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from functools import lru_cache
from types import ModuleType
from typing import List
import cv2
import numpy
import facefusion.choices
import facefusion.jobs.job_manager
import facefusion.jobs.job_store
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, state_manager, translator, video_manager
from facefusion.common_helper import create_int_metavar, get_middle
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.face_creator import scale_face
from facefusion.face_helper import merge_matrix, paste_back, scale_face_landmark_5, warp_face_by_face_landmark_5
from facefusion.face_masker import create_box_mask, create_occlusion_mask
from facefusion.face_selector import select_faces
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
from facefusion.processors.modules.age_modifier import choices as age_modifier_choices
from facefusion.processors.modules.age_modifier.types import AgeModifierDirection, AgeModifierInputs
from facefusion.processors.types import ProcessorOutputs
from facefusion.program_helper import find_argument_group
from facefusion.thread_helper import thread_semaphore
from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, VisionFrame
from facefusion.vision import match_frame_color, read_static_image, read_static_video_frame
@lru_cache()
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'fran':
{
'__metadata__':
{
'vendor': 'ry-lu',
'license': 'mit',
'year': 2024
},
'hashes':
{
'age_modifier':
{
'url': resolve_download_url('models-3.6.0', 'fran.hash'),
'path': resolve_relative_path('../.assets/models/fran.hash')
}
},
'sources':
{
'age_modifier':
{
'url': resolve_download_url('models-3.6.0', 'fran.onnx'),
'path': resolve_relative_path('../.assets/models/fran.onnx')
}
},
'templates':
{
'target': 'ffhq_512',
},
'sizes':
{
'target': (1024, 1024),
},
'mean': [ 0.0, 0.0, 0.0 ],
'standard_deviation': [ 1.0, 1.0, 1.0 ]
},
'styleganex_age':
{
'__metadata__':
{
'vendor': 'williamyang1991',
'license': 'S-Lab-1.0',
'year': 2023
},
'hashes':
{
'age_modifier':
{
'url': resolve_download_url('models-3.1.0', 'styleganex_age.hash'),
'path': resolve_relative_path('../.assets/models/styleganex_age.hash')
}
},
'sources':
{
'age_modifier':
{
'url': resolve_download_url('models-3.1.0', 'styleganex_age.onnx'),
'path': resolve_relative_path('../.assets/models/styleganex_age.onnx')
}
},
'templates':
{
'target': 'ffhq_512',
'target_with_background': 'styleganex_384'
},
'sizes':
{
'target': (256, 256),
'target_with_background': (384, 384)
},
'mean': [ 0.5, 0.5, 0.5 ],
'standard_deviation': [ 0.5, 0.5, 0.5 ]
}
}
def get_inference_pool() -> InferencePool:
model_names = [ state_manager.get_item('age_modifier_model') ]
model_source_set = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
model_names = [ state_manager.get_item('age_modifier_model') ]
inference_manager.clear_inference_pool(__name__, model_names)
def get_model_options() -> ModelOptions:
model_name = state_manager.get_item('age_modifier_model')
return create_static_model_set('full').get(model_name)
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--age-modifier-model', help = translator.get('help.model', __package__), default = config.get_str_value('processors', 'age_modifier_model', 'fran'), choices = age_modifier_choices.age_modifier_models)
group_processors.add_argument('--age-modifier-direction', help = translator.get('help.direction', __package__), type = int, default = config.get_int_value('processors', 'age_modifier_direction', '0'), choices = age_modifier_choices.age_modifier_direction_range, metavar = create_int_metavar(age_modifier_choices.age_modifier_direction_range))
facefusion.jobs.job_store.register_step_keys([ 'age_modifier_model', 'age_modifier_direction' ])
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
apply_state_item('age_modifier_model', args.get('age_modifier_model'))
apply_state_item('age_modifier_direction', args.get('age_modifier_direction'))
def get_common_modules() -> List[ModuleType]:
return [ content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer ]
def pre_check() -> bool:
model_hash_set = get_model_options().get('hashes')
model_source_set = get_model_options().get('sources')
for common_module in get_common_modules():
if not common_module.pre_check():
return False
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def pre_process(mode : ProcessMode) -> bool:
if mode in [ 'output', 'preview' ] and not is_image(state_manager.get_item('target_path')) and not is_video(state_manager.get_item('target_path')):
logger.error(translator.get('choose_image_or_video_target') + translator.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(translator.get('specify_image_or_video_output') + translator.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(translator.get('match_target_and_output_extension') + translator.get('exclamation_mark'), __name__)
return False
return True
def post_process() -> None:
read_static_image.cache_clear()
read_static_video_frame.cache_clear()
video_manager.clear_video_pool()
if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]:
clear_inference_pool()
if state_manager.get_item('video_memory_strategy') == 'strict':
for common_module in get_common_modules():
common_module.clear_inference_pool()
def modify_age(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
model_templates = get_model_options().get('templates')
model_sizes = get_model_options().get('sizes')
face_landmark_5 = target_face.landmark_set.get('5/68').copy()
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5, model_templates.get('target'), model_sizes.get('target'))
if state_manager.get_item('age_modifier_model') == 'fran':
box_mask = create_box_mask(crop_vision_frame, state_manager.get_item('face_mask_blur'), (0, 0, 0, 0))
crop_masks =\
[
box_mask
]
if 'occlusion' in state_manager.get_item('face_mask_types'):
occlusion_mask = create_occlusion_mask(crop_vision_frame)
crop_masks.append(occlusion_mask)
crop_vision_frame = prepare_vision_frame(crop_vision_frame)
target_age = numpy.mean(target_face.age)
age_modifier_direction = numpy.array([ target_age, target_age + state_manager.get_item('age_modifier_direction') ], dtype = numpy.float32) / 100
age_modifier_direction = age_modifier_direction.clip(0, 1)
crop_vision_frame = forward(crop_vision_frame, crop_vision_frame, age_modifier_direction)
crop_vision_frame = normalize_vision_frame(crop_vision_frame)
crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
paste_vision_frame = paste_back(temp_vision_frame, crop_vision_frame, crop_mask, affine_matrix)
return paste_vision_frame
if state_manager.get_item('age_modifier_model') == 'styleganex_age':
extend_face_landmark_5 = scale_face_landmark_5(face_landmark_5, 0.875)
extend_vision_frame, extend_affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, extend_face_landmark_5, model_templates.get('target_with_background'), model_sizes.get('target_with_background'))
extend_vision_frame_raw = extend_vision_frame.copy()
box_mask = create_box_mask(extend_vision_frame, state_manager.get_item('face_mask_blur'), (0, 0, 0, 0))
crop_masks =\
[
box_mask
]
if 'occlusion' in state_manager.get_item('face_mask_types'):
occlusion_mask = create_occlusion_mask(crop_vision_frame)
temp_matrix = merge_matrix([ extend_affine_matrix, cv2.invertAffineTransform(affine_matrix) ])
occlusion_mask = cv2.warpAffine(occlusion_mask, temp_matrix, model_sizes.get('target_with_background'))
crop_masks.append(occlusion_mask)
crop_vision_frame = prepare_vision_frame(crop_vision_frame)
extend_vision_frame = prepare_vision_frame(extend_vision_frame)
age_modifier_direction = numpy.array(numpy.interp(state_manager.get_item('age_modifier_direction'), [ -100, 100 ], [ 2.5, -2.5 ])).astype(numpy.float32)
extend_vision_frame = forward(crop_vision_frame, extend_vision_frame, age_modifier_direction)
extend_vision_frame = normalize_extend_frame(extend_vision_frame)
extend_vision_frame = match_frame_color(extend_vision_frame_raw, extend_vision_frame)
extend_affine_matrix *= (model_sizes.get('target')[0] * 4) / model_sizes.get('target_with_background')[0]
crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
crop_mask = cv2.resize(crop_mask, (model_sizes.get('target')[0] * 4, model_sizes.get('target')[1] * 4))
paste_vision_frame = paste_back(temp_vision_frame, extend_vision_frame, crop_mask, extend_affine_matrix)
return paste_vision_frame
return temp_vision_frame
def forward(crop_vision_frame : VisionFrame, extend_vision_frame : VisionFrame, age_modifier_direction : AgeModifierDirection) -> VisionFrame:
age_modifier = get_inference_pool().get('age_modifier')
age_modifier_inputs = {}
for age_modifier_input in age_modifier.get_inputs():
if age_modifier_input.name == 'target':
age_modifier_inputs[age_modifier_input.name] = crop_vision_frame
if age_modifier_input.name == 'target_with_background':
age_modifier_inputs[age_modifier_input.name] = extend_vision_frame
if age_modifier_input.name == 'direction':
age_modifier_inputs[age_modifier_input.name] = age_modifier_direction
with thread_semaphore():
crop_vision_frame = age_modifier.run(None, age_modifier_inputs)[0][0]
return crop_vision_frame
def prepare_vision_frame(vision_frame : VisionFrame) -> VisionFrame:
model_mean = get_model_options().get('mean')
model_standard_deviation = get_model_options().get('standard_deviation')
vision_frame = vision_frame[:, :, ::-1] / 255.0
vision_frame = (vision_frame - model_mean) / model_standard_deviation
vision_frame = numpy.expand_dims(vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
return vision_frame
def normalize_vision_frame(vision_frame : VisionFrame) -> VisionFrame:
model_mean = get_model_options().get('mean')
model_standard_deviation = get_model_options().get('standard_deviation')
vision_frame = vision_frame.transpose(1, 2, 0)
vision_frame = vision_frame * model_standard_deviation + model_mean
vision_frame = vision_frame.clip(0, 1)
vision_frame = vision_frame[:, :, ::-1] * 255
return vision_frame
def normalize_extend_frame(extend_vision_frame : VisionFrame) -> VisionFrame:
model_sizes = get_model_options().get('sizes')
extend_vision_frame = numpy.clip(extend_vision_frame, -1, 1)
extend_vision_frame = (extend_vision_frame + 1) / 2
extend_vision_frame = extend_vision_frame.transpose(1, 2, 0).clip(0, 255)
extend_vision_frame = (extend_vision_frame * 255.0)
extend_vision_frame = extend_vision_frame.astype(numpy.uint8)[:, :, ::-1]
extend_vision_frame = cv2.resize(extend_vision_frame, (model_sizes.get('target')[0] * 4, model_sizes.get('target')[1] * 4), interpolation = cv2.INTER_AREA)
return extend_vision_frame
def process_frame(inputs : AgeModifierInputs) -> ProcessorOutputs:
reference_vision_frame = inputs.get('reference_vision_frame')
source_vision_frames = inputs.get('source_vision_frames')
target_vision_frames = inputs.get('target_vision_frames')
temp_vision_frame = inputs.get('temp_vision_frame')
temp_vision_mask = inputs.get('temp_vision_mask')
target_vision_frame = get_middle(target_vision_frames)
target_faces = select_faces(reference_vision_frame, source_vision_frames, target_vision_frames)
if target_faces:
for target_face in target_faces:
target_face = scale_face(target_face, target_vision_frame, temp_vision_frame)
temp_vision_frame = modify_age(target_face, temp_vision_frame)
return temp_vision_frame, temp_vision_mask
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