forked from Gourieff/comfyui-reactor-node
/
nodes.py
363 lines (291 loc) · 13.9 KB
/
nodes.py
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import os, glob, sys
import torch
from torchvision.transforms.functional import normalize
import numpy as np
import cv2
from modules.processing import StableDiffusionProcessingImg2Img
from comfy_extras.chainner_models import model_loading
import model_management
import comfy.utils
import folder_paths
import scripts.reactor_version
from scripts.reactor_faceswap import FaceSwapScript, get_models, get_current_faces_model, analyze_faces
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
)
from reactor_log_patch import apply_logging_patch
from r_facelib.utils.face_restoration_helper import FaceRestoreHelper
from basicsr.utils.registry import ARCH_REGISTRY
import scripts.r_archs.codeformer_arch
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)
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")]
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"
]
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")]
return models
def get_model_names(get_models):
models = get_models()
names = ["none"]
for x in models:
names.append(os.path.basename(x))
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_source": (["no","female","male"], {"default": "no"}),
"detect_gender_input": (["no","female","male"], {"default": "no"}),
"source_faces_index": ("STRING", {"default": "0"}),
"input_faces_index": ("STRING", {"default": "0"}),
"det_size": ("INT", {"default": 640, "min": 1, "max": 3000}),
"console_log_level": ([0, 1, 2], {"default": 1}),
},
"optional": {
"source_image": ("IMAGE",),
"face_model": ("FACE_MODEL",),
}
}
RETURN_TYPES = ("IMAGE","FACE_MODEL")
FUNCTION = "execute"
CATEGORY = "ReActor"
def __init__(self):
self.face_helper = None
def restore_face(
self,
input_image,
face_restore_model,
face_restore_visibility,
codeformer_weight,
facedetection
):
result = input_image
if face_restore_model != "none":
logger.status(f"Restoring with {face_restore_model}")
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()
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 self.face_helper is None:
self.face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model=facedetection, save_ext='png', use_parse=True, device=device)
image_np = 255. * result.cpu().numpy()
total_images = image_np.shape[0]
out_images = np.ndarray(shape=image_np.shape)
for i in range(total_images):
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):
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():
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 for CodeFormer: {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_LINEAR)
self.face_helper.clean_all()
out_images[i] = restored_img
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, det_size, face_restore_model, face_restore_visibility, codeformer_weight, facedetection, source_image=None, face_model=None):
apply_logging_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,
det_size=det_size,
face_model=face_model,
)
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
result = reactor.restore_face(self,result,face_restore_model,face_restore_visibility,codeformer_weight,facedetection)
return (result,face_model_to_provide)
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 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"}),
},
"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, image=None, face_model=None):
if save_mode and image is not None:
source = tensor_to_pil(image)
source = cv2.cvtColor(np.array(source), cv2.COLOR_RGB2BGR)
apply_logging_patch(1)
logger.status("Building Face Model...")
face_model = analyze_faces(source)[0]
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
def execute(self, image, model, visibility, codeformer_weight, facedetection):
result = reactor.restore_face(self,image,model,visibility,codeformer_weight,facedetection)
return (result,)
NODE_CLASS_MAPPINGS = {
"ReActorFaceSwap": reactor,
"ReActorLoadFaceModel": LoadFaceModel,
"ReActorSaveFaceModel": SaveFaceModel,
"ReActorRestoreFace": RestoreFace,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ReActorFaceSwap": "ReActor - Fast Face Swap",
"ReActorLoadFaceModel": "Load Face Model",
"ReActorSaveFaceModel": "Save Face Model",
"ReActorRestoreFace": "Restore Face",
}