4efa5b2c6e
* Validate the overrides from facefusion.ini * Break down cli testing * Remove architecture lookup to support old driver * Remove architecture lookup to support old driver * Remove hwaccel auto * Respect the output video resolution * Bump next version * Full directml support (#501) * Introduce conditional thread management for DML support * Finish migration to thread helpers * Introduce dynamic frame colorizer sizes * Introduce dynamic frame colorizer sizes * Add 192x192 to frame colorizer * Fix async audio
250 lines
9.7 KiB
Python
250 lines
9.7 KiB
Python
from typing import Any, List, Literal, Optional
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from argparse import ArgumentParser
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from time import sleep
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import cv2
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import numpy
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import onnxruntime
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import facefusion.globals
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import facefusion.processors.frame.core as frame_processors
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from facefusion import config, process_manager, logger, wording
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from facefusion.face_analyser import clear_face_analyser
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from facefusion.content_analyser import clear_content_analyser
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from facefusion.execution import apply_execution_provider_options
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from facefusion.normalizer import normalize_output_path
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from facefusion.thread_helper import thread_lock, conditional_thread_semaphore
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from facefusion.typing import Face, VisionFrame, UpdateProgress, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
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from facefusion.common_helper import create_metavar
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from facefusion.filesystem import is_file, resolve_relative_path, is_image, is_video
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from facefusion.download import conditional_download, is_download_done
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from facefusion.vision import read_image, read_static_image, write_image, merge_tile_frames, create_tile_frames
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from facefusion.processors.frame.typings import FrameEnhancerInputs
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from facefusion.processors.frame import globals as frame_processors_globals
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from facefusion.processors.frame import choices as frame_processors_choices
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FRAME_PROCESSOR = None
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NAME = __name__.upper()
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MODELS : ModelSet =\
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{
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'lsdir_x4':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/lsdir_x4.onnx',
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'path': resolve_relative_path('../.assets/models/lsdir_x4.onnx'),
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'size': (128, 8, 2),
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'scale': 4
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},
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'nomos8k_sc_x4':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/nomos8k_sc_x4.onnx',
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'path': resolve_relative_path('../.assets/models/nomos8k_sc_x4.onnx'),
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'size': (128, 8, 2),
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'scale': 4
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},
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'real_esrgan_x2':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrgan_x2.onnx',
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'path': resolve_relative_path('../.assets/models/real_esrgan_x2.onnx'),
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'size': (128, 8, 2),
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'scale': 2
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},
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'real_esrgan_x2_fp16':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrgan_x2_fp16.onnx',
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'path': resolve_relative_path('../.assets/models/real_esrgan_x2_fp16.onnx'),
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'size': (128, 8, 2),
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'scale': 2
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},
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'real_esrgan_x4':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrgan_x4.onnx',
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'path': resolve_relative_path('../.assets/models/real_esrgan_x4.onnx'),
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'size': (128, 8, 2),
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'scale': 4
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},
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'real_esrgan_x4_fp16':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrgan_x4_fp16.onnx',
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'path': resolve_relative_path('../.assets/models/real_esrgan_x4_fp16.onnx'),
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'size': (128, 8, 2),
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'scale': 4
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},
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'real_hatgan_x4':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_hatgan_x4.onnx',
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'path': resolve_relative_path('../.assets/models/real_hatgan_x4.onnx'),
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'size': (256, 8, 2),
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'scale': 4
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},
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'span_kendata_x4':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/span_kendata_x4.onnx',
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'path': resolve_relative_path('../.assets/models/span_kendata_x4.onnx'),
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'size': (128, 8, 2),
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'scale': 4
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}
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}
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OPTIONS : Optional[OptionsWithModel] = None
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def get_frame_processor() -> Any:
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global FRAME_PROCESSOR
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with thread_lock():
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while process_manager.is_checking():
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sleep(0.5)
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if FRAME_PROCESSOR is None:
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model_path = get_options('model').get('path')
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FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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return FRAME_PROCESSOR
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def clear_frame_processor() -> None:
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global FRAME_PROCESSOR
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FRAME_PROCESSOR = None
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def get_options(key : Literal['model']) -> Any:
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global OPTIONS
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if OPTIONS is None:
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OPTIONS =\
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{
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'model': MODELS[frame_processors_globals.frame_enhancer_model]
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}
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return OPTIONS.get(key)
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def set_options(key : Literal['model'], value : Any) -> None:
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global OPTIONS
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OPTIONS[key] = value
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def register_args(program : ArgumentParser) -> None:
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program.add_argument('--frame-enhancer-model', help = wording.get('help.frame_enhancer_model'), default = config.get_str_value('frame_processors.frame_enhancer_model', 'span_kendata_x4'), choices = frame_processors_choices.frame_enhancer_models)
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program.add_argument('--frame-enhancer-blend', help = wording.get('help.frame_enhancer_blend'), type = int, default = config.get_int_value('frame_processors.frame_enhancer_blend', '80'), choices = frame_processors_choices.frame_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.frame_enhancer_blend_range))
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def apply_args(program : ArgumentParser) -> None:
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args = program.parse_args()
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frame_processors_globals.frame_enhancer_model = args.frame_enhancer_model
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frame_processors_globals.frame_enhancer_blend = args.frame_enhancer_blend
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def pre_check() -> bool:
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download_directory_path = resolve_relative_path('../.assets/models')
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model_url = get_options('model').get('url')
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model_path = get_options('model').get('path')
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if not facefusion.globals.skip_download:
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process_manager.check()
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conditional_download(download_directory_path, [ model_url ])
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process_manager.end()
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return is_file(model_path)
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def post_check() -> bool:
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model_url = get_options('model').get('url')
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model_path = get_options('model').get('path')
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if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
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logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
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return False
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if not is_file(model_path):
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logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
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return False
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return True
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def pre_process(mode : ProcessMode) -> bool:
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if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path):
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logger.error(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
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return False
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if mode == 'output' and not normalize_output_path(facefusion.globals.target_path, facefusion.globals.output_path):
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logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
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return False
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return True
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def post_process() -> None:
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read_static_image.cache_clear()
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if facefusion.globals.video_memory_strategy == 'strict' or facefusion.globals.video_memory_strategy == 'moderate':
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clear_frame_processor()
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if facefusion.globals.video_memory_strategy == 'strict':
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clear_face_analyser()
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clear_content_analyser()
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def enhance_frame(temp_vision_frame : VisionFrame) -> VisionFrame:
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frame_processor = get_frame_processor()
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size = get_options('model').get('size')
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scale = get_options('model').get('scale')
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temp_height, temp_width = temp_vision_frame.shape[:2]
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tile_vision_frames, pad_width, pad_height = create_tile_frames(temp_vision_frame, size)
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for index, tile_vision_frame in enumerate(tile_vision_frames):
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with conditional_thread_semaphore(facefusion.globals.execution_providers):
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tile_vision_frame = frame_processor.run(None,
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{
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frame_processor.get_inputs()[0].name : prepare_tile_frame(tile_vision_frame)
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})[0]
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tile_vision_frames[index] = normalize_tile_frame(tile_vision_frame)
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merge_vision_frame = merge_tile_frames(tile_vision_frames, temp_width * scale, temp_height * scale, pad_width * scale, pad_height * scale, (size[0] * scale, size[1] * scale, size[2] * scale))
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temp_vision_frame = blend_frame(temp_vision_frame, merge_vision_frame)
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return temp_vision_frame
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def prepare_tile_frame(vision_tile_frame : VisionFrame) -> VisionFrame:
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vision_tile_frame = numpy.expand_dims(vision_tile_frame[:, :, ::-1], axis = 0)
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vision_tile_frame = vision_tile_frame.transpose(0, 3, 1, 2)
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vision_tile_frame = vision_tile_frame.astype(numpy.float32) / 255
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return vision_tile_frame
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def normalize_tile_frame(vision_tile_frame : VisionFrame) -> VisionFrame:
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vision_tile_frame = vision_tile_frame.transpose(0, 2, 3, 1).squeeze(0) * 255
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vision_tile_frame = vision_tile_frame.clip(0, 255).astype(numpy.uint8)[:, :, ::-1]
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return vision_tile_frame
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def blend_frame(temp_vision_frame : VisionFrame, merge_vision_frame : VisionFrame) -> VisionFrame:
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frame_enhancer_blend = 1 - (frame_processors_globals.frame_enhancer_blend / 100)
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temp_vision_frame = cv2.resize(temp_vision_frame, (merge_vision_frame.shape[1], merge_vision_frame.shape[0]))
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temp_vision_frame = cv2.addWeighted(temp_vision_frame, frame_enhancer_blend, merge_vision_frame, 1 - frame_enhancer_blend, 0)
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return temp_vision_frame
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def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
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pass
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def process_frame(inputs : FrameEnhancerInputs) -> VisionFrame:
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target_vision_frame = inputs.get('target_vision_frame')
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return enhance_frame(target_vision_frame)
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def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
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for queue_payload in process_manager.manage(queue_payloads):
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target_vision_path = queue_payload['frame_path']
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target_vision_frame = read_image(target_vision_path)
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output_vision_frame = process_frame(
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{
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'target_vision_frame': target_vision_frame
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})
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write_image(target_vision_path, output_vision_frame)
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update_progress(1)
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def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
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target_vision_frame = read_static_image(target_path)
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output_vision_frame = process_frame(
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{
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'target_vision_frame': target_vision_frame
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})
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write_image(output_path, output_vision_frame)
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def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
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frame_processors.multi_process_frames(None, temp_frame_paths, process_frames)
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