9d0c377aa0
* Extract download directory path from assets path * Fix lint * Fix force-download command, Fix urls in frame enhancer
522 lines
15 KiB
Python
522 lines
15 KiB
Python
from argparse import ArgumentParser
|
|
from typing import List
|
|
|
|
import cv2
|
|
import numpy
|
|
|
|
import facefusion.jobs.job_manager
|
|
import facefusion.jobs.job_store
|
|
import facefusion.processors.core as processors
|
|
from facefusion import config, content_analyser, inference_manager, logger, process_manager, state_manager, wording
|
|
from facefusion.common_helper import create_int_metavar
|
|
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
|
|
from facefusion.filesystem import in_directory, is_image, is_video, resolve_relative_path, same_file_extension
|
|
from facefusion.processors import choices as processors_choices
|
|
from facefusion.processors.typing import FrameEnhancerInputs
|
|
from facefusion.program_helper import find_argument_group
|
|
from facefusion.thread_helper import conditional_thread_semaphore
|
|
from facefusion.typing import ApplyStateItem, Args, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame
|
|
from facefusion.vision import create_tile_frames, merge_tile_frames, read_image, read_static_image, write_image
|
|
|
|
|
|
def create_model_set() -> ModelSet:
|
|
return\
|
|
{
|
|
'clear_reality_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'clear_reality_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/clear_reality_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'clear_reality_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/clear_reality_x4.onnx')
|
|
}
|
|
},
|
|
'size': (128, 8, 4),
|
|
'scale': 4
|
|
},
|
|
'lsdir_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'lsdir_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/lsdir_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'lsdir_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/lsdir_x4.onnx')
|
|
}
|
|
},
|
|
'size': (128, 8, 4),
|
|
'scale': 4
|
|
},
|
|
'nomos8k_sc_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'nomos8k_sc_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/nomos8k_sc_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'nomos8k_sc_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/nomos8k_sc_x4.onnx')
|
|
}
|
|
},
|
|
'size': (128, 8, 4),
|
|
'scale': 4
|
|
},
|
|
'real_esrgan_x2':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x2.hash'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x2.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x2.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x2.onnx')
|
|
}
|
|
},
|
|
'size': (256, 16, 8),
|
|
'scale': 2
|
|
},
|
|
'real_esrgan_x2_fp16':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x2_fp16.hash'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x2_fp16.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x2_fp16.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x2_fp16.onnx')
|
|
}
|
|
},
|
|
'size': (256, 16, 8),
|
|
'scale': 2
|
|
},
|
|
'real_esrgan_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x4.onnx')
|
|
}
|
|
},
|
|
'size': (256, 16, 8),
|
|
'scale': 4
|
|
},
|
|
'real_esrgan_x4_fp16':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x4_fp16.hash'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x4_fp16.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x4_fp16.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x4_fp16.onnx')
|
|
}
|
|
},
|
|
'size': (256, 16, 8),
|
|
'scale': 4
|
|
},
|
|
'real_esrgan_x8':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x8.hash'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x8.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x8.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x8.onnx')
|
|
}
|
|
},
|
|
'size': (256, 16, 8),
|
|
'scale': 8
|
|
},
|
|
'real_esrgan_x8_fp16':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x8_fp16.hash'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x8_fp16.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_esrgan_x8_fp16.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/real_esrgan_x8_fp16.onnx')
|
|
}
|
|
},
|
|
'size': (256, 16, 8),
|
|
'scale': 8
|
|
},
|
|
'real_hatgan_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_hatgan_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/real_hatgan_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'real_hatgan_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/real_hatgan_x4.onnx')
|
|
}
|
|
},
|
|
'size': (256, 16, 8),
|
|
'scale': 4
|
|
},
|
|
'real_web_photo_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.1.0', 'real_web_photo_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/real_web_photo_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.1.0', 'real_web_photo_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/real_web_photo_x4.onnx')
|
|
}
|
|
},
|
|
'size': (64, 4, 2),
|
|
'scale': 4
|
|
},
|
|
'realistic_rescaler_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.1.0', 'realistic_rescaler_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/realistic_rescaler_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.1.0', 'realistic_rescaler_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/realistic_rescaler_x4.onnx')
|
|
}
|
|
},
|
|
'size': (128, 8, 4),
|
|
'scale': 4
|
|
},
|
|
'remacri_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.1.0', 'remacri_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/remacri_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.1.0', 'remacri_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/remacri_x4.onnx')
|
|
}
|
|
},
|
|
'size': (128, 8, 4),
|
|
'scale': 4
|
|
},
|
|
'siax_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.1.0', 'siax_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/siax_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.1.0', 'siax_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/siax_x4.onnx')
|
|
}
|
|
},
|
|
'size': (128, 8, 4),
|
|
'scale': 4
|
|
},
|
|
'span_kendata_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'span_kendata_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/span_kendata_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'span_kendata_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/span_kendata_x4.onnx')
|
|
}
|
|
},
|
|
'size': (128, 8, 4),
|
|
'scale': 4
|
|
},
|
|
'swin2_sr_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.1.0', 'swin2_sr_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/swin2_sr_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.1.0', 'swin2_sr_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/swin2_sr_x4.onnx')
|
|
}
|
|
},
|
|
'size': (128, 8, 4),
|
|
'scale': 4
|
|
},
|
|
'ultra_sharp_x4':
|
|
{
|
|
'hashes':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'ultra_sharp_x4.hash'),
|
|
'path': resolve_relative_path('../.assets/models/ultra_sharp_x4.hash')
|
|
}
|
|
},
|
|
'sources':
|
|
{
|
|
'frame_enhancer':
|
|
{
|
|
'url': resolve_download_url('models-3.0.0', 'ultra_sharp_x4.onnx'),
|
|
'path': resolve_relative_path('../.assets/models/ultra_sharp_x4.onnx')
|
|
}
|
|
},
|
|
'size': (128, 8, 4),
|
|
'scale': 4
|
|
}
|
|
}
|
|
|
|
|
|
def get_inference_pool() -> InferencePool:
|
|
model_sources = get_model_options().get('sources')
|
|
model_context = __name__ + '.' + state_manager.get_item('frame_enhancer_model')
|
|
return inference_manager.get_inference_pool(model_context, model_sources)
|
|
|
|
|
|
def clear_inference_pool() -> None:
|
|
model_context = __name__ + '.' + state_manager.get_item('frame_enhancer_model')
|
|
inference_manager.clear_inference_pool(model_context)
|
|
|
|
|
|
def get_model_options() -> ModelOptions:
|
|
frame_enhancer_model = state_manager.get_item('frame_enhancer_model')
|
|
return create_model_set().get(frame_enhancer_model)
|
|
|
|
|
|
def register_args(program : ArgumentParser) -> None:
|
|
group_processors = find_argument_group(program, 'processors')
|
|
if group_processors:
|
|
group_processors.add_argument('--frame-enhancer-model', help = wording.get('help.frame_enhancer_model'), default = config.get_str_value('processors.frame_enhancer_model', 'span_kendata_x4'), choices = processors_choices.frame_enhancer_models)
|
|
group_processors.add_argument('--frame-enhancer-blend', help = wording.get('help.frame_enhancer_blend'), type = int, default = config.get_int_value('processors.frame_enhancer_blend', '80'), choices = processors_choices.frame_enhancer_blend_range, metavar = create_int_metavar(processors_choices.frame_enhancer_blend_range))
|
|
facefusion.jobs.job_store.register_step_keys([ 'frame_enhancer_model', 'frame_enhancer_blend' ])
|
|
|
|
|
|
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
|
|
apply_state_item('frame_enhancer_model', args.get('frame_enhancer_model'))
|
|
apply_state_item('frame_enhancer_blend', args.get('frame_enhancer_blend'))
|
|
|
|
|
|
def pre_check() -> bool:
|
|
model_hashes = get_model_options().get('hashes')
|
|
model_sources = get_model_options().get('sources')
|
|
|
|
return conditional_download_hashes(model_hashes) and conditional_download_sources(model_sources)
|
|
|
|
|
|
def pre_process(mode : ProcessMode) -> bool:
|
|
if mode in [ 'output', 'preview' ] and not is_image(state_manager.get_item('target_path')) and not is_video(state_manager.get_item('target_path')):
|
|
logger.error(wording.get('choose_image_or_video_target') + wording.get('exclamation_mark'), __name__)
|
|
return False
|
|
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
|
|
logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__)
|
|
return False
|
|
if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]):
|
|
logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__)
|
|
return False
|
|
return True
|
|
|
|
|
|
def post_process() -> None:
|
|
read_static_image.cache_clear()
|
|
if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]:
|
|
clear_inference_pool()
|
|
if state_manager.get_item('video_memory_strategy') == 'strict':
|
|
content_analyser.clear_inference_pool()
|
|
|
|
|
|
def enhance_frame(temp_vision_frame : VisionFrame) -> VisionFrame:
|
|
model_size = get_model_options().get('size')
|
|
model_scale = get_model_options().get('scale')
|
|
temp_height, temp_width = temp_vision_frame.shape[:2]
|
|
tile_vision_frames, pad_width, pad_height = create_tile_frames(temp_vision_frame, model_size)
|
|
|
|
for index, tile_vision_frame in enumerate(tile_vision_frames):
|
|
tile_vision_frame = prepare_tile_frame(tile_vision_frame)
|
|
tile_vision_frame = forward(tile_vision_frame)
|
|
tile_vision_frames[index] = normalize_tile_frame(tile_vision_frame)
|
|
|
|
merge_vision_frame = merge_tile_frames(tile_vision_frames, temp_width * model_scale, temp_height * model_scale, pad_width * model_scale, pad_height * model_scale, (model_size[0] * model_scale, model_size[1] * model_scale, model_size[2] * model_scale))
|
|
temp_vision_frame = blend_frame(temp_vision_frame, merge_vision_frame)
|
|
return temp_vision_frame
|
|
|
|
|
|
def forward(tile_vision_frame : VisionFrame) -> VisionFrame:
|
|
frame_enhancer = get_inference_pool().get('frame_enhancer')
|
|
|
|
with conditional_thread_semaphore():
|
|
tile_vision_frame = frame_enhancer.run(None,
|
|
{
|
|
'input': tile_vision_frame
|
|
})[0]
|
|
|
|
return tile_vision_frame
|
|
|
|
|
|
def prepare_tile_frame(vision_tile_frame : VisionFrame) -> VisionFrame:
|
|
vision_tile_frame = numpy.expand_dims(vision_tile_frame[:, :, ::-1], axis = 0)
|
|
vision_tile_frame = vision_tile_frame.transpose(0, 3, 1, 2)
|
|
vision_tile_frame = vision_tile_frame.astype(numpy.float32) / 255
|
|
return vision_tile_frame
|
|
|
|
|
|
def normalize_tile_frame(vision_tile_frame : VisionFrame) -> VisionFrame:
|
|
vision_tile_frame = vision_tile_frame.transpose(0, 2, 3, 1).squeeze(0) * 255
|
|
vision_tile_frame = vision_tile_frame.clip(0, 255).astype(numpy.uint8)[:, :, ::-1]
|
|
return vision_tile_frame
|
|
|
|
|
|
def blend_frame(temp_vision_frame : VisionFrame, merge_vision_frame : VisionFrame) -> VisionFrame:
|
|
frame_enhancer_blend = 1 - (state_manager.get_item('frame_enhancer_blend') / 100)
|
|
temp_vision_frame = cv2.resize(temp_vision_frame, (merge_vision_frame.shape[1], merge_vision_frame.shape[0]))
|
|
temp_vision_frame = cv2.addWeighted(temp_vision_frame, frame_enhancer_blend, merge_vision_frame, 1 - frame_enhancer_blend, 0)
|
|
return temp_vision_frame
|
|
|
|
|
|
def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
|
pass
|
|
|
|
|
|
def process_frame(inputs : FrameEnhancerInputs) -> VisionFrame:
|
|
target_vision_frame = inputs.get('target_vision_frame')
|
|
return enhance_frame(target_vision_frame)
|
|
|
|
|
|
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
|
|
for queue_payload in process_manager.manage(queue_payloads):
|
|
target_vision_path = queue_payload['frame_path']
|
|
target_vision_frame = read_image(target_vision_path)
|
|
output_vision_frame = process_frame(
|
|
{
|
|
'target_vision_frame': target_vision_frame
|
|
})
|
|
write_image(target_vision_path, output_vision_frame)
|
|
update_progress(1)
|
|
|
|
|
|
def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
|
|
target_vision_frame = read_static_image(target_path)
|
|
output_vision_frame = process_frame(
|
|
{
|
|
'target_vision_frame': target_vision_frame
|
|
})
|
|
write_image(output_path, output_vision_frame)
|
|
|
|
|
|
def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
|
|
processors.multi_process_frames(None, temp_frame_paths, process_frames)
|