mirror of
https://github.com/facefusion/facefusion.git
synced 2026-07-27 12:30:55 +02:00
henryruhs
d88c1f6060
Two selectable workflows share one swap loop (process_target_frame): - disk (default): extract -> process -> merge; temp frames on disk, supports fps resampling, robust/inspectable. - stream: read -> swap -> encode over pipes; no temp frames, ~2x faster wall. Remove --keep-temp entirely (arg, config, state, ui, locale). Replace the dead cv2.VideoWriter in video_manager with an ffmpeg rawvideo encoder held in the pool (symmetric to the reader): get_video_writer / write_video_writer_frame / close_video_writer, with fix_video_encoder relocated to ffmpeg_builder. The encoder must be closed before processor post_process(), which clears the video pool and would otherwise SIGTERM the still-open writer. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
119 lines
6.0 KiB
Python
119 lines
6.0 KiB
Python
from facefusion import state_manager
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from facefusion.filesystem import get_file_name, is_video, resolve_file_paths
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from facefusion.jobs import job_store
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from facefusion.normalizer import normalize_fps, normalize_space
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from facefusion.processors.core import get_processors_modules
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from facefusion.types import ApplyStateItem, Args
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from facefusion.vision import detect_video_fps
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def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
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apply_state_item('command', args.get('command'))
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apply_state_item('temp_path', args.get('temp_path'))
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apply_state_item('jobs_path', args.get('jobs_path'))
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apply_state_item('source_paths', args.get('source_paths'))
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apply_state_item('target_path', args.get('target_path'))
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apply_state_item('output_path', args.get('output_path'))
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apply_state_item('source_pattern', args.get('source_pattern'))
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apply_state_item('target_pattern', args.get('target_pattern'))
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apply_state_item('output_pattern', args.get('output_pattern'))
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apply_state_item('face_detector_model', args.get('face_detector_model'))
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apply_state_item('face_detector_size', args.get('face_detector_size'))
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apply_state_item('face_detector_margin', normalize_space(args.get('face_detector_margin')))
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apply_state_item('face_detector_angles', args.get('face_detector_angles'))
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apply_state_item('face_detector_score', args.get('face_detector_score'))
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apply_state_item('face_landmarker_model', args.get('face_landmarker_model'))
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apply_state_item('face_landmarker_score', args.get('face_landmarker_score'))
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apply_state_item('face_selector_mode', args.get('face_selector_mode'))
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apply_state_item('face_selector_order', args.get('face_selector_order'))
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apply_state_item('face_selector_age_start', args.get('face_selector_age_start'))
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apply_state_item('face_selector_age_end', args.get('face_selector_age_end'))
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apply_state_item('face_selector_gender', args.get('face_selector_gender'))
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apply_state_item('face_selector_race', args.get('face_selector_race'))
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apply_state_item('reference_face_position', args.get('reference_face_position'))
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apply_state_item('reference_face_distance', args.get('reference_face_distance'))
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apply_state_item('reference_frame_number', args.get('reference_frame_number'))
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apply_state_item('face_tracker_score', args.get('face_tracker_score'))
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apply_state_item('face_occluder_model', args.get('face_occluder_model'))
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apply_state_item('face_parser_model', args.get('face_parser_model'))
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apply_state_item('face_mask_types', args.get('face_mask_types'))
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apply_state_item('face_mask_areas', args.get('face_mask_areas'))
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apply_state_item('face_mask_regions', args.get('face_mask_regions'))
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apply_state_item('face_mask_blur', args.get('face_mask_blur'))
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apply_state_item('face_mask_padding', normalize_space(args.get('face_mask_padding')))
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apply_state_item('voice_extractor_model', args.get('voice_extractor_model'))
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apply_state_item('trim_frame_start', args.get('trim_frame_start'))
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apply_state_item('trim_frame_end', args.get('trim_frame_end'))
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apply_state_item('temp_frame_format', args.get('temp_frame_format'))
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apply_state_item('workflow_mode', args.get('workflow_mode'))
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apply_state_item('target_frame_amount', args.get('target_frame_amount'))
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apply_state_item('output_image_quality', args.get('output_image_quality'))
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apply_state_item('output_image_scale', args.get('output_image_scale'))
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apply_state_item('output_audio_encoder', args.get('output_audio_encoder'))
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apply_state_item('output_audio_quality', args.get('output_audio_quality'))
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apply_state_item('output_audio_volume', args.get('output_audio_volume'))
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apply_state_item('output_video_encoder', args.get('output_video_encoder'))
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apply_state_item('output_video_preset', args.get('output_video_preset'))
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apply_state_item('output_video_quality', args.get('output_video_quality'))
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apply_state_item('output_video_scale', args.get('output_video_scale'))
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if args.get('output_video_fps') or is_video(args.get('target_path')):
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output_video_fps = normalize_fps(args.get('output_video_fps')) or detect_video_fps(args.get('target_path'))
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apply_state_item('output_video_fps', output_video_fps)
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available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
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apply_state_item('processors', args.get('processors'))
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for processor_module in get_processors_modules(available_processors):
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processor_module.apply_args(args, apply_state_item)
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apply_state_item('open_browser', args.get('open_browser'))
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apply_state_item('ui_layouts', args.get('ui_layouts'))
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apply_state_item('ui_workflow', args.get('ui_workflow'))
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apply_state_item('execution_device_ids', args.get('execution_device_ids'))
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apply_state_item('execution_providers', args.get('execution_providers'))
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apply_state_item('execution_thread_count', args.get('execution_thread_count'))
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apply_state_item('download_providers', args.get('download_providers'))
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apply_state_item('download_scope', args.get('download_scope'))
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apply_state_item('benchmark_mode', args.get('benchmark_mode'))
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apply_state_item('benchmark_resolutions', args.get('benchmark_resolutions'))
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apply_state_item('benchmark_cycle_count', args.get('benchmark_cycle_count'))
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apply_state_item('video_memory_strategy', args.get('video_memory_strategy'))
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apply_state_item('log_level', args.get('log_level'))
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apply_state_item('halt_on_error', args.get('halt_on_error'))
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apply_state_item('job_id', args.get('job_id'))
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apply_state_item('job_status', args.get('job_status'))
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apply_state_item('step_index', args.get('step_index'))
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def reduce_step_args(args : Args) -> Args:
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step_args =\
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{
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key: args[key] for key in args if key in job_store.get_step_keys()
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}
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return step_args
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def reduce_job_args(args : Args) -> Args:
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job_args =\
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{
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key: args[key] for key in args if key in job_store.get_job_keys()
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}
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return job_args
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def collect_step_args() -> Args:
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step_args =\
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{
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key: state_manager.get_item(key) for key in job_store.get_step_keys() #type:ignore[arg-type]
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}
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return step_args
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def collect_job_args() -> Args:
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job_args =\
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{
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key: state_manager.get_item(key) for key in job_store.get_job_keys() #type:ignore[arg-type]
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}
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return job_args
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