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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-14 00:53:48 -07:00
converter:
fixed crashes removed useless 'ebs' color transfer changed keys for color degrade added image degrade via denoise - same as denoise extracted data_dst.bat , but you can control this option directly in the interactive converter added image degrade via bicubic downscale and upscale SAEHD: default ae_dims for df now 256.
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Colombo
parent
374d8c2388
commit
770c70d778
8 changed files with 274 additions and 57 deletions
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@ -101,12 +101,21 @@ class ConvertSubprocessor(Subprocessor):
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return cv2.filter2D(img, -1, kernel)
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elif sharpen_mode == 2: #gaussian
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blur = cv2.GaussianBlur(img, (kernel_size, kernel_size) , 0)
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img = cv2.addWeighted(img, 1.0 + (0.5 * amount), blur, -(0.5 * amount), 0)
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img = cv2.addWeighted(img, 1.0 + (0.5 * amount), blur, -(0.5 * amount), 0)
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return img
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elif amount < 0:
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blur = cv2.GaussianBlur(img, (kernel_size, kernel_size) , 0)
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img = cv2.addWeighted(img, 1.0 - a / 50.0, blur, a /50.0, 0)
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return img
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n = -amount
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while n > 0:
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img_blur = cv2.medianBlur(img, 5)
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if int(n / 10) != 0:
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img = img_blur
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else:
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pass_power = (n % 10) / 10.0
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img = img*(1.0-pass_power)+img_blur*pass_power
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n = max(n-10,0)
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return img
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return img
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self.blursharpen_func = blursharpen_func
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@ -122,7 +131,7 @@ class ConvertSubprocessor(Subprocessor):
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return fanseg.extract(*args, **kwargs)
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self.fanseg_extract_func = fanseg_extract
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self.fanchq_by_face_type = {}
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self.fanchq_input_size = 256
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def fanchq_extract(face_type, *args, **kwargs):
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@ -134,13 +143,13 @@ class ConvertSubprocessor(Subprocessor):
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return fanchq.extract(*args, **kwargs)
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self.fanchq_extract_func = fanchq_extract
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import ebsynth
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def ebs_ct(*args, **kwargs):
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def ebs_ct(*args, **kwargs):
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return ebsynth.color_transfer(*args, **kwargs)
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self.ebs_ct_func = ebs_ct
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return None
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#override
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@ -188,7 +197,7 @@ class ConvertSubprocessor(Subprocessor):
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raise Exception( 'Error while converting file [%s]: %s' % (filename, e_str) )
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elif cfg.type == ConverterConfig.TYPE_FACE_AVATAR:
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final_img = ConvertFaceAvatar (self.predictor_func, self.predictor_input_shape,
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final_img = ConvertFaceAvatar (self.predictor_func, self.predictor_input_shape,
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cfg, pf.prev_temporal_frame_infos,
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pf.frame_info,
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pf.next_temporal_frame_infos )
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@ -241,7 +250,7 @@ class ConvertSubprocessor(Subprocessor):
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session_data = None
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if self.is_interactive and self.converter_session_filepath.exists():
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if io.input_bool ("Use saved session? (y/n skip:y) : ", True):
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try:
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with open( str(self.converter_session_filepath), "rb") as f:
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@ -276,10 +285,19 @@ class ConvertSubprocessor(Subprocessor):
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if frames_equal:
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io.log_info ('Using saved session from ' + '/'.join (self.converter_session_filepath.parts[-2:]) )
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for frame in s_frames:
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if frame.cfg is not None:
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#recreate ConverterConfig class using constructor with get_config() as dict params
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#so if any new param will be added, old converter session will work properly
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frame.cfg = frame.cfg.__class__( **frame.cfg.get_config() )
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self.frames = s_frames
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self.frames_idxs = s_frames_idxs
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self.frames_done_idxs = s_frames_done_idxs
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if self.model_iter != s_model_iter:
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#model is more trained, recompute all frames
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for frame in self.frames:
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@ -288,7 +306,7 @@ class ConvertSubprocessor(Subprocessor):
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if self.model_iter != s_model_iter or \
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len(self.frames_idxs) == 0:
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#rewind to begin if model is more trained or all frames are done
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while len(self.frames_done_idxs) > 0:
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prev_frame = self.frames[self.frames_done_idxs.pop()]
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self.frames_idxs.insert(0, prev_frame.idx)
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@ -367,7 +385,7 @@ class ConvertSubprocessor(Subprocessor):
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io.log_info ("Session is saved to " + '/'.join (self.converter_session_filepath.parts[-2:]) )
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cfg_change_keys = ['`','1', '2', '3', '4', '5', '6', '7', '8', '9',
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'q', 'a', 'w', 's', 'e', 'd', 'r', 'f', 't', 'g','y','h','u','j',
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'q', 'a', 'w', 's', 'e', 'd', 'r', 'f', 'y','h','u','j','i','k','o','l','p', ';',':',#'t', 'g',
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'z', 'x', 'c', 'v', 'b','n' ]
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#override
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def on_tick(self):
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@ -447,10 +465,6 @@ class ConvertSubprocessor(Subprocessor):
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cfg.add_motion_blur_power(1 if not shift_pressed else 5)
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elif chr_key == 'f':
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cfg.add_motion_blur_power(-1 if not shift_pressed else -5)
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elif chr_key == 't':
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cfg.add_color_degrade_power(1 if not shift_pressed else 5)
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elif chr_key == 'g':
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cfg.add_color_degrade_power(-1 if not shift_pressed else -5)
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elif chr_key == 'y':
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cfg.add_blursharpen_amount(1 if not shift_pressed else 5)
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elif chr_key == 'h':
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@ -459,6 +473,21 @@ class ConvertSubprocessor(Subprocessor):
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cfg.add_output_face_scale(1 if not shift_pressed else 5)
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elif chr_key == 'j':
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cfg.add_output_face_scale(-1 if not shift_pressed else -5)
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elif chr_key == 'i':
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cfg.add_image_denoise_power(1 if not shift_pressed else 5)
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elif chr_key == 'k':
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cfg.add_image_denoise_power(-1 if not shift_pressed else -5)
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elif chr_key == 'o':
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cfg.add_bicubic_degrade_power(1 if not shift_pressed else 5)
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elif chr_key == 'l':
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cfg.add_bicubic_degrade_power(-1 if not shift_pressed else -5)
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elif chr_key == 'p':
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cfg.add_color_degrade_power(1 if not shift_pressed else 5)
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elif chr_key == ';':
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cfg.add_color_degrade_power(-1)
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elif chr_key == ':':
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cfg.add_color_degrade_power(-5)
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elif chr_key == 'z':
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cfg.toggle_masked_hist_match()
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@ -579,7 +608,7 @@ class ConvertSubprocessor(Subprocessor):
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for i in range ( min(len(self.frames_idxs), self.prefetch_frame_count) ):
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frame = self.frames[ self.frames_idxs[i] ]
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if not frame.is_done and not frame.is_processing and frame.cfg is not None:
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if not frame.is_done and not frame.is_processing and frame.cfg is not None:
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frame.is_processing = True
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return ConvertSubprocessor.ProcessingFrame(idx=frame.idx,
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cfg=frame.cfg.copy(),
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@ -623,7 +652,7 @@ def main (args, device_args):
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import models
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model = models.import_model( args['model_name'])(model_path, device_args=device_args, training_data_src_path=training_data_src_path)
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converter_session_filepath = model.get_strpath_storage_for_file('converter_session.dat')
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converter_session_filepath = model.get_strpath_storage_for_file('converter_session.dat')
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predictor_func, predictor_input_shape, cfg = model.get_ConverterConfig()
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if not is_interactive:
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@ -750,8 +779,8 @@ def main (args, device_args):
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is_interactive = is_interactive,
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converter_session_filepath = converter_session_filepath,
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predictor_func = predictor_func,
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predictor_input_shape = predictor_input_shape,
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converter_config = cfg,
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predictor_input_shape = predictor_input_shape,
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converter_config = cfg,
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frames = frames,
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output_path = output_path,
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model_iter = model.get_iter()
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