diff options
Diffstat (limited to 'nodes.py')
| -rw-r--r-- | nodes.py | 1237 |
1 files changed, 1237 insertions, 0 deletions
diff --git a/nodes.py b/nodes.py new file mode 100644 index 0000000..805863d --- /dev/null +++ b/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",
+}
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