Next (#144)

* Improve typing for our callbacks

* Return 0 for get_download_size

* Introduce ONNX powered face enhancer

* Introduce ONNX powered face enhancer

* Introduce ONNX powered face enhancer

* Remove tile processing from frame enhancer

* Fix video compress translation for libvpx-vp9

* Allow zero values for video compression

* Develop (#134)

* Introduce model options to the frame processors

* Finish UI to select frame processors models

* Simplify frame processors options

* Fix lint in CI

* Rename all kind of settings to options

* Add blend to enhancers

* Simplify webcam mode naming

* Bypass SSL issues under Windows

* Fix blend of frame enhancer

* Massive CLI refactoring, Register and apply ARGS via the frame processors

* Refine UI theme and introduce donate button

* Update dependencies and fix cpu only torch

* Update dependencies and fix cpu only torch

* Fix theme, Fix frame_processors in headless mode

* Remove useless astype

* Disable CoreML for the ONNX face enhancer

* Disable CoreML for the ONNX face enhancer

* Predict webcam too

* Improve resize of preview

* Change output quality defaults, Move options to the right

* Support for codeformer model

* Update the typo

* Add GPEN and GFPGAN 1.2

* Extract blend_frame methods

* Extend the installer

* Revert broken Gradio

* Rework on ui components

* Move output path selector to the output options

* Remove tons of pointless component updates

* Reset more base theme styling

* Use latest Gradio

* Fix the sliders

* More styles

* Update torch to 2.1.0

* Add RealESRNet_x4plus

* Fix that button

* Use latest onnxruntime-silicon

* Looks stable to me

* Lowercase model keys, Update preview and readme

facefusion/processors/frame/choices.py

@@ -0,0 +1,5 @@
+from typing import List
+
+face_swapper_models : List[str] = [ 'inswapper_128', 'inswapper_128_fp16' ]
+face_enhancer_models : List[str] = [ 'codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_512' ]
+frame_enhancer_models : List[str] = [ 'realesrgan_x2plus', 'realesrgan_x4plus', 'realesrnet_x4plus' ]

facefusion/processors/frame/core.py

@@ -5,17 +5,22 @@ import psutil
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from queue import Queue
 from types import ModuleType
-from typing import Any, List, Callable
+from typing import Any, List
 from tqdm import tqdm
 
 import facefusion.globals
 from facefusion import wording
+from facefusion.typing import Process_Frames
 
 FRAME_PROCESSORS_MODULES : List[ModuleType] = []
 FRAME_PROCESSORS_METHODS =\
 [
 	'get_frame_processor',
 	'clear_frame_processor',
+	'get_options',
+	'set_options',
+	'register_args',
+	'apply_args',
 	'pre_check',
 	'pre_process',
 	'process_frame',
@@ -57,7 +62,7 @@ def clear_frame_processors_modules() -> None:
 	FRAME_PROCESSORS_MODULES = []
 
 
-def multi_process_frames(source_path : str, temp_frame_paths : List[str], process_frames : Callable[[str, List[str], Callable[[], None]], None]) -> None:
+def multi_process_frames(source_path : str, temp_frame_paths : List[str], process_frames : Process_Frames) -> None:
 	progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
 	with tqdm(total = len(temp_frame_paths), desc = wording.get('processing'), unit = 'frame', dynamic_ncols = True, bar_format = progress_bar_format) as progress:
 		with ThreadPoolExecutor(max_workers = facefusion.globals.execution_thread_count) as executor:

facefusion/processors/frame/globals.py

@@ -0,0 +1,7 @@
+from typing import Optional
+
+face_swapper_model : Optional[str] = None
+face_enhancer_model : Optional[str] = None
+face_enhancer_blend : Optional[int] = None
+frame_enhancer_model : Optional[str] = None
+frame_enhancer_blend : Optional[int] = None

facefusion/processors/frame/modules/face_enhancer.py

@@ -1,21 +1,53 @@
-from typing import Any, List, Callable
+from typing import Any, List, Tuple, Dict, Literal, Optional
+from argparse import ArgumentParser
+import cv2
 import threading
-from gfpgan.utils import GFPGANer
+import numpy
+import onnxruntime
 
 import facefusion.globals
-from facefusion import wording, utilities
+from facefusion import wording
 from facefusion.core import update_status
 from facefusion.face_analyser import get_many_faces, clear_face_analyser
-from facefusion.typing import Frame, Face, ProcessMode
+from facefusion.typing import Face, Frame, Matrix, Update_Process, ProcessMode, ModelValue, OptionsWithModel
 from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video, is_file, is_download_done
 from facefusion.vision import read_image, read_static_image, write_image
+from facefusion.processors.frame import globals as frame_processors_globals
+from facefusion.processors.frame import choices as frame_processors_choices
 
 FRAME_PROCESSOR = None
 THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
 THREAD_LOCK : threading.Lock = threading.Lock()
 NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_ENHANCER'
-MODEL_URL = 'https://github.com/facefusion/facefusion-assets/releases/download/models/GFPGANv1.4.pth'
-MODEL_PATH = resolve_relative_path('../.assets/models/GFPGANv1.4.pth')
+MODELS : Dict[str, ModelValue] =\
+{
+	'codeformer':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/codeformer.onnx',
+		'path': resolve_relative_path('../.assets/models/codeformer.onnx')
+	},
+	'gfpgan_1.2':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/GFPGANv1.2.onnx',
+		'path': resolve_relative_path('../.assets/models/GFPGANv1.2.onnx')
+	},
+	'gfpgan_1.3':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/GFPGANv1.3.onnx',
+		'path': resolve_relative_path('../.assets/models/GFPGANv1.3.onnx')
+	},
+	'gfpgan_1.4':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/GFPGANv1.4.onnx',
+		'path': resolve_relative_path('../.assets/models/GFPGANv1.4.onnx')
+	},
+	'gpen_bfr_512':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/GPEN-BFR-512.onnx',
+		'path': resolve_relative_path('../.assets/models/GPEN-BFR-512.onnx')
+	}
+}
+OPTIONS : Optional[OptionsWithModel] = None
 
 
 def get_frame_processor() -> Any:
@@ -23,11 +55,8 @@ def get_frame_processor() -> Any:
 
 	with THREAD_LOCK:
 		if FRAME_PROCESSOR is None:
-			FRAME_PROCESSOR = GFPGANer(
-				model_path = MODEL_PATH,
-				upscale = 1,
-				device = utilities.get_device(facefusion.globals.execution_providers)
-			)
+			model_path = get_options('model').get('path')
+			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers)
 	return FRAME_PROCESSOR
 
 
@@ -37,18 +66,49 @@ def clear_frame_processor() -> None:
 	FRAME_PROCESSOR = None
 
 
+def get_options(key : Literal[ 'model' ]) -> Any:
+	global OPTIONS
+
+	if OPTIONS is None:
+		OPTIONS =\
+		{
+			'model': MODELS[frame_processors_globals.face_enhancer_model]
+		}
+	return OPTIONS.get(key)
+
+
+def set_options(key : Literal[ 'model' ], value : Any) -> None:
+	global OPTIONS
+
+	OPTIONS[key] = value
+
+
+def register_args(program : ArgumentParser) -> None:
+	program.add_argument('--face-enhancer-model', help = wording.get('frame_processor_model_help'), dest = 'face_enhancer_model', default = 'gfpgan_1.4', choices = frame_processors_choices.face_enhancer_models)
+	program.add_argument('--face-enhancer-blend', help = wording.get('frame_processor_blend_help'), dest= 'face_enhancer_blend', type = int, default= 100, choices = range(101), metavar = '[0-100]')
+
+
+def apply_args(program : ArgumentParser) -> None:
+	args = program.parse_args()
+	frame_processors_globals.face_enhancer_model = args.face_enhancer_model
+	frame_processors_globals.face_enhancer_blend = args.face_enhancer_blend
+
+
 def pre_check() -> bool:
 	if not facefusion.globals.skip_download:
 		download_directory_path = resolve_relative_path('../.assets/models')
-		conditional_download(download_directory_path, [ MODEL_URL ])
+		model_url = get_options('model').get('url')
+		conditional_download(download_directory_path, [ model_url ])
 	return True
 
 
 def pre_process(mode : ProcessMode) -> bool:
-	if not facefusion.globals.skip_download and not is_download_done(MODEL_URL, MODEL_PATH):
+	model_url = get_options('model').get('url')
+	model_path = get_options('model').get('path')
+	if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
 		update_status(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
 		return False
-	elif not is_file(MODEL_PATH):
+	elif not is_file(model_path):
 		update_status(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
 		return False
 	if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path):
@@ -66,22 +126,77 @@ def post_process() -> None:
 	read_static_image.cache_clear()
 
 
-def enhance_face(target_face : Face, temp_frame : Frame) -> Frame:
-	start_x, start_y, end_x, end_y = map(int, target_face['bbox'])
-	padding_x = int((end_x - start_x) * 0.5)
-	padding_y = int((end_y - start_y) * 0.5)
-	start_x = max(0, start_x - padding_x)
-	start_y = max(0, start_y - padding_y)
-	end_x = max(0, end_x + padding_x)
-	end_y = max(0, end_y + padding_y)
-	crop_frame = temp_frame[start_y:end_y, start_x:end_x]
-	if crop_frame.size:
-		with THREAD_SEMAPHORE:
-			_, _, crop_frame = get_frame_processor().enhance(
-				crop_frame,
-				paste_back = True
-			)
-		temp_frame[start_y:end_y, start_x:end_x] = crop_frame
+def enhance_face(target_face: Face, temp_frame: Frame) -> Frame:
+	frame_processor = get_frame_processor()
+	crop_frame, affine_matrix = warp_face(target_face, temp_frame)
+	crop_frame = prepare_crop_frame(crop_frame)
+	frame_processor_inputs = {}
+	for frame_processor_input in frame_processor.get_inputs():
+		if frame_processor_input.name == 'input':
+			frame_processor_inputs[frame_processor_input.name] = crop_frame
+		if frame_processor_input.name == 'weight':
+			frame_processor_inputs[frame_processor_input.name] = numpy.array([ 1 ], dtype = numpy.double)
+	with THREAD_SEMAPHORE:
+		crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
+	crop_frame = normalize_crop_frame(crop_frame)
+	paste_frame = paste_back(temp_frame, crop_frame, affine_matrix)
+	temp_frame = blend_frame(temp_frame, paste_frame)
+	return temp_frame
+
+
+def warp_face(target_face : Face, temp_frame : Frame) -> Tuple[Frame, Matrix]:
+	template = numpy.array(
+	[
+		[ 192.98138, 239.94708 ],
+		[ 318.90277, 240.1936 ],
+		[ 256.63416, 314.01935 ],
+		[ 201.26117, 371.41043 ],
+		[ 313.08905, 371.15118 ]
+	])
+	affine_matrix = cv2.estimateAffinePartial2D(target_face['kps'], template, method = cv2.LMEDS)[0]
+	crop_frame = cv2.warpAffine(temp_frame, affine_matrix, (512, 512))
+	return crop_frame, affine_matrix
+
+
+def prepare_crop_frame(crop_frame : Frame) -> Frame:
+	crop_frame = crop_frame[:, :, ::-1] / 255.0
+	crop_frame = (crop_frame - 0.5) / 0.5
+	crop_frame = numpy.expand_dims(crop_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
+	return crop_frame
+
+
+def normalize_crop_frame(crop_frame : Frame) -> Frame:
+	crop_frame = numpy.clip(crop_frame, -1, 1)
+	crop_frame = (crop_frame + 1) / 2
+	crop_frame = crop_frame.transpose(1, 2, 0)
+	crop_frame = (crop_frame * 255.0).round()
+	crop_frame = crop_frame.astype(numpy.uint8)[:, :, ::-1]
+	return crop_frame
+
+
+def paste_back(temp_frame : Frame, crop_frame : Frame, affine_matrix : Matrix) -> Frame:
+	inverse_affine_matrix = cv2.invertAffineTransform(affine_matrix)
+	temp_frame_height, temp_frame_width = temp_frame.shape[0:2]
+	crop_frame_height, crop_frame_width = crop_frame.shape[0:2]
+	inverse_crop_frame = cv2.warpAffine(crop_frame, inverse_affine_matrix, (temp_frame_width, temp_frame_height))
+	inverse_mask = numpy.ones((crop_frame_height, crop_frame_width, 3), dtype = numpy.float32)
+	inverse_mask_frame = cv2.warpAffine(inverse_mask, inverse_affine_matrix, (temp_frame_width, temp_frame_height))
+	inverse_mask_frame = cv2.erode(inverse_mask_frame, numpy.ones((2, 2)))
+	inverse_mask_border = inverse_mask_frame * inverse_crop_frame
+	inverse_mask_area = numpy.sum(inverse_mask_frame) // 3
+	inverse_mask_edge = int(inverse_mask_area ** 0.5) // 20
+	inverse_mask_radius = inverse_mask_edge * 2
+	inverse_mask_center = cv2.erode(inverse_mask_frame, numpy.ones((inverse_mask_radius, inverse_mask_radius)))
+	inverse_mask_blur_size = inverse_mask_edge * 2 + 1
+	inverse_mask_blur_area = cv2.GaussianBlur(inverse_mask_center, (inverse_mask_blur_size, inverse_mask_blur_size), 0)
+	temp_frame = inverse_mask_blur_area * inverse_mask_border + (1 - inverse_mask_blur_area) * temp_frame
+	temp_frame = temp_frame.clip(0, 255).astype(numpy.uint8)
+	return temp_frame
+
+
+def blend_frame(temp_frame : Frame, paste_frame : Frame) -> Frame:
+	face_enhancer_blend = 1 - (frame_processors_globals.face_enhancer_blend / 100)
+	temp_frame = cv2.addWeighted(temp_frame, face_enhancer_blend, paste_frame, 1 - face_enhancer_blend, 0)
 	return temp_frame
 
 
@@ -93,7 +208,7 @@ def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame)
 	return temp_frame
 
 
-def process_frames(source_path : str, temp_frame_paths : List[str], update_progress: Callable[[], None]) -> None:
+def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None:
 	for temp_frame_path in temp_frame_paths:
 		temp_frame = read_image(temp_frame_path)
 		result_frame = process_frame(None, None, temp_frame)

facefusion/processors/frame/modules/face_swapper.py

@@ -1,4 +1,5 @@
-from typing import Any, List, Callable
+from typing import Any, List, Dict, Literal, Optional
+from argparse import ArgumentParser
 import insightface
 import threading
 
@@ -8,15 +9,29 @@ from facefusion import wording
 from facefusion.core import update_status
 from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_faces, clear_face_analyser
 from facefusion.face_reference import get_face_reference, set_face_reference
-from facefusion.typing import Face, Frame, ProcessMode
+from facefusion.typing import Face, Frame, Update_Process, ProcessMode, ModelValue, OptionsWithModel
 from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video, is_file, is_download_done
 from facefusion.vision import read_image, read_static_image, write_image
+from facefusion.processors.frame import globals as frame_processors_globals
+from facefusion.processors.frame import choices as frame_processors_choices
 
 FRAME_PROCESSOR = None
 THREAD_LOCK : threading.Lock = threading.Lock()
 NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_SWAPPER'
-MODEL_URL = 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx'
-MODEL_PATH = resolve_relative_path('../.assets/models/inswapper_128.onnx')
+MODELS : Dict[str, ModelValue] =\
+{
+	'inswapper_128':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx',
+		'path': resolve_relative_path('../.assets/models/inswapper_128.onnx')
+	},
+	'inswapper_128_fp16':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx',
+		'path': resolve_relative_path('../.assets/models/inswapper_128_fp16.onnx')
+	}
+}
+OPTIONS : Optional[OptionsWithModel] = None
 
 
 def get_frame_processor() -> Any:
@@ -24,7 +39,8 @@ def get_frame_processor() -> Any:
 
 	with THREAD_LOCK:
 		if FRAME_PROCESSOR is None:
-			FRAME_PROCESSOR = insightface.model_zoo.get_model(MODEL_PATH, providers = facefusion.globals.execution_providers)
+			model_path = get_options('model').get('path')
+			FRAME_PROCESSOR = insightface.model_zoo.get_model(model_path, providers = facefusion.globals.execution_providers)
 	return FRAME_PROCESSOR
 
 
@@ -34,18 +50,47 @@ def clear_frame_processor() -> None:
 	FRAME_PROCESSOR = None
 
 
+def get_options(key : Literal[ 'model' ]) -> Any:
+	global OPTIONS
+
+	if OPTIONS is None:
+		OPTIONS = \
+		{
+			'model': MODELS[frame_processors_globals.face_swapper_model]
+		}
+	return OPTIONS.get(key)
+
+
+def set_options(key : Literal[ 'model' ], value : Any) -> None:
+	global OPTIONS
+
+	OPTIONS[key] = value
+
+
+def register_args(program : ArgumentParser) -> None:
+	program.add_argument('--face-swapper-model', help = wording.get('frame_processor_model_help'), dest = 'face_swapper_model', default = 'inswapper_128', choices = frame_processors_choices.face_swapper_models)
+
+
+def apply_args(program : ArgumentParser) -> None:
+	args = program.parse_args()
+	frame_processors_globals.face_swapper_model = args.face_swapper_model
+
+
 def pre_check() -> bool:
 	if not facefusion.globals.skip_download:
 		download_directory_path = resolve_relative_path('../.assets/models')
-		conditional_download(download_directory_path, [ MODEL_URL ])
+		model_url = get_options('model').get('url')
+		conditional_download(download_directory_path, [ model_url ])
 	return True
 
 
 def pre_process(mode : ProcessMode) -> bool:
-	if not facefusion.globals.skip_download and not is_download_done(MODEL_URL, MODEL_PATH):
+	model_url = get_options('model').get('url')
+	model_path = get_options('model').get('path')
+	if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
 		update_status(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
 		return False
-	elif not is_file(MODEL_PATH):
+	elif not is_file(model_path):
 		update_status(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
 		return False
 	if not is_image(facefusion.globals.source_path):
@@ -87,7 +132,7 @@ def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame)
 	return temp_frame
 
 
-def process_frames(source_path : str, temp_frame_paths : List[str], update_progress: Callable[[], None]) -> None:
+def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None:
 	source_face = get_one_face(read_static_image(source_path))
 	reference_face = get_face_reference() if 'reference' in facefusion.globals.face_recognition else None
 	for temp_frame_path in temp_frame_paths:

facefusion/processors/frame/modules/frame_enhancer.py

@@ -1,23 +1,47 @@
-from typing import Any, List, Callable
+from typing import Any, List, Dict, Literal, Optional
+from argparse import ArgumentParser
 import threading
+import cv2
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from realesrgan import RealESRGANer
 
 import facefusion.globals
 import facefusion.processors.frame.core as frame_processors
-from facefusion import wording, utilities
+from facefusion import wording
 from facefusion.core import update_status
 from facefusion.face_analyser import clear_face_analyser
-from facefusion.typing import Frame, Face, ProcessMode
-from facefusion.utilities import conditional_download, resolve_relative_path, is_file, is_download_done
+from facefusion.typing import Frame, Face, Update_Process, ProcessMode, ModelValue, OptionsWithModel
+from facefusion.utilities import conditional_download, resolve_relative_path, is_file, is_download_done, get_device
 from facefusion.vision import read_image, read_static_image, write_image
+from facefusion.processors.frame import globals as frame_processors_globals
+from facefusion.processors.frame import choices as frame_processors_choices
 
 FRAME_PROCESSOR = None
 THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
 THREAD_LOCK : threading.Lock = threading.Lock()
 NAME = 'FACEFUSION.FRAME_PROCESSOR.FRAME_ENHANCER'
-MODEL_URL = 'https://github.com/facefusion/facefusion-assets/releases/download/models/RealESRGAN_x4plus.pth'
-MODEL_PATH = resolve_relative_path('../.assets/models/RealESRGAN_x4plus.pth')
+MODELS: Dict[str, ModelValue] =\
+{
+	'realesrgan_x2plus':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/RealESRGAN_x2plus.pth',
+		'path': resolve_relative_path('../.assets/models/RealESRGAN_x2plus.pth'),
+		'scale': 2
+	},
+	'realesrgan_x4plus':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/RealESRGAN_x4plus.pth',
+		'path': resolve_relative_path('../.assets/models/RealESRGAN_x4plus.pth'),
+		'scale': 4
+	},
+	'realesrnet_x4plus':
+	{
+		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/RealESRNet_x4plus.pth',
+		'path': resolve_relative_path('../.assets/models/RealESRNet_x4plus.pth'),
+		'scale': 4
+	}
+}
+OPTIONS : Optional[OptionsWithModel] = None
 
 
 def get_frame_processor() -> Any:
@@ -25,21 +49,17 @@ def get_frame_processor() -> Any:
 
 	with THREAD_LOCK:
 		if FRAME_PROCESSOR is None:
+			model_path = get_options('model').get('path')
+			model_scale = get_options('model').get('scale')
 			FRAME_PROCESSOR = RealESRGANer(
-				model_path = MODEL_PATH,
+				model_path = model_path,
 				model = RRDBNet(
 					num_in_ch = 3,
 					num_out_ch = 3,
-					num_feat = 64,
-					num_block = 23,
-					num_grow_ch = 32,
-					scale = 4
+					scale = model_scale
 				),
-				device = utilities.get_device(facefusion.globals.execution_providers),
-				tile = 512,
-				tile_pad = 32,
-				pre_pad = 0,
-				scale = 4
+				device = get_device(facefusion.globals.execution_providers),
+				scale = model_scale
 			)
 	return FRAME_PROCESSOR
 
@@ -50,18 +70,49 @@ def clear_frame_processor() -> None:
 	FRAME_PROCESSOR = None
 
 
+def get_options(key : Literal[ 'model' ]) -> Any:
+	global OPTIONS
+
+	if OPTIONS is None:
+		OPTIONS = \
+		{
+			'model': MODELS[frame_processors_globals.frame_enhancer_model]
+		}
+	return OPTIONS.get(key)
+
+
+def set_options(key : Literal[ 'model' ], value : Any) -> None:
+	global OPTIONS
+
+	OPTIONS[key] = value
+
+
+def register_args(program : ArgumentParser) -> None:
+	program.add_argument('--frame-enhancer-model', help = wording.get('frame_processor_model_help'), dest = 'frame_enhancer_model', default = 'realesrgan_x2plus', choices = frame_processors_choices.frame_enhancer_models)
+	program.add_argument('--frame-enhancer-blend', help = wording.get('frame_processor_blend_help'), dest = 'frame_enhancer_blend', type = int, default = 100, choices = range(101), metavar = '[0-100]')
+
+
+def apply_args(program : ArgumentParser) -> None:
+	args = program.parse_args()
+	frame_processors_globals.frame_enhancer_model = args.frame_enhancer_model
+	frame_processors_globals.frame_enhancer_blend = args.frame_enhancer_blend
+
+
 def pre_check() -> bool:
 	if not facefusion.globals.skip_download:
 		download_directory_path = resolve_relative_path('../.assets/models')
-		conditional_download(download_directory_path, [ MODEL_URL ])
+		model_url = get_options('model').get('url')
+		conditional_download(download_directory_path, [ model_url ])
 	return True
 
 
 def pre_process(mode : ProcessMode) -> bool:
-	if not facefusion.globals.skip_download and not is_download_done(MODEL_URL, MODEL_PATH):
+	model_url = get_options('model').get('url')
+	model_path = get_options('model').get('path')
+	if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
 		update_status(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
 		return False
-	elif not is_file(MODEL_PATH):
+	elif not is_file(model_path):
 		update_status(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
 		return False
 	if mode == 'output' and not facefusion.globals.output_path:
@@ -78,7 +129,15 @@ def post_process() -> None:
 
 def enhance_frame(temp_frame : Frame) -> Frame:
 	with THREAD_SEMAPHORE:
-		temp_frame, _ = get_frame_processor().enhance(temp_frame, outscale = 1)
+		paste_frame, _ = get_frame_processor().enhance(temp_frame)
+		temp_frame = blend_frame(temp_frame, paste_frame)
+	return temp_frame
+
+
+def blend_frame(temp_frame : Frame, paste_frame : Frame) -> Frame:
+	frame_enhancer_blend = 1 - (frame_processors_globals.frame_enhancer_blend / 100)
+	temp_frame = cv2.resize(temp_frame, (paste_frame.shape[1], paste_frame.shape[0]))
+	temp_frame = cv2.addWeighted(temp_frame, frame_enhancer_blend, paste_frame, 1 - frame_enhancer_blend, 0)
 	return temp_frame
 
 
@@ -86,7 +145,7 @@ def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame)
 	return enhance_frame(temp_frame)
 
 
-def process_frames(source_path : str, temp_frame_paths : List[str], update_progress: Callable[[], None]) -> None:
+def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None:
 	for temp_frame_path in temp_frame_paths:
 		temp_frame = read_image(temp_frame_path)
 		result_frame = process_frame(None, None, temp_frame)