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from facefusion import state_manager
from facefusion.filesystem import get_file_name, is_image, is_video, resolve_file_paths
from facefusion.jobs import job_store
from facefusion.normalizer import normalize_fps, normalize_padding
from facefusion.processors.core import get_processors_modules
from facefusion.types import ApplyStateItem, Args
from facefusion.vision import create_image_resolutions, create_video_resolutions, detect_image_resolution, detect_video_fps, detect_video_resolution, pack_resolution
def reduce_step_args(args : Args) -> Args:
step_args =\
{
key: args[key] for key in args if key in job_store.get_step_keys()
}
return step_args
def reduce_job_args(args : Args) -> Args:
job_args =\
{
key: args[key] for key in args if key in job_store.get_job_keys()
}
return job_args
def collect_step_args() -> Args:
step_args =\
{
key: state_manager.get_item(key) for key in job_store.get_step_keys() #type:ignore[arg-type]
}
return step_args
def collect_job_args() -> Args:
job_args =\
{
key: state_manager.get_item(key) for key in job_store.get_job_keys() #type:ignore[arg-type]
}
return job_args
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
# general
apply_state_item('command', args.get('command'))
# paths
apply_state_item('temp_path', args.get('temp_path'))
apply_state_item('jobs_path', args.get('jobs_path'))
apply_state_item('source_paths', args.get('source_paths'))
apply_state_item('target_path', args.get('target_path'))
apply_state_item('output_path', args.get('output_path'))
# patterns
apply_state_item('source_pattern', args.get('source_pattern'))
apply_state_item('target_pattern', args.get('target_pattern'))
apply_state_item('output_pattern', args.get('output_pattern'))
# face detector
apply_state_item('face_detector_model', args.get('face_detector_model'))
apply_state_item('face_detector_size', args.get('face_detector_size'))
apply_state_item('face_detector_angles', args.get('face_detector_angles'))
apply_state_item('face_detector_score', args.get('face_detector_score'))
# face landmarker
apply_state_item('face_landmarker_model', args.get('face_landmarker_model'))
apply_state_item('face_landmarker_score', args.get('face_landmarker_score'))
# face selector
apply_state_item('face_selector_mode', args.get('face_selector_mode'))
apply_state_item('face_selector_order', args.get('face_selector_order'))
apply_state_item('face_selector_age_start', args.get('face_selector_age_start'))
apply_state_item('face_selector_age_end', args.get('face_selector_age_end'))
apply_state_item('face_selector_gender', args.get('face_selector_gender'))
apply_state_item('face_selector_race', args.get('face_selector_race'))
apply_state_item('reference_face_position', args.get('reference_face_position'))
apply_state_item('reference_face_distance', args.get('reference_face_distance'))
apply_state_item('reference_frame_number', args.get('reference_frame_number'))
# face masker
apply_state_item('face_occluder_model', args.get('face_occluder_model'))
apply_state_item('face_parser_model', args.get('face_parser_model'))
apply_state_item('face_mask_types', args.get('face_mask_types'))
apply_state_item('face_mask_areas', args.get('face_mask_areas'))
apply_state_item('face_mask_regions', args.get('face_mask_regions'))
apply_state_item('face_mask_blur', args.get('face_mask_blur'))
apply_state_item('face_mask_padding', normalize_padding(args.get('face_mask_padding')))
# frame extraction
apply_state_item('trim_frame_start', args.get('trim_frame_start'))
apply_state_item('trim_frame_end', args.get('trim_frame_end'))
apply_state_item('temp_frame_format', args.get('temp_frame_format'))
apply_state_item('keep_temp', args.get('keep_temp'))
# output creation
apply_state_item('output_image_quality', args.get('output_image_quality'))
if is_image(args.get('target_path')):
output_image_resolution = detect_image_resolution(args.get('target_path'))
output_image_resolutions = create_image_resolutions(output_image_resolution)
if args.get('output_image_resolution') in output_image_resolutions:
apply_state_item('output_image_resolution', args.get('output_image_resolution'))
else:
apply_state_item('output_image_resolution', pack_resolution(output_image_resolution))
apply_state_item('output_audio_encoder', args.get('output_audio_encoder'))
apply_state_item('output_audio_quality', args.get('output_audio_quality'))
apply_state_item('output_audio_volume', args.get('output_audio_volume'))
apply_state_item('output_video_encoder', args.get('output_video_encoder'))
apply_state_item('output_video_preset', args.get('output_video_preset'))
apply_state_item('output_video_quality', args.get('output_video_quality'))
if is_video(args.get('target_path')):
output_video_resolution = detect_video_resolution(args.get('target_path'))
output_video_resolutions = create_video_resolutions(output_video_resolution)
if args.get('output_video_resolution') in output_video_resolutions:
apply_state_item('output_video_resolution', args.get('output_video_resolution'))
else:
apply_state_item('output_video_resolution', pack_resolution(output_video_resolution))
if args.get('output_video_fps') or is_video(args.get('target_path')):
output_video_fps = normalize_fps(args.get('output_video_fps')) or detect_video_fps(args.get('target_path'))
apply_state_item('output_video_fps', output_video_fps)
# processors
available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
apply_state_item('processors', args.get('processors'))
for processor_module in get_processors_modules(available_processors):
processor_module.apply_args(args, apply_state_item)
# uis
apply_state_item('open_browser', args.get('open_browser'))
apply_state_item('ui_layouts', args.get('ui_layouts'))
apply_state_item('ui_workflow', args.get('ui_workflow'))
# execution
apply_state_item('execution_device_id', args.get('execution_device_id'))
apply_state_item('execution_providers', args.get('execution_providers'))
apply_state_item('execution_thread_count', args.get('execution_thread_count'))
apply_state_item('execution_queue_count', args.get('execution_queue_count'))
# download
apply_state_item('download_providers', args.get('download_providers'))
apply_state_item('download_scope', args.get('download_scope'))
# benchmark
apply_state_item('benchmark_resolutions', args.get('benchmark_resolutions'))
apply_state_item('benchmark_cycle_count', args.get('benchmark_cycle_count'))
# memory
apply_state_item('video_memory_strategy', args.get('video_memory_strategy'))
apply_state_item('system_memory_limit', args.get('system_memory_limit'))
# misc
apply_state_item('log_level', args.get('log_level'))
apply_state_item('halt_on_error', args.get('halt_on_error'))
# jobs
apply_state_item('job_id', args.get('job_id'))
apply_state_item('job_status', args.get('job_status'))
apply_state_item('step_index', args.get('step_index'))
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