_model_folder_path(folder_name, full_folder_path)
+
+    # Now handle the extensions. If the folder name already exists, update the extensions
+    if folder_name in folder_paths.folder_names_and_paths:
+        # Unpack the current paths and extensions
+        current_paths, current_extensions = folder_paths.folder_names_and_paths[folder_name]
+        # Update the extensions set with the new extensions
+        updated_extensions = current_extensions | extensions
+        # Reassign the updated tuple back to the dictionary
+        folder_paths.folder_names_and_paths[folder_name] = (current_paths, updated_extensions)
+    else:
+        # If the folder name was not present, add_model_folder_path would have added it with the last path
+        # Now we just need to update the set of extensions as it would be an empty set
+        # Also ensure that all paths are included (since add_model_folder_path adds only one path at a time)
+        folder_paths.folder_names_and_paths[folder_name] = (full_folder_paths, extensions)
+# <---

requirements.txt

@@ -0,0 +1,7 @@
+albumentations>=1.4.16
+insightface==0.7.3
+onnx>=1.14.0
+opencv-python>=4.7.0.72
+numpy==1.26.3
+segment_anything
+ultralytics