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Introduce create_static_model_set() everywhere (#824)

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Henry Ruhs 2024-11-20 21:05:18 +01:00 committed by GitHub
parent ab34dbb991
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17 changed files with 323 additions and 255 deletions
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54
facefusion/content_analyser.py
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@ -11,35 +11,39 @@ from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Fps, InferencePool, ModelOptions, ModelSet, VisionFrame from facefusion.typing import Fps, InferencePool, ModelOptions, ModelSet, VisionFrame
from facefusion.vision import count_video_frame_total, detect_video_fps, get_video_frame, read_image from facefusion.vision import count_video_frame_total, detect_video_fps, get_video_frame, read_image
MODEL_SET : ModelSet =\
{
'open_nsfw':
{
'hashes':
{
'content_analyser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/open_nsfw.hash',
'path': resolve_relative_path('../.assets/models/open_nsfw.hash')
}
},
'sources':
{
'content_analyser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/open_nsfw.onnx',
'path': resolve_relative_path('../.assets/models/open_nsfw.onnx')
}
},
'size': (224, 224),
'mean': [ 104, 117, 123 ]
}
}
PROBABILITY_LIMIT = 0.80 PROBABILITY_LIMIT = 0.80
RATE_LIMIT = 10 RATE_LIMIT = 10
STREAM_COUNTER = 0 STREAM_COUNTER = 0
@lru_cache(maxsize = None)
def create_static_model_set() -> ModelSet:
return\
{
'open_nsfw':
{
'hashes':
{
'content_analyser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/open_nsfw.hash',
'path': resolve_relative_path('../.assets/models/open_nsfw.hash')
}
},
'sources':
{
'content_analyser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/open_nsfw.onnx',
'path': resolve_relative_path('../.assets/models/open_nsfw.onnx')
}
},
'size': (224, 224),
'mean': [ 104, 117, 123 ]
}
}
def get_inference_pool() -> InferencePool: def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources') model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources) return inference_manager.get_inference_pool(__name__, model_sources)
@ -50,7 +54,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
return MODEL_SET.get('open_nsfw') return create_static_model_set().get('open_nsfw')
def pre_check() -> bool: def pre_check() -> bool:

19
facefusion/core.py
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@ -157,6 +157,25 @@ def conditional_append_reference_faces() -> None:
append_reference_face(processor_module.__name__, abstract_reference_face) append_reference_face(processor_module.__name__, abstract_reference_face)
def clear_model_sets() -> None:
available_processors = list_directory('facefusion/processors/modules')
common_modules =\
[
content_analyser,
face_classifier,
face_detector,
face_landmarker,
face_recognizer,
face_masker,
voice_extractor
]
processor_modules = get_processors_modules(available_processors)
for module in common_modules + processor_modules:
if hasattr(module, 'create_static_model_set'):
module.create_static_model_set.cache_clear()
def force_download() -> ErrorCode: def force_download() -> ErrorCode:
available_processors = list_directory('facefusion/processors/modules') available_processors = list_directory('facefusion/processors/modules')
common_modules =\ common_modules =\

48
facefusion/face_classifier.py
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@ -1,3 +1,4 @@
from functools import lru_cache
from typing import List, Tuple from typing import List, Tuple
import numpy import numpy
@ -9,32 +10,35 @@ from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Age, FaceLandmark5, Gender, InferencePool, ModelOptions, ModelSet, Race, VisionFrame from facefusion.typing import Age, FaceLandmark5, Gender, InferencePool, ModelOptions, ModelSet, Race, VisionFrame
MODEL_SET : ModelSet =\
{ @lru_cache(maxsize = None)
'fairface': def create_static_model_set() -> ModelSet:
return\
{ {
'hashes': 'fairface':
{ {
'face_classifier': 'hashes':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fairface.hash', 'face_classifier':
'path': resolve_relative_path('../.assets/models/fairface.hash') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fairface.hash',
}, 'path': resolve_relative_path('../.assets/models/fairface.hash')
'sources': }
{ },
'face_classifier': 'sources':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fairface.onnx', 'face_classifier':
'path': resolve_relative_path('../.assets/models/fairface.onnx') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fairface.onnx',
}, 'path': resolve_relative_path('../.assets/models/fairface.onnx')
'template': 'arcface_112_v2', }
'size': (224, 224), },
'mean': [ 0.485, 0.456, 0.406 ], 'template': 'arcface_112_v2',
'standard_deviation': [ 0.229, 0.224, 0.225 ] 'size': (224, 224),
'mean': [ 0.485, 0.456, 0.406 ],
'standard_deviation': [ 0.229, 0.224, 0.225 ]
}
} }
}
def get_inference_pool() -> InferencePool: def get_inference_pool() -> InferencePool:
@ -47,7 +51,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
return MODEL_SET.get('fairface') return create_static_model_set().get('fairface')
def pre_check() -> bool: def pre_check() -> bool:

105
facefusion/face_detector.py
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@ -2,6 +2,7 @@ from typing import List, Tuple
import cv2 import cv2
import numpy import numpy
from charset_normalizer.md import lru_cache
from facefusion import inference_manager, state_manager from facefusion import inference_manager, state_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources from facefusion.download import conditional_download_hashes, conditional_download_sources
@ -11,66 +12,69 @@ from facefusion.thread_helper import thread_semaphore
from facefusion.typing import Angle, BoundingBox, Detection, DownloadSet, FaceLandmark5, InferencePool, ModelSet, Score, VisionFrame from facefusion.typing import Angle, BoundingBox, Detection, DownloadSet, FaceLandmark5, InferencePool, ModelSet, Score, VisionFrame
from facefusion.vision import resize_frame_resolution, unpack_resolution from facefusion.vision import resize_frame_resolution, unpack_resolution
MODEL_SET : ModelSet =\
{ @lru_cache(maxsize = None)
'retinaface': def create_static_model_set() -> ModelSet:
return\
{ {
'hashes': 'retinaface':
{ {
'retinaface': 'hashes':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/retinaface_10g.hash', 'retinaface':
'path': resolve_relative_path('../.assets/models/retinaface_10g.hash') {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/retinaface_10g.hash',
'path': resolve_relative_path('../.assets/models/retinaface_10g.hash')
}
},
'sources':
{
'retinaface':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/retinaface_10g.onnx',
'path': resolve_relative_path('../.assets/models/retinaface_10g.onnx')
}
} }
}, },
'sources': 'scrfd':
{ {
'retinaface': 'hashes':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/retinaface_10g.onnx', 'scrfd':
'path': resolve_relative_path('../.assets/models/retinaface_10g.onnx') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/scrfd_2.5g.hash',
} 'path': resolve_relative_path('../.assets/models/scrfd_2.5g.hash')
}, }
'scrfd': },
{ 'sources':
'hashes':
{
'scrfd':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/scrfd_2.5g.hash', 'scrfd':
'path': resolve_relative_path('../.assets/models/scrfd_2.5g.hash') {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/scrfd_2.5g.onnx',
'path': resolve_relative_path('../.assets/models/scrfd_2.5g.onnx')
}
} }
}, },
'sources': 'yoloface':
{ {
'scrfd': 'hashes':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/scrfd_2.5g.onnx', 'yoloface':
'path': resolve_relative_path('../.assets/models/scrfd_2.5g.onnx') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/yoloface_8n.hash',
} 'path': resolve_relative_path('../.assets/models/yoloface_8n.hash')
}, }
'yoloface': },
{ 'sources':
'hashes':
{
'yoloface':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/yoloface_8n.hash', 'yoloface':
'path': resolve_relative_path('../.assets/models/yoloface_8n.hash') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/yoloface_8n.onnx',
}, 'path': resolve_relative_path('../.assets/models/yoloface_8n.onnx')
'sources': }
{
'yoloface':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/yoloface_8n.onnx',
'path': resolve_relative_path('../.assets/models/yoloface_8n.onnx')
} }
} }
} }
}
def get_inference_pool() -> InferencePool: def get_inference_pool() -> InferencePool:
@ -87,16 +91,17 @@ def clear_inference_pool() -> None:
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]: def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
model_hashes = {} model_hashes = {}
model_sources = {} model_sources = {}
model_set = create_static_model_set()
if state_manager.get_item('face_detector_model') in [ 'many', 'retinaface' ]: if state_manager.get_item('face_detector_model') in [ 'many', 'retinaface' ]:
model_hashes['retinaface'] = MODEL_SET.get('retinaface').get('hashes').get('retinaface') model_hashes['retinaface'] = model_set.get('retinaface').get('hashes').get('retinaface')
model_sources['retinaface'] = MODEL_SET.get('retinaface').get('sources').get('retinaface') model_sources['retinaface'] = model_set.get('retinaface').get('sources').get('retinaface')
if state_manager.get_item('face_detector_model') in [ 'many', 'scrfd' ]: if state_manager.get_item('face_detector_model') in [ 'many', 'scrfd' ]:
model_hashes['scrfd'] = MODEL_SET.get('scrfd').get('hashes').get('scrfd') model_hashes['scrfd'] = model_set.get('scrfd').get('hashes').get('scrfd')
model_sources['scrfd'] = MODEL_SET.get('scrfd').get('sources').get('scrfd') model_sources['scrfd'] = model_set.get('scrfd').get('sources').get('scrfd')
if state_manager.get_item('face_detector_model') in [ 'many', 'yoloface' ]: if state_manager.get_item('face_detector_model') in [ 'many', 'yoloface' ]:
model_hashes['yoloface'] = MODEL_SET.get('yoloface').get('hashes').get('yoloface') model_hashes['yoloface'] = model_set.get('yoloface').get('hashes').get('yoloface')
model_sources['yoloface'] = MODEL_SET.get('yoloface').get('sources').get('yoloface') model_sources['yoloface'] = model_set.get('yoloface').get('sources').get('yoloface')
return model_hashes, model_sources return model_hashes, model_sources

119
facefusion/face_landmarker.py
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@ -1,3 +1,4 @@
from functools import lru_cache
from typing import Tuple from typing import Tuple
import cv2 import cv2
@ -10,68 +11,71 @@ from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Angle, BoundingBox, DownloadSet, FaceLandmark5, FaceLandmark68, InferencePool, ModelSet, Prediction, Score, VisionFrame from facefusion.typing import Angle, BoundingBox, DownloadSet, FaceLandmark5, FaceLandmark68, InferencePool, ModelSet, Prediction, Score, VisionFrame
MODEL_SET : ModelSet =\
{ @lru_cache(maxsize = None)
'2dfan4': def create_static_model_set() -> ModelSet:
return\
{ {
'hashes': '2dfan4':
{ {
'2dfan4': 'hashes':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/2dfan4.hash', '2dfan4':
'path': resolve_relative_path('../.assets/models/2dfan4.hash') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/2dfan4.hash',
'path': resolve_relative_path('../.assets/models/2dfan4.hash')
}
},
'sources':
{
'2dfan4':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/2dfan4.onnx',
'path': resolve_relative_path('../.assets/models/2dfan4.onnx')
}
},
'size': (256, 256)
}, },
'sources': 'peppa_wutz':
{ {
'2dfan4': 'hashes':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/2dfan4.onnx', 'peppa_wutz':
'path': resolve_relative_path('../.assets/models/2dfan4.onnx') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/peppa_wutz.hash',
'path': resolve_relative_path('../.assets/models/peppa_wutz.hash')
}
},
'sources':
{
'peppa_wutz':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/peppa_wutz.onnx',
'path': resolve_relative_path('../.assets/models/peppa_wutz.onnx')
}
},
'size': (256, 256)
}, },
'size': (256, 256) 'fan_68_5':
},
'peppa_wutz':
{
'hashes':
{ {
'peppa_wutz': 'hashes':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/peppa_wutz.hash', 'fan_68_5':
'path': resolve_relative_path('../.assets/models/peppa_wutz.hash') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fan_68_5.hash',
}, 'path': resolve_relative_path('../.assets/models/fan_68_5.hash')
'sources': }
{ },
'peppa_wutz': 'sources':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/peppa_wutz.onnx', 'fan_68_5':
'path': resolve_relative_path('../.assets/models/peppa_wutz.onnx') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fan_68_5.onnx',
}, 'path': resolve_relative_path('../.assets/models/fan_68_5.onnx')
'size': (256, 256) }
},
'fan_68_5':
{
'hashes':
{
'fan_68_5':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fan_68_5.hash',
'path': resolve_relative_path('../.assets/models/fan_68_5.hash')
}
},
'sources':
{
'fan_68_5':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fan_68_5.onnx',
'path': resolve_relative_path('../.assets/models/fan_68_5.onnx')
} }
} }
} }
}
def get_inference_pool() -> InferencePool: def get_inference_pool() -> InferencePool:
@ -86,21 +90,22 @@ def clear_inference_pool() -> None:
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]: def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
model_set = create_static_model_set()
model_hashes =\ model_hashes =\
{ {
'fan_68_5': MODEL_SET.get('fan_68_5').get('hashes').get('fan_68_5') 'fan_68_5': model_set.get('fan_68_5').get('hashes').get('fan_68_5')
} }
model_sources =\ model_sources =\
{ {
'fan_68_5': MODEL_SET.get('fan_68_5').get('sources').get('fan_68_5') 'fan_68_5': model_set.get('fan_68_5').get('sources').get('fan_68_5')
} }
if state_manager.get_item('face_landmarker_model') in [ 'many', '2dfan4' ]: if state_manager.get_item('face_landmarker_model') in [ 'many', '2dfan4' ]:
model_hashes['2dfan4'] = MODEL_SET.get('2dfan4').get('hashes').get('2dfan4') model_hashes['2dfan4'] = model_set.get('2dfan4').get('hashes').get('2dfan4')
model_sources['2dfan4'] = MODEL_SET.get('2dfan4').get('sources').get('2dfan4') model_sources['2dfan4'] = model_set.get('2dfan4').get('sources').get('2dfan4')
if state_manager.get_item('face_landmarker_model') in [ 'many', 'peppa_wutz' ]: if state_manager.get_item('face_landmarker_model') in [ 'many', 'peppa_wutz' ]:
model_hashes['peppa_wutz'] = MODEL_SET.get('peppa_wutz').get('hashes').get('peppa_wutz') model_hashes['peppa_wutz'] = model_set.get('peppa_wutz').get('hashes').get('peppa_wutz')
model_sources['peppa_wutz'] = MODEL_SET.get('peppa_wutz').get('sources').get('peppa_wutz') model_sources['peppa_wutz'] = model_set.get('peppa_wutz').get('sources').get('peppa_wutz')
return model_hashes, model_sources return model_hashes, model_sources
@ -127,7 +132,7 @@ def detect_face_landmarks(vision_frame : VisionFrame, bounding_box : BoundingBox
def detect_with_2dfan4(temp_vision_frame: VisionFrame, bounding_box: BoundingBox, face_angle: Angle) -> Tuple[FaceLandmark68, Score]: def detect_with_2dfan4(temp_vision_frame: VisionFrame, bounding_box: BoundingBox, face_angle: Angle) -> Tuple[FaceLandmark68, Score]:
model_size = MODEL_SET.get('2dfan4').get('size') model_size = create_static_model_set().get('2dfan4').get('size')
scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None) scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None)
translation = (model_size[0] - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5 translation = (model_size[0] - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5
rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, model_size) rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, model_size)
@ -146,7 +151,7 @@ def detect_with_2dfan4(temp_vision_frame: VisionFrame, bounding_box: BoundingBox
def detect_with_peppa_wutz(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]: def detect_with_peppa_wutz(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]:
model_size = MODEL_SET.get('peppa_wutz').get('size') model_size = create_static_model_set().get('peppa_wutz').get('size')
scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None) scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None)
translation = (model_size[0] - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5 translation = (model_size[0] - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5
rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, model_size) rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, model_size)

103
facefusion/face_masker.py
View File

@ -11,49 +11,6 @@ from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import DownloadSet, FaceLandmark68, FaceMaskRegion, InferencePool, Mask, ModelSet, Padding, VisionFrame from facefusion.typing import DownloadSet, FaceLandmark68, FaceMaskRegion, InferencePool, Mask, ModelSet, Padding, VisionFrame
MODEL_SET : ModelSet =\
{
'face_occluder':
{
'hashes':
{
'face_occluder':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/dfl_xseg.hash',
'path': resolve_relative_path('../.assets/models/dfl_xseg.hash')
}
},
'sources':
{
'face_occluder':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/dfl_xseg.onnx',
'path': resolve_relative_path('../.assets/models/dfl_xseg.onnx')
}
},
'size': (256, 256)
},
'face_parser':
{
'hashes':
{
'face_parser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/bisenet_resnet_34.hash',
'path': resolve_relative_path('../.assets/models/bisenet_resnet_34.hash')
}
},
'sources':
{
'face_parser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/bisenet_resnet_34.onnx',
'path': resolve_relative_path('../.assets/models/bisenet_resnet_34.onnx')
}
},
'size': (512, 512)
}
}
FACE_MASK_REGIONS : Dict[FaceMaskRegion, int] =\ FACE_MASK_REGIONS : Dict[FaceMaskRegion, int] =\
{ {
'skin': 1, 'skin': 1,
@ -69,6 +26,53 @@ FACE_MASK_REGIONS : Dict[FaceMaskRegion, int] =\
} }
@lru_cache(maxsize = None)
def create_static_model_set() -> ModelSet:
return\
{
'face_occluder':
{
'hashes':
{
'face_occluder':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/dfl_xseg.hash',
'path': resolve_relative_path('../.assets/models/dfl_xseg.hash')
}
},
'sources':
{
'face_occluder':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/dfl_xseg.onnx',
'path': resolve_relative_path('../.assets/models/dfl_xseg.onnx')
}
},
'size': (256, 256)
},
'face_parser':
{
'hashes':
{
'face_parser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/bisenet_resnet_34.hash',
'path': resolve_relative_path('../.assets/models/bisenet_resnet_34.hash')
}
},
'sources':
{
'face_parser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/bisenet_resnet_34.onnx',
'path': resolve_relative_path('../.assets/models/bisenet_resnet_34.onnx')
}
},
'size': (512, 512)
}
}
def get_inference_pool() -> InferencePool: def get_inference_pool() -> InferencePool:
_, model_sources = collect_model_downloads() _, model_sources = collect_model_downloads()
return inference_manager.get_inference_pool(__name__, model_sources) return inference_manager.get_inference_pool(__name__, model_sources)
@ -79,15 +83,16 @@ def clear_inference_pool() -> None:
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]: def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
model_set = create_static_model_set()
model_hashes =\ model_hashes =\
{ {
'face_occluder': MODEL_SET.get('face_occluder').get('hashes').get('face_occluder'), 'face_occluder': model_set.get('face_occluder').get('hashes').get('face_occluder'),
'face_parser': MODEL_SET.get('face_parser').get('hashes').get('face_parser') 'face_parser': model_set.get('face_parser').get('hashes').get('face_parser')
} }
model_sources =\ model_sources =\
{ {
'face_occluder': MODEL_SET.get('face_occluder').get('sources').get('face_occluder'), 'face_occluder': model_set.get('face_occluder').get('sources').get('face_occluder'),
'face_parser': MODEL_SET.get('face_parser').get('sources').get('face_parser') 'face_parser': model_set.get('face_parser').get('sources').get('face_parser')
} }
return model_hashes, model_sources return model_hashes, model_sources
@ -113,7 +118,7 @@ def create_static_box_mask(crop_size : Size, face_mask_blur : float, face_mask_p
def create_occlusion_mask(crop_vision_frame : VisionFrame) -> Mask: def create_occlusion_mask(crop_vision_frame : VisionFrame) -> Mask:
model_size = MODEL_SET.get('face_occluder').get('size') model_size = create_static_model_set().get('face_occluder').get('size')
prepare_vision_frame = cv2.resize(crop_vision_frame, model_size) prepare_vision_frame = cv2.resize(crop_vision_frame, model_size)
prepare_vision_frame = numpy.expand_dims(prepare_vision_frame, axis = 0).astype(numpy.float32) / 255 prepare_vision_frame = numpy.expand_dims(prepare_vision_frame, axis = 0).astype(numpy.float32) / 255
prepare_vision_frame = prepare_vision_frame.transpose(0, 1, 2, 3) prepare_vision_frame = prepare_vision_frame.transpose(0, 1, 2, 3)
@ -125,7 +130,7 @@ def create_occlusion_mask(crop_vision_frame : VisionFrame) -> Mask:
def create_region_mask(crop_vision_frame : VisionFrame, face_mask_regions : List[FaceMaskRegion]) -> Mask: def create_region_mask(crop_vision_frame : VisionFrame, face_mask_regions : List[FaceMaskRegion]) -> Mask:
model_size = MODEL_SET.get('face_parser').get('size') model_size = create_static_model_set().get('face_parser').get('size')
prepare_vision_frame = cv2.resize(crop_vision_frame, model_size) prepare_vision_frame = cv2.resize(crop_vision_frame, model_size)
prepare_vision_frame = prepare_vision_frame[:, :, ::-1].astype(numpy.float32) / 255 prepare_vision_frame = prepare_vision_frame[:, :, ::-1].astype(numpy.float32) / 255
prepare_vision_frame = numpy.subtract(prepare_vision_frame, numpy.array([ 0.485, 0.456, 0.406 ]).astype(numpy.float32)) prepare_vision_frame = numpy.subtract(prepare_vision_frame, numpy.array([ 0.485, 0.456, 0.406 ]).astype(numpy.float32))

44
facefusion/face_recognizer.py
View File

@ -1,3 +1,4 @@
from functools import lru_cache
from typing import Tuple from typing import Tuple
import numpy import numpy
@ -9,30 +10,33 @@ from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Embedding, FaceLandmark5, InferencePool, ModelOptions, ModelSet, VisionFrame from facefusion.typing import Embedding, FaceLandmark5, InferencePool, ModelOptions, ModelSet, VisionFrame
MODEL_SET : ModelSet =\
{ @lru_cache(maxsize = None)
'arcface': def create_static_model_set() -> ModelSet:
return\
{ {
'hashes': 'arcface':
{ {
'face_recognizer': 'hashes':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_w600k_r50.hash', 'face_recognizer':
'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.hash') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_w600k_r50.hash',
}, 'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.hash')
'sources': }
{ },
'face_recognizer': 'sources':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_w600k_r50.onnx', 'face_recognizer':
'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.onnx') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_w600k_r50.onnx',
}, 'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.onnx')
'template': 'arcface_112_v2', }
'size': (112, 112) },
'template': 'arcface_112_v2',
'size': (112, 112)
}
} }
}
def get_inference_pool() -> InferencePool: def get_inference_pool() -> InferencePool:
@ -45,7 +49,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
return MODEL_SET.get('arcface') return create_static_model_set().get('arcface')
def pre_check() -> bool: def pre_check() -> bool:

6
facefusion/processors/modules/age_modifier.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser from argparse import ArgumentParser
from functools import lru_cache
from typing import List from typing import List
import cv2 import cv2
@ -24,7 +25,8 @@ from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ModelOp
from facefusion.vision import match_frame_color, read_image, read_static_image, write_image from facefusion.vision import match_frame_color, read_image, read_static_image, write_image
def create_model_set() -> ModelSet: @lru_cache(maxsize = None)
def create_static_model_set() -> ModelSet:
return\ return\
{ {
'styleganex_age': 'styleganex_age':
@ -73,7 +75,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
age_modifier_model = state_manager.get_item('age_modifier_model') age_modifier_model = state_manager.get_item('age_modifier_model')
return create_model_set().get(age_modifier_model) return create_static_model_set().get(age_modifier_model)
def register_args(program : ArgumentParser) -> None: def register_args(program : ArgumentParser) -> None:

6
facefusion/processors/modules/deep_swapper.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser from argparse import ArgumentParser
from functools import lru_cache
from typing import List, Tuple from typing import List, Tuple
import cv2 import cv2
@ -24,7 +25,8 @@ from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, Mask, M
from facefusion.vision import conditional_match_frame_color, read_image, read_static_image, write_image from facefusion.vision import conditional_match_frame_color, read_image, read_static_image, write_image
def create_model_set() -> ModelSet: @lru_cache(maxsize = None)
def create_static_model_set() -> ModelSet:
model_config =\ model_config =\
[ [
('druuzil', 'adrianne_palicki_384', (384, 384)), ('druuzil', 'adrianne_palicki_384', (384, 384)),
@ -217,7 +219,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
deep_swapper_model = state_manager.get_item('deep_swapper_model') deep_swapper_model = state_manager.get_item('deep_swapper_model')
return create_model_set().get(deep_swapper_model) return create_static_model_set().get(deep_swapper_model)
def register_args(program : ArgumentParser) -> None: def register_args(program : ArgumentParser) -> None:

6
facefusion/processors/modules/expression_restorer.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser from argparse import ArgumentParser
from functools import lru_cache
from typing import List, Tuple from typing import List, Tuple
import cv2 import cv2
@ -26,7 +27,8 @@ from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ModelOp
from facefusion.vision import get_video_frame, read_image, read_static_image, write_image from facefusion.vision import get_video_frame, read_image, read_static_image, write_image
def create_model_set() -> ModelSet: @lru_cache(maxsize = None)
def create_static_model_set() -> ModelSet:
return\ return\
{ {
'live_portrait': 'live_portrait':
@ -85,7 +87,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
expression_restorer_model = state_manager.get_item('expression_restorer_model') expression_restorer_model = state_manager.get_item('expression_restorer_model')
return create_model_set().get(expression_restorer_model) return create_static_model_set().get(expression_restorer_model)
def register_args(program : ArgumentParser) -> None: def register_args(program : ArgumentParser) -> None:

6
facefusion/processors/modules/face_editor.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser from argparse import ArgumentParser
from functools import lru_cache
from typing import List, Tuple from typing import List, Tuple
import cv2 import cv2
@ -25,7 +26,8 @@ from facefusion.typing import ApplyStateItem, Args, Face, FaceLandmark68, Infere
from facefusion.vision import read_image, read_static_image, write_image from facefusion.vision import read_image, read_static_image, write_image
def create_model_set() -> ModelSet: @lru_cache(maxsize = None)
def create_static_model_set() -> ModelSet:
return\ return\
{ {
'live_portrait': 'live_portrait':
@ -115,7 +117,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
face_editor_model = state_manager.get_item('face_editor_model') face_editor_model = state_manager.get_item('face_editor_model')
return create_model_set().get(face_editor_model) return create_static_model_set().get(face_editor_model)
def register_args(program : ArgumentParser) -> None: def register_args(program : ArgumentParser) -> None:

6
facefusion/processors/modules/face_enhancer.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser from argparse import ArgumentParser
from functools import lru_cache
from typing import List from typing import List
import cv2 import cv2
@ -24,7 +25,8 @@ from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ModelOp
from facefusion.vision import read_image, read_static_image, write_image from facefusion.vision import read_image, read_static_image, write_image
def create_model_set() -> ModelSet: @lru_cache(maxsize = None)
def create_static_model_set() -> ModelSet:
return\ return\
{ {
'codeformer': 'codeformer':
@ -232,7 +234,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
face_enhancer_model = state_manager.get_item('face_enhancer_model') face_enhancer_model = state_manager.get_item('face_enhancer_model')
return create_model_set().get(face_enhancer_model) return create_static_model_set().get(face_enhancer_model)
def register_args(program : ArgumentParser) -> None: def register_args(program : ArgumentParser) -> None:

6
facefusion/processors/modules/frame_colorizer.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser from argparse import ArgumentParser
from functools import lru_cache
from typing import List from typing import List
import cv2 import cv2
@ -19,7 +20,8 @@ from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ModelOp
from facefusion.vision import read_image, read_static_image, unpack_resolution, write_image from facefusion.vision import read_image, read_static_image, unpack_resolution, write_image
def create_model_set() -> ModelSet: @lru_cache(maxsize = None)
def create_static_model_set() -> ModelSet:
return\ return\
{ {
'ddcolor': 'ddcolor':
@ -138,7 +140,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
frame_colorizer_model = state_manager.get_item('frame_colorizer_model') frame_colorizer_model = state_manager.get_item('frame_colorizer_model')
return create_model_set().get(frame_colorizer_model) return create_static_model_set().get(frame_colorizer_model)
def register_args(program : ArgumentParser) -> None: def register_args(program : ArgumentParser) -> None:

6
facefusion/processors/modules/frame_enhancer.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser from argparse import ArgumentParser
from functools import lru_cache
from typing import List from typing import List
import cv2 import cv2
@ -19,7 +20,8 @@ from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ModelOp
from facefusion.vision import create_tile_frames, merge_tile_frames, read_image, read_static_image, write_image from facefusion.vision import create_tile_frames, merge_tile_frames, read_image, read_static_image, write_image
def create_model_set() -> ModelSet: @lru_cache(maxsize = None)
def create_static_model_set() -> ModelSet:
return\ return\
{ {
'clear_reality_x4': 'clear_reality_x4':
@ -395,7 +397,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
frame_enhancer_model = state_manager.get_item('frame_enhancer_model') frame_enhancer_model = state_manager.get_item('frame_enhancer_model')
return create_model_set().get(frame_enhancer_model) return create_static_model_set().get(frame_enhancer_model)
def register_args(program : ArgumentParser) -> None: def register_args(program : ArgumentParser) -> None:

6
facefusion/processors/modules/lip_syncer.py
View File

@ -1,4 +1,5 @@
from argparse import ArgumentParser from argparse import ArgumentParser
from functools import lru_cache
from typing import List from typing import List
import cv2 import cv2
@ -25,7 +26,8 @@ from facefusion.typing import ApplyStateItem, Args, AudioFrame, Face, InferenceP
from facefusion.vision import read_image, read_static_image, restrict_video_fps, write_image from facefusion.vision import read_image, read_static_image, restrict_video_fps, write_image
def create_model_set() -> ModelSet: @lru_cache(maxsize = None)
def create_static_model_set() -> ModelSet:
return\ return\
{ {
'wav2lip_96': 'wav2lip_96':
@ -84,7 +86,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
lip_syncer_model = state_manager.get_item('lip_syncer_model') lip_syncer_model = state_manager.get_item('lip_syncer_model')
return create_model_set().get(lip_syncer_model) return create_static_model_set().get(lip_syncer_model)
def register_args(program : ArgumentParser) -> None: def register_args(program : ArgumentParser) -> None:

2
facefusion/uis/components/download.py
View File

@ -4,6 +4,7 @@ import gradio
from facefusion import state_manager, wording from facefusion import state_manager, wording
from facefusion.choices import download_provider_set from facefusion.choices import download_provider_set
from facefusion.core import clear_model_sets
from facefusion.typing import DownloadProviderKey from facefusion.typing import DownloadProviderKey
DOWNLOAD_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None DOWNLOAD_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
@ -24,6 +25,7 @@ def listen() -> None:
def update_download_providers(download_providers : List[DownloadProviderKey]) -> gradio.CheckboxGroup: def update_download_providers(download_providers : List[DownloadProviderKey]) -> gradio.CheckboxGroup:
clear_model_sets()
download_providers = download_providers or list(download_provider_set.keys()) download_providers = download_providers or list(download_provider_set.keys())
state_manager.set_item('download_providers', download_providers) state_manager.set_item('download_providers', download_providers)
return gradio.CheckboxGroup(value = state_manager.get_item('download_providers')) return gradio.CheckboxGroup(value = state_manager.get_item('download_providers'))

36
facefusion/voice_extractor.py
View File

@ -1,3 +1,4 @@
from functools import lru_cache
from typing import Tuple from typing import Tuple
import numpy import numpy
@ -9,28 +10,31 @@ from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import thread_semaphore from facefusion.thread_helper import thread_semaphore
from facefusion.typing import Audio, AudioChunk, InferencePool, ModelOptions, ModelSet from facefusion.typing import Audio, AudioChunk, InferencePool, ModelOptions, ModelSet
MODEL_SET : ModelSet =\
{ @lru_cache(maxsize = None)
'kim_vocal_2': def create_static_model_set() -> ModelSet:
return\
{ {
'hashes': 'kim_vocal_2':
{ {
'voice_extractor': 'hashes':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/kim_vocal_2.hash', 'voice_extractor':
'path': resolve_relative_path('../.assets/models/kim_vocal_2.hash') {
} 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/kim_vocal_2.hash',
}, 'path': resolve_relative_path('../.assets/models/kim_vocal_2.hash')
'sources': }
{ },
'voice_extractor': 'sources':
{ {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/kim_vocal_2.onnx', 'voice_extractor':
'path': resolve_relative_path('../.assets/models/kim_vocal_2.onnx') {
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/kim_vocal_2.onnx',
'path': resolve_relative_path('../.assets/models/kim_vocal_2.onnx')
}
} }
} }
} }
}
def get_inference_pool() -> InferencePool: def get_inference_pool() -> InferencePool:
@ -43,7 +47,7 @@ def clear_inference_pool() -> None:
def get_model_options() -> ModelOptions: def get_model_options() -> ModelOptions:
return MODEL_SET.get('kim_vocal_2') return create_static_model_set().get('kim_vocal_2')
def pre_check() -> bool: def pre_check() -> bool: