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nodes.py
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nodes.py
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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
if result.is_cuda:
image_np = 255. * result.cpu().numpy()
else:
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)