mirror of
https://github.com/facefusion/facefusion.git
synced 2026-05-14 11:02:09 +02:00
Patch 3.5.4 (#1055)
* remove insecure flag from curl * eleminate repating definitons * limit processors and ui layouts by choices * follow couple of v4 standards * use more secure mkstemp * dynamic cache path for execution providers * fix benchmarker, prevent path traveling via job-id * fix order in execution provider choices * resort by prioroty * introduce support for QNN * close file description for Windows to stop crying * prevent ConnectionResetError under windows * needed for nested .caches directory as onnxruntime does not create it * different approach to silent asyncio * update dependencies * simplify the name to just inference providers * switch to trt_builder_optimization_level 4
This commit is contained in:
Henry Ruhs
+78
-92
@@ -7,6 +7,84 @@ 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_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('keep_temp', args.get('keep_temp'))
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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('system_memory_limit', args.get('system_memory_limit'))
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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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@@ -37,95 +115,3 @@ def collect_job_args() -> Args:
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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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def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
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# general
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apply_state_item('command', args.get('command'))
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# paths
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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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# patterns
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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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# face detector
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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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# face landmarker
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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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# face selector
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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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# face masker
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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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# voice extractor
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apply_state_item('voice_extractor_model', args.get('voice_extractor_model'))
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# frame extraction
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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('keep_temp', args.get('keep_temp'))
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# output creation
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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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# processors
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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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# uis
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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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# execution
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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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# download
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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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# benchmark
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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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# memory
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apply_state_item('video_memory_strategy', args.get('video_memory_strategy'))
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apply_state_item('system_memory_limit', args.get('system_memory_limit'))
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# misc
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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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# jobs
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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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@@ -89,7 +89,7 @@ def cycle(cycle_count : int) -> BenchmarkCycleSet:
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def suggest_output_path(target_path : str) -> str:
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target_file_extension = get_file_extension(target_path)
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return os.path.join(tempfile.gettempdir(), hashlib.sha1().hexdigest()[:8] + target_file_extension)
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return os.path.join(tempfile.gettempdir(), hashlib.sha1(target_path.encode()).hexdigest() + target_file_extension)
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def render() -> None:
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+34
-33
@@ -1,5 +1,5 @@
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import logging
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from typing import List, Sequence
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from typing import List, Sequence, get_args
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from facefusion.common_helper import create_float_range, create_int_range
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from facefusion.types import Angle, AudioEncoder, AudioFormat, AudioTypeSet, BenchmarkMode, BenchmarkResolution, BenchmarkSet, DownloadProvider, DownloadProviderSet, DownloadScope, EncoderSet, ExecutionProvider, ExecutionProviderSet, FaceDetectorModel, FaceDetectorSet, FaceLandmarkerModel, FaceMaskArea, FaceMaskAreaSet, FaceMaskRegion, FaceMaskRegionSet, FaceMaskType, FaceOccluderModel, FaceParserModel, FaceSelectorMode, FaceSelectorOrder, Gender, ImageFormat, ImageTypeSet, JobStatus, LogLevel, LogLevelSet, Race, Score, TempFrameFormat, UiWorkflow, VideoEncoder, VideoFormat, VideoMemoryStrategy, VideoPreset, VideoTypeSet, VoiceExtractorModel
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@@ -12,15 +12,15 @@ face_detector_set : FaceDetectorSet =\
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'yolo_face': [ '640x640' ],
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'yunet': [ '640x640' ]
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}
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face_detector_models : List[FaceDetectorModel] = list(face_detector_set.keys())
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face_landmarker_models : List[FaceLandmarkerModel] = [ 'many', '2dfan4', 'peppa_wutz' ]
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face_selector_modes : List[FaceSelectorMode] = [ 'many', 'one', 'reference' ]
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face_selector_orders : List[FaceSelectorOrder] = [ 'left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best' ]
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face_selector_genders : List[Gender] = [ 'female', 'male' ]
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face_selector_races : List[Race] = [ 'white', 'black', 'latino', 'asian', 'indian', 'arabic' ]
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face_occluder_models : List[FaceOccluderModel] = [ 'many', 'xseg_1', 'xseg_2', 'xseg_3' ]
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face_parser_models : List[FaceParserModel] = [ 'bisenet_resnet_18', 'bisenet_resnet_34' ]
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face_mask_types : List[FaceMaskType] = [ 'box', 'occlusion', 'area', 'region' ]
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face_detector_models : List[FaceDetectorModel] = list(get_args(FaceDetectorModel))
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face_landmarker_models : List[FaceLandmarkerModel] = list(get_args(FaceLandmarkerModel))
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face_selector_modes : List[FaceSelectorMode] = list(get_args(FaceSelectorMode))
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face_selector_orders : List[FaceSelectorOrder] = list(get_args(FaceSelectorOrder))
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face_selector_genders : List[Gender] = list(get_args(Gender))
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face_selector_races : List[Race] = list(get_args(Race))
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face_occluder_models : List[FaceOccluderModel] = list(get_args(FaceOccluderModel))
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face_parser_models : List[FaceParserModel] = list(get_args(FaceParserModel))
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face_mask_types : List[FaceMaskType] = list(get_args(FaceMaskType))
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face_mask_area_set : FaceMaskAreaSet =\
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{
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'upper-face': [ 0, 1, 2, 31, 32, 33, 34, 35, 14, 15, 16, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17 ],
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@@ -40,10 +40,10 @@ face_mask_region_set : FaceMaskRegionSet =\
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'upper-lip': 12,
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'lower-lip': 13
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}
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face_mask_areas : List[FaceMaskArea] = list(face_mask_area_set.keys())
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face_mask_regions : List[FaceMaskRegion] = list(face_mask_region_set.keys())
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face_mask_areas : List[FaceMaskArea] = list(get_args(FaceMaskArea))
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face_mask_regions : List[FaceMaskRegion] = list(get_args(FaceMaskRegion))
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voice_extractor_models : List[VoiceExtractorModel] = [ 'kim_vocal_1', 'kim_vocal_2', 'uvr_mdxnet' ]
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voice_extractor_models : List[VoiceExtractorModel] = list(get_args(VoiceExtractorModel))
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audio_type_set : AudioTypeSet =\
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{
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@@ -74,21 +74,21 @@ video_type_set : VideoTypeSet =\
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'webm': 'video/webm',
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'wmv': 'video/x-ms-wmv'
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}
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audio_formats : List[AudioFormat] = list(audio_type_set.keys())
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image_formats : List[ImageFormat] = list(image_type_set.keys())
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video_formats : List[VideoFormat] = list(video_type_set.keys())
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temp_frame_formats : List[TempFrameFormat] = [ 'bmp', 'jpeg', 'png', 'tiff' ]
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audio_formats : List[AudioFormat] = list(get_args(AudioFormat))
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image_formats : List[ImageFormat] = list(get_args(ImageFormat))
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video_formats : List[VideoFormat] = list(get_args(VideoFormat))
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temp_frame_formats : List[TempFrameFormat] = list(get_args(TempFrameFormat))
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output_audio_encoders : List[AudioEncoder] = list(get_args(AudioEncoder))
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output_video_encoders : List[VideoEncoder] = list(get_args(VideoEncoder))
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output_encoder_set : EncoderSet =\
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{
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'audio': [ 'flac', 'aac', 'libmp3lame', 'libopus', 'libvorbis', 'pcm_s16le', 'pcm_s32le' ],
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'video': [ 'libx264', 'libx264rgb', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf', 'h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox', 'rawvideo' ]
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'audio': output_audio_encoders,
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'video': output_video_encoders
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}
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output_audio_encoders : List[AudioEncoder] = output_encoder_set.get('audio')
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output_video_encoders : List[VideoEncoder] = output_encoder_set.get('video')
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output_video_presets : List[VideoPreset] = [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow' ]
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output_video_presets : List[VideoPreset] = list(get_args(VideoPreset))
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benchmark_modes : List[BenchmarkMode] = [ 'warm', 'cold' ]
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benchmark_modes : List[BenchmarkMode] = list(get_args(BenchmarkMode))
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benchmark_set : BenchmarkSet =\
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{
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'240p': '.assets/examples/target-240p.mp4',
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@@ -99,20 +99,21 @@ benchmark_set : BenchmarkSet =\
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'1440p': '.assets/examples/target-1440p.mp4',
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'2160p': '.assets/examples/target-2160p.mp4'
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}
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benchmark_resolutions : List[BenchmarkResolution] = list(benchmark_set.keys())
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benchmark_resolutions : List[BenchmarkResolution] = list(get_args(BenchmarkResolution))
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execution_provider_set : ExecutionProviderSet =\
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{
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'cuda': 'CUDAExecutionProvider',
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'tensorrt': 'TensorrtExecutionProvider',
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'directml': 'DmlExecutionProvider',
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'rocm': 'ROCMExecutionProvider',
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'migraphx': 'MIGraphXExecutionProvider',
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'openvino': 'OpenVINOExecutionProvider',
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'coreml': 'CoreMLExecutionProvider',
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'openvino': 'OpenVINOExecutionProvider',
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'qnn': 'QNNExecutionProvider',
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'directml': 'DmlExecutionProvider',
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'cpu': 'CPUExecutionProvider'
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}
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execution_providers : List[ExecutionProvider] = list(execution_provider_set.keys())
|
||||
execution_providers : List[ExecutionProvider] = list(get_args(ExecutionProvider))
|
||||
download_provider_set : DownloadProviderSet =\
|
||||
{
|
||||
'github':
|
||||
@@ -133,10 +134,10 @@ download_provider_set : DownloadProviderSet =\
|
||||
'path': '/facefusion/{base_name}/resolve/main/{file_name}'
|
||||
}
|
||||
}
|
||||
download_providers : List[DownloadProvider] = list(download_provider_set.keys())
|
||||
download_scopes : List[DownloadScope] = [ 'lite', 'full' ]
|
||||
download_providers : List[DownloadProvider] = list(get_args(DownloadProvider))
|
||||
download_scopes : List[DownloadScope] = list(get_args(DownloadScope))
|
||||
|
||||
video_memory_strategies : List[VideoMemoryStrategy] = [ 'strict', 'moderate', 'tolerant' ]
|
||||
video_memory_strategies : List[VideoMemoryStrategy] = list(get_args(VideoMemoryStrategy))
|
||||
|
||||
log_level_set : LogLevelSet =\
|
||||
{
|
||||
@@ -145,10 +146,10 @@ log_level_set : LogLevelSet =\
|
||||
'info': logging.INFO,
|
||||
'debug': logging.DEBUG
|
||||
}
|
||||
log_levels : List[LogLevel] = list(log_level_set.keys())
|
||||
log_levels : List[LogLevel] = list(get_args(LogLevel))
|
||||
|
||||
ui_workflows : List[UiWorkflow] = [ 'instant_runner', 'job_runner', 'job_manager' ]
|
||||
job_statuses : List[JobStatus] = [ 'drafted', 'queued', 'completed', 'failed' ]
|
||||
ui_workflows : List[UiWorkflow] = list(get_args(UiWorkflow))
|
||||
job_statuses : List[JobStatus] = list(get_args(JobStatus))
|
||||
|
||||
benchmark_cycle_count_range : Sequence[int] = create_int_range(1, 10, 1)
|
||||
execution_thread_count_range : Sequence[int] = create_int_range(1, 32, 1)
|
||||
|
||||
@@ -9,7 +9,7 @@ from facefusion.types import Command
|
||||
def run(commands : List[Command]) -> List[Command]:
|
||||
user_agent = metadata.get('name') + '/' + metadata.get('version')
|
||||
|
||||
return [ shutil.which('curl'), '--user-agent', user_agent, '--insecure', '--location', '--silent' ] + commands
|
||||
return [ shutil.which('curl'), '--user-agent', user_agent, '--location', '--silent' ] + commands
|
||||
|
||||
|
||||
def chain(*commands : List[Command]) -> List[Command]:
|
||||
|
||||
+61
-27
@@ -1,15 +1,17 @@
|
||||
import os
|
||||
import shutil
|
||||
import subprocess
|
||||
import xml.etree.ElementTree as ElementTree
|
||||
from functools import lru_cache
|
||||
from typing import List, Optional
|
||||
|
||||
from onnxruntime import get_available_providers, set_default_logger_severity
|
||||
import onnxruntime
|
||||
|
||||
import facefusion.choices
|
||||
from facefusion.types import ExecutionDevice, ExecutionProvider, InferenceSessionProvider, ValueAndUnit
|
||||
from facefusion.filesystem import create_directory, is_directory
|
||||
from facefusion.types import ExecutionDevice, ExecutionProvider, InferenceOptionSet, InferenceProvider, ValueAndUnit
|
||||
|
||||
set_default_logger_severity(3)
|
||||
onnxruntime.set_default_logger_severity(3)
|
||||
|
||||
|
||||
def has_execution_provider(execution_provider : ExecutionProvider) -> bool:
|
||||
@@ -17,7 +19,7 @@ def has_execution_provider(execution_provider : ExecutionProvider) -> bool:
|
||||
|
||||
|
||||
def get_available_execution_providers() -> List[ExecutionProvider]:
|
||||
inference_session_providers = get_available_providers()
|
||||
inference_session_providers = onnxruntime.get_available_providers()
|
||||
available_execution_providers : List[ExecutionProvider] = []
|
||||
|
||||
for execution_provider, execution_provider_value in facefusion.choices.execution_provider_set.items():
|
||||
@@ -28,54 +30,86 @@ def get_available_execution_providers() -> List[ExecutionProvider]:
|
||||
return available_execution_providers
|
||||
|
||||
|
||||
def create_inference_session_providers(execution_device_id : int, execution_providers : List[ExecutionProvider]) -> List[InferenceSessionProvider]:
|
||||
inference_session_providers : List[InferenceSessionProvider] = []
|
||||
def create_inference_providers(execution_device_id : int, execution_providers : List[ExecutionProvider]) -> List[InferenceProvider]:
|
||||
inference_providers : List[InferenceProvider] = []
|
||||
cache_path = resolve_cache_path()
|
||||
|
||||
for execution_provider in execution_providers:
|
||||
if execution_provider == 'cuda':
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
{
|
||||
'device_id': execution_device_id,
|
||||
'cudnn_conv_algo_search': resolve_cudnn_conv_algo_search()
|
||||
}))
|
||||
|
||||
if execution_provider == 'tensorrt':
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
inference_option_set : InferenceOptionSet =\
|
||||
{
|
||||
'device_id': execution_device_id,
|
||||
'trt_engine_cache_enable': True,
|
||||
'trt_engine_cache_path': '.caches',
|
||||
'trt_timing_cache_enable': True,
|
||||
'trt_timing_cache_path': '.caches',
|
||||
'trt_builder_optimization_level': 5
|
||||
}))
|
||||
'device_id': execution_device_id
|
||||
}
|
||||
if is_directory(cache_path) or create_directory(cache_path):
|
||||
inference_option_set.update(
|
||||
{
|
||||
'trt_engine_cache_enable': True,
|
||||
'trt_engine_cache_path': cache_path,
|
||||
'trt_timing_cache_enable': True,
|
||||
'trt_timing_cache_path': cache_path,
|
||||
'trt_builder_optimization_level': 4
|
||||
})
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider), inference_option_set))
|
||||
|
||||
if execution_provider in [ 'directml', 'rocm' ]:
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
{
|
||||
'device_id': execution_device_id
|
||||
}))
|
||||
|
||||
if execution_provider == 'migraphx':
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
inference_option_set =\
|
||||
{
|
||||
'device_id': execution_device_id,
|
||||
'migraphx_model_cache_dir': '.caches'
|
||||
}))
|
||||
'device_id': execution_device_id
|
||||
}
|
||||
if is_directory(cache_path) or create_directory(cache_path):
|
||||
inference_option_set.update(
|
||||
{
|
||||
'migraphx_model_cache_dir': cache_path
|
||||
})
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider), inference_option_set))
|
||||
|
||||
if execution_provider == 'coreml':
|
||||
inference_option_set =\
|
||||
{
|
||||
'SpecializationStrategy': 'FastPrediction'
|
||||
}
|
||||
if is_directory(cache_path) or create_directory(cache_path):
|
||||
inference_option_set.update(
|
||||
{
|
||||
'ModelCacheDirectory': cache_path
|
||||
})
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider), inference_option_set))
|
||||
|
||||
if execution_provider == 'openvino':
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
{
|
||||
'device_type': resolve_openvino_device_type(execution_device_id),
|
||||
'precision': 'FP32'
|
||||
}))
|
||||
if execution_provider == 'coreml':
|
||||
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
|
||||
if execution_provider == 'qnn':
|
||||
inference_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
|
||||
{
|
||||
'SpecializationStrategy': 'FastPrediction',
|
||||
'ModelCacheDirectory': '.caches'
|
||||
'device_id': execution_device_id,
|
||||
'backend_type': 'htp'
|
||||
}))
|
||||
|
||||
if 'cpu' in execution_providers:
|
||||
inference_session_providers.append(facefusion.choices.execution_provider_set.get('cpu'))
|
||||
inference_providers.append(facefusion.choices.execution_provider_set.get('cpu'))
|
||||
|
||||
return inference_session_providers
|
||||
return inference_providers
|
||||
|
||||
|
||||
def resolve_cache_path() -> str:
|
||||
return os.path.join('.caches', onnxruntime.get_version_string())
|
||||
|
||||
|
||||
def resolve_cudnn_conv_algo_search() -> str:
|
||||
|
||||
@@ -233,7 +233,7 @@ def create_area_mask(crop_vision_frame : VisionFrame, face_landmark_68 : FaceLan
|
||||
|
||||
convex_hull = cv2.convexHull(face_landmark_68[landmark_points].astype(numpy.int32))
|
||||
area_mask = numpy.zeros(crop_size).astype(numpy.float32)
|
||||
cv2.fillConvexPoly(area_mask, convex_hull, 1.0) # type: ignore[call-overload]
|
||||
cv2.fillConvexPoly(area_mask, convex_hull, 1.0) #type:ignore[call-overload]
|
||||
area_mask = (cv2.GaussianBlur(area_mask.clip(0, 1), (0, 0), 5).clip(0.5, 1) - 0.5) * 2
|
||||
return area_mask
|
||||
|
||||
|
||||
@@ -244,7 +244,8 @@ def merge_video(target_path : str, temp_video_fps : Fps, output_video_resolution
|
||||
|
||||
|
||||
def concat_video(output_path : str, temp_output_paths : List[str]) -> bool:
|
||||
concat_video_path = tempfile.mktemp()
|
||||
file_descriptor, concat_video_path = tempfile.mkstemp()
|
||||
os.close(file_descriptor)
|
||||
|
||||
with open(concat_video_path, 'w') as concat_video_file:
|
||||
for temp_output_path in temp_output_paths:
|
||||
|
||||
@@ -8,7 +8,7 @@ from onnxruntime import InferenceSession
|
||||
from facefusion import logger, process_manager, state_manager, translator
|
||||
from facefusion.app_context import detect_app_context
|
||||
from facefusion.common_helper import is_windows
|
||||
from facefusion.execution import create_inference_session_providers, has_execution_provider
|
||||
from facefusion.execution import create_inference_providers, has_execution_provider
|
||||
from facefusion.exit_helper import fatal_exit
|
||||
from facefusion.filesystem import get_file_name, is_file
|
||||
from facefusion.time_helper import calculate_end_time
|
||||
@@ -72,8 +72,8 @@ def create_inference_session(model_path : str, execution_device_id : int, execut
|
||||
start_time = time()
|
||||
|
||||
try:
|
||||
inference_session_providers = create_inference_session_providers(execution_device_id, execution_providers)
|
||||
inference_session = InferenceSession(model_path, providers = inference_session_providers)
|
||||
inference_providers = create_inference_providers(execution_device_id, execution_providers)
|
||||
inference_session = InferenceSession(model_path, providers = inference_providers)
|
||||
logger.debug(translator.get('loading_model_succeeded').format(model_name = model_file_name, seconds = calculate_end_time(start_time)), __name__)
|
||||
return inference_session
|
||||
|
||||
|
||||
@@ -22,12 +22,13 @@ ONNXRUNTIME_SET =\
|
||||
'default': ('onnxruntime', '1.24.1')
|
||||
}
|
||||
if is_windows() or is_linux():
|
||||
ONNXRUNTIME_SET['cuda'] = ('onnxruntime-gpu', '1.24.1')
|
||||
ONNXRUNTIME_SET['openvino'] = ('onnxruntime-openvino', '1.23.0')
|
||||
ONNXRUNTIME_SET['cuda'] = ('onnxruntime-gpu', '1.24.3')
|
||||
ONNXRUNTIME_SET['openvino'] = ('onnxruntime-openvino', '1.24.1')
|
||||
if is_windows():
|
||||
ONNXRUNTIME_SET['directml'] = ('onnxruntime-directml', '1.24.1')
|
||||
ONNXRUNTIME_SET['directml'] = ('onnxruntime-directml', '1.24.3')
|
||||
ONNXRUNTIME_SET['qnn'] = ('onnxruntime-qnn', '1.24.3')
|
||||
if is_linux():
|
||||
ONNXRUNTIME_SET['migraphx'] = ('onnxruntime-migraphx', '1.23.2')
|
||||
ONNXRUNTIME_SET['migraphx'] = ('onnxruntime-migraphx', '1.24.2')
|
||||
ONNXRUNTIME_SET['rocm'] = ('onnxruntime-rocm', '1.22.2.post1')
|
||||
|
||||
|
||||
|
||||
@@ -4,7 +4,7 @@ METADATA =\
|
||||
{
|
||||
'name': 'FaceFusion',
|
||||
'description': 'Industry leading face manipulation platform',
|
||||
'version': '3.5.3',
|
||||
'version': '3.5.4',
|
||||
'license': 'OpenRAIL-AS',
|
||||
'author': 'Henry Ruhs',
|
||||
'url': 'https://facefusion.io'
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_int_range
|
||||
from facefusion.processors.modules.age_modifier.types import AgeModifierModel
|
||||
|
||||
age_modifier_models : List[AgeModifierModel] = [ 'styleganex_age' ]
|
||||
age_modifier_models : List[AgeModifierModel] = list(get_args(AgeModifierModel))
|
||||
|
||||
age_modifier_direction_range : Sequence[int] = create_int_range(-100, 100, 1)
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_int_range
|
||||
from facefusion.processors.modules.background_remover.types import BackgroundRemoverModel
|
||||
|
||||
background_remover_models : List[BackgroundRemoverModel] = [ 'ben_2', 'birefnet_general', 'birefnet_portrait', 'isnet_general', 'modnet', 'ormbg', 'rmbg_1.4', 'rmbg_2.0', 'silueta', 'u2net_cloth', 'u2net_general', 'u2net_human', 'u2netp' ]
|
||||
background_remover_models : List[BackgroundRemoverModel] = list(get_args(BackgroundRemoverModel))
|
||||
|
||||
background_remover_color_range : Sequence[int] = create_int_range(0, 255, 1)
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_int_range
|
||||
from facefusion.processors.modules.expression_restorer.types import ExpressionRestorerArea, ExpressionRestorerModel
|
||||
|
||||
expression_restorer_models : List[ExpressionRestorerModel] = [ 'live_portrait' ]
|
||||
expression_restorer_models : List[ExpressionRestorerModel] = list(get_args(ExpressionRestorerModel))
|
||||
|
||||
expression_restorer_areas : List[ExpressionRestorerArea] = [ 'upper-face', 'lower-face' ]
|
||||
expression_restorer_areas : List[ExpressionRestorerArea] = list(get_args(ExpressionRestorerArea))
|
||||
|
||||
expression_restorer_factor_range : Sequence[int] = create_int_range(0, 100, 1)
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
from typing import List
|
||||
from typing import List, get_args
|
||||
|
||||
from facefusion.processors.modules.face_debugger.types import FaceDebuggerItem
|
||||
|
||||
face_debugger_items : List[FaceDebuggerItem] = [ 'bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-landmark-68/5', 'face-mask' ]
|
||||
face_debugger_items : List[FaceDebuggerItem] = list(get_args(FaceDebuggerItem))
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_float_range
|
||||
from facefusion.processors.modules.face_editor.types import FaceEditorModel
|
||||
|
||||
face_editor_models : List[FaceEditorModel] = [ 'live_portrait' ]
|
||||
face_editor_models : List[FaceEditorModel] = list(get_args(FaceEditorModel))
|
||||
|
||||
face_editor_eyebrow_direction_range : Sequence[float] = create_float_range(-1.0, 1.0, 0.05)
|
||||
face_editor_eye_gaze_horizontal_range : Sequence[float] = create_float_range(-1.0, 1.0, 0.05)
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_float_range, create_int_range
|
||||
from facefusion.processors.modules.face_enhancer.types import FaceEnhancerModel
|
||||
|
||||
face_enhancer_models : List[FaceEnhancerModel] = [ 'codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'gpen_bfr_1024', 'gpen_bfr_2048', 'restoreformer_plus_plus' ]
|
||||
face_enhancer_models : List[FaceEnhancerModel] = list(get_args(FaceEnhancerModel))
|
||||
|
||||
face_enhancer_blend_range : Sequence[int] = create_int_range(0, 100, 1)
|
||||
|
||||
|
||||
@@ -1,8 +1,9 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_float_range
|
||||
from facefusion.processors.modules.face_swapper.types import FaceSwapperModel, FaceSwapperSet, FaceSwapperWeight
|
||||
|
||||
|
||||
face_swapper_set : FaceSwapperSet =\
|
||||
{
|
||||
'blendswap_256': [ '256x256', '384x384', '512x512', '768x768', '1024x1024' ],
|
||||
@@ -20,6 +21,6 @@ face_swapper_set : FaceSwapperSet =\
|
||||
'uniface_256': [ '256x256', '512x512', '768x768', '1024x1024' ]
|
||||
}
|
||||
|
||||
face_swapper_models : List[FaceSwapperModel] = list(face_swapper_set.keys())
|
||||
face_swapper_models : List[FaceSwapperModel] = list(get_args(FaceSwapperModel))
|
||||
|
||||
face_swapper_weight_range : Sequence[FaceSwapperWeight] = create_float_range(0.0, 1.0, 0.05)
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_int_range
|
||||
from facefusion.processors.modules.frame_colorizer.types import FrameColorizerModel
|
||||
|
||||
frame_colorizer_models : List[FrameColorizerModel] = [ 'ddcolor', 'ddcolor_artistic', 'deoldify', 'deoldify_artistic', 'deoldify_stable' ]
|
||||
frame_colorizer_models : List[FrameColorizerModel] = list(get_args(FrameColorizerModel))
|
||||
|
||||
frame_colorizer_sizes : List[str] = [ '192x192', '256x256', '384x384', '512x512' ]
|
||||
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_int_range
|
||||
from facefusion.processors.modules.frame_enhancer.types import FrameEnhancerModel
|
||||
|
||||
frame_enhancer_models : List[FrameEnhancerModel] = [ 'clear_reality_x4', 'face_dat_x4', 'lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x2', 'real_esrgan_x2_fp16', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'real_esrgan_x8', 'real_esrgan_x8_fp16', 'real_hatgan_x4', 'real_web_photo_x4', 'realistic_rescaler_x4', 'remacri_x4', 'siax_x4', 'span_kendata_x4', 'swin2_sr_x4', 'tghq_face_x8', 'ultra_sharp_x4', 'ultra_sharp_2_x4' ]
|
||||
frame_enhancer_models : List[FrameEnhancerModel] = list(get_args(FrameEnhancerModel))
|
||||
|
||||
frame_enhancer_blend_range : Sequence[int] = create_int_range(0, 100, 1)
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
from typing import List, Sequence
|
||||
from typing import List, Sequence, get_args
|
||||
|
||||
from facefusion.common_helper import create_float_range
|
||||
from facefusion.processors.modules.lip_syncer.types import LipSyncerModel
|
||||
|
||||
lip_syncer_models : List[LipSyncerModel] = [ 'edtalk_256', 'wav2lip_96', 'wav2lip_gan_96' ]
|
||||
lip_syncer_models : List[LipSyncerModel] = list(get_args(LipSyncerModel))
|
||||
|
||||
lip_syncer_weight_range : Sequence[float] = create_float_range(0.0, 1.0, 0.05)
|
||||
|
||||
@@ -10,7 +10,7 @@ from facefusion.ffmpeg import get_available_encoder_set
|
||||
from facefusion.filesystem import get_file_name, resolve_file_paths
|
||||
from facefusion.jobs import job_store
|
||||
from facefusion.processors.core import get_processors_modules
|
||||
from facefusion.sanitizer import sanitize_int_range
|
||||
from facefusion.sanitizer import sanitize_int_range, sanitize_job_id
|
||||
|
||||
|
||||
def create_help_formatter_small(prog : str) -> HelpFormatter:
|
||||
@@ -188,7 +188,7 @@ def create_processors_program() -> ArgumentParser:
|
||||
program = ArgumentParser(add_help = False)
|
||||
available_processors = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/processors/modules') ]
|
||||
group_processors = program.add_argument_group('processors')
|
||||
group_processors.add_argument('--processors', help = translator.get('help.processors').format(choices = ', '.join(available_processors)), default = config.get_str_list('processors', 'processors', 'face_swapper'), nargs = '+')
|
||||
group_processors.add_argument('--processors', help = translator.get('help.processors').format(choices = ', '.join(available_processors)), default = config.get_str_list('processors', 'processors', 'face_swapper'), choices = available_processors, nargs = '+', metavar = 'PROCESSORS')
|
||||
job_store.register_step_keys([ 'processors' ])
|
||||
for processor_module in get_processors_modules(available_processors):
|
||||
processor_module.register_args(program)
|
||||
@@ -200,7 +200,7 @@ def create_uis_program() -> ArgumentParser:
|
||||
available_ui_layouts = [ get_file_name(file_path) for file_path in resolve_file_paths('facefusion/uis/layouts') ]
|
||||
group_uis = program.add_argument_group('uis')
|
||||
group_uis.add_argument('--open-browser', help = translator.get('help.open_browser'), action = 'store_true', default = config.get_bool_value('uis', 'open_browser'))
|
||||
group_uis.add_argument('--ui-layouts', help = translator.get('help.ui_layouts').format(choices = ', '.join(available_ui_layouts)), default = config.get_str_list('uis', 'ui_layouts', 'default'), nargs = '+')
|
||||
group_uis.add_argument('--ui-layouts', help = translator.get('help.ui_layouts').format(choices = ', '.join(available_ui_layouts)), default = config.get_str_list('uis', 'ui_layouts', 'default'), choices = available_ui_layouts, nargs = '+', metavar = 'UI_LAYOUTS')
|
||||
group_uis.add_argument('--ui-workflow', help = translator.get('help.ui_workflow'), default = config.get_str_value('uis', 'ui_workflow', 'instant_runner'), choices = facefusion.choices.ui_workflows)
|
||||
return program
|
||||
|
||||
@@ -268,7 +268,7 @@ def create_halt_on_error_program() -> ArgumentParser:
|
||||
|
||||
def create_job_id_program() -> ArgumentParser:
|
||||
program = ArgumentParser(add_help = False)
|
||||
program.add_argument('job_id', help = translator.get('help.job_id'))
|
||||
program.add_argument('job_id', help = translator.get('help.job_id'), type = sanitize_job_id)
|
||||
return program
|
||||
|
||||
|
||||
@@ -297,13 +297,11 @@ def create_program() -> ArgumentParser:
|
||||
program._positionals.title = 'commands'
|
||||
program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
|
||||
sub_program = program.add_subparsers(dest = 'command')
|
||||
# general
|
||||
sub_program.add_parser('run', help = translator.get('help.run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_uis_program(), create_benchmark_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
|
||||
sub_program.add_parser('headless-run', help = translator.get('help.headless_run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
|
||||
sub_program.add_parser('batch-run', help = translator.get('help.batch_run'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), create_source_pattern_program(), create_target_pattern_program(), create_output_pattern_program(), collect_step_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
|
||||
sub_program.add_parser('force-download', help = translator.get('help.force_download'), parents = [ create_download_providers_program(), create_download_scope_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
|
||||
sub_program.add_parser('benchmark', help = translator.get('help.benchmark'), parents = [ create_temp_path_program(), collect_step_program(), create_benchmark_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
|
||||
# job manager
|
||||
sub_program.add_parser('job-list', help = translator.get('help.job_list'), parents = [ create_job_status_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
|
||||
sub_program.add_parser('job-create', help = translator.get('help.job_create'), parents = [ create_job_id_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
|
||||
sub_program.add_parser('job-submit', help = translator.get('help.job_submit'), parents = [ create_job_id_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
|
||||
@@ -314,7 +312,6 @@ def create_program() -> ArgumentParser:
|
||||
sub_program.add_parser('job-remix-step', help = translator.get('help.job_remix_step'), parents = [ create_job_id_program(), create_step_index_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_output_path_program(), collect_step_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
|
||||
sub_program.add_parser('job-insert-step', help = translator.get('help.job_insert_step'), parents = [ create_job_id_program(), create_step_index_program(), create_config_path_program(), create_jobs_path_program(), create_source_paths_program(), create_target_path_program(), create_output_path_program(), collect_step_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
|
||||
sub_program.add_parser('job-remove-step', help = translator.get('help.job_remove_step'), parents = [ create_job_id_program(), create_step_index_program(), create_jobs_path_program(), create_log_level_program() ], formatter_class = create_help_formatter_large)
|
||||
# job runner
|
||||
sub_program.add_parser('job-run', help = translator.get('help.job_run'), parents = [ create_job_id_program(), create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
|
||||
sub_program.add_parser('job-run-all', help = translator.get('help.job_run_all'), parents = [ create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program(), create_halt_on_error_program() ], formatter_class = create_help_formatter_large)
|
||||
sub_program.add_parser('job-retry', help = translator.get('help.job_retry'), parents = [ create_job_id_program(), create_config_path_program(), create_temp_path_program(), create_jobs_path_program(), collect_job_program() ], formatter_class = create_help_formatter_large)
|
||||
|
||||
@@ -1,6 +1,15 @@
|
||||
import hashlib
|
||||
from typing import Sequence
|
||||
|
||||
|
||||
def sanitize_job_id(job_id : str) -> str:
|
||||
__job_id__ = job_id.replace('-', '')
|
||||
|
||||
if __job_id__.isalnum():
|
||||
return job_id
|
||||
return hashlib.sha1(job_id.encode()).hexdigest()
|
||||
|
||||
|
||||
def sanitize_int_range(value : int, int_range : Sequence[int]) -> int:
|
||||
if value in int_range:
|
||||
return value
|
||||
|
||||
+4
-3
@@ -167,10 +167,11 @@ ModelOptions : TypeAlias = Dict[str, Any]
|
||||
ModelSet : TypeAlias = Dict[str, ModelOptions]
|
||||
ModelInitializer : TypeAlias = NDArray[Any]
|
||||
|
||||
ExecutionProvider = Literal['cpu', 'coreml', 'cuda', 'directml', 'openvino', 'migraphx', 'rocm', 'tensorrt']
|
||||
ExecutionProviderValue = Literal['CPUExecutionProvider', 'CoreMLExecutionProvider', 'CUDAExecutionProvider', 'DmlExecutionProvider', 'OpenVINOExecutionProvider', 'MIGraphXExecutionProvider', 'ROCMExecutionProvider', 'TensorrtExecutionProvider']
|
||||
ExecutionProvider = Literal['cuda', 'tensorrt', 'rocm', 'migraphx', 'coreml', 'openvino', 'qnn', 'directml', 'cpu']
|
||||
ExecutionProviderValue = Literal['CPUExecutionProvider', 'CoreMLExecutionProvider', 'CUDAExecutionProvider', 'DmlExecutionProvider', 'OpenVINOExecutionProvider', 'MIGraphXExecutionProvider', 'QNNExecutionProvider', 'ROCMExecutionProvider', 'TensorrtExecutionProvider']
|
||||
ExecutionProviderSet : TypeAlias = Dict[ExecutionProvider, ExecutionProviderValue]
|
||||
InferenceSessionProvider : TypeAlias = Any
|
||||
InferenceProvider : TypeAlias = Any
|
||||
InferenceOptionSet : TypeAlias = Dict[str, Any]
|
||||
ValueAndUnit = TypedDict('ValueAndUnit',
|
||||
{
|
||||
'value' : int,
|
||||
|
||||
@@ -90,7 +90,7 @@ def start(webcam_device_id : int, webcam_mode : WebcamMode, webcam_resolution :
|
||||
stream = None
|
||||
|
||||
if webcam_mode in [ 'udp', 'v4l2' ]:
|
||||
stream = open_stream(webcam_mode, webcam_resolution, webcam_fps) # type:ignore[arg-type]
|
||||
stream = open_stream(webcam_mode, webcam_resolution, webcam_fps) #type:ignore[arg-type]
|
||||
webcam_width, webcam_height = unpack_resolution(webcam_resolution)
|
||||
|
||||
if camera_capture and camera_capture.isOpened():
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import importlib
|
||||
import logging
|
||||
import os
|
||||
import warnings
|
||||
from types import ModuleType
|
||||
@@ -72,6 +73,7 @@ def init() -> None:
|
||||
os.environ['GRADIO_ANALYTICS_ENABLED'] = '0'
|
||||
os.environ['GRADIO_TEMP_DIR'] = os.path.join(state_manager.get_item('temp_path'), 'gradio')
|
||||
|
||||
logging.getLogger('asyncio').setLevel(logging.CRITICAL)
|
||||
warnings.filterwarnings('ignore', category = UserWarning, module = 'gradio')
|
||||
gradio.processing_utils._check_allowed = uis_overrides.mock
|
||||
gradio.processing_utils.convert_video_to_playable_mp4 = uis_overrides.convert_video_to_playable_mp4
|
||||
|
||||
@@ -24,7 +24,7 @@ def process(start_time : float) -> ErrorCode:
|
||||
process_manager.start()
|
||||
|
||||
for task in tasks:
|
||||
error_code = task() # type:ignore[operator]
|
||||
error_code = task() #type:ignore[operator]
|
||||
|
||||
if error_code > 0:
|
||||
process_manager.end()
|
||||
|
||||
@@ -31,7 +31,7 @@ def process(start_time : float) -> ErrorCode:
|
||||
process_manager.start()
|
||||
|
||||
for task in tasks:
|
||||
error_code = task() # type:ignore[operator]
|
||||
error_code = task() #type:ignore[operator]
|
||||
|
||||
if error_code > 0:
|
||||
process_manager.end()
|
||||
|
||||
+2
-2
@@ -2,7 +2,7 @@ gradio-rangeslider==0.0.8
|
||||
gradio==5.44.1
|
||||
numpy==2.2.1
|
||||
onnx==1.20.1
|
||||
onnxruntime==1.24.1
|
||||
onnxruntime==1.24.3
|
||||
opencv-python==4.13.0.92
|
||||
tqdm==4.67.3
|
||||
scipy==1.17.0
|
||||
scipy==1.17.1
|
||||
|
||||
@@ -7,7 +7,7 @@ from facefusion.curl_builder import chain, ping, run, set_timeout
|
||||
def test_run() -> None:
|
||||
user_agent = metadata.get('name') + '/' + metadata.get('version')
|
||||
|
||||
assert run([]) == [ which('curl'), '--user-agent', user_agent, '--insecure', '--location', '--silent' ]
|
||||
assert run([]) == [ which('curl'), '--user-agent', user_agent, '--location', '--silent' ]
|
||||
|
||||
|
||||
def test_chain() -> None:
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
from facefusion.execution import create_inference_session_providers, get_available_execution_providers, has_execution_provider
|
||||
from facefusion.execution import create_inference_providers, get_available_execution_providers, has_execution_provider
|
||||
|
||||
|
||||
def test_has_execution_provider() -> None:
|
||||
@@ -10,8 +10,8 @@ def test_get_available_execution_providers() -> None:
|
||||
assert 'cpu' in get_available_execution_providers()
|
||||
|
||||
|
||||
def test_create_inference_session_providers() -> None:
|
||||
inference_session_providers =\
|
||||
def test_create_inference_providers() -> None:
|
||||
inference_providers =\
|
||||
[
|
||||
('CUDAExecutionProvider',
|
||||
{
|
||||
@@ -21,4 +21,4 @@ def test_create_inference_session_providers() -> None:
|
||||
'CPUExecutionProvider'
|
||||
]
|
||||
|
||||
assert create_inference_session_providers(1, [ 'cpu', 'cuda' ]) == inference_session_providers
|
||||
assert create_inference_providers(1, [ 'cpu', 'cuda' ]) == inference_providers
|
||||
|
||||
+5
-2
@@ -1,10 +1,12 @@
|
||||
import os
|
||||
import tempfile
|
||||
|
||||
from facefusion.json import read_json, write_json
|
||||
|
||||
|
||||
def test_read_json() -> None:
|
||||
_, json_path = tempfile.mkstemp(suffix = '.json')
|
||||
file_descriptor, json_path = tempfile.mkstemp(suffix = '.json')
|
||||
os.close(file_descriptor)
|
||||
|
||||
assert not read_json(json_path)
|
||||
|
||||
@@ -14,6 +16,7 @@ def test_read_json() -> None:
|
||||
|
||||
|
||||
def test_write_json() -> None:
|
||||
_, json_path = tempfile.mkstemp(suffix = '.json')
|
||||
file_descriptor, json_path = tempfile.mkstemp(suffix = '.json')
|
||||
os.close(file_descriptor)
|
||||
|
||||
assert write_json(json_path, {})
|
||||
|
||||
Reference in New Issue
Block a user