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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
Added interactive converter.
With interactive converter you can change any parameter of any frame and see the result in real time. Converter: added motion_blur_power param. Motion blur is applied by precomputed motion vectors. So the moving face will look more realistic. RecycleGAN model is removed. Added experimental AVATAR model. Minimum required VRAM is 6GB (NVIDIA), 12GB (AMD) Usage: 1) place data_src.mp4 10-20min square resolution video of news reporter sitting at the table with static background, other faces should not appear in frames. 2) process "extract images from video data_src.bat" with FULL fps 3) place data_dst.mp4 video of face who will control the src face 4) process "extract images from video data_dst FULL FPS.bat" 5) process "data_src mark faces S3FD best GPU.bat" 6) process "data_dst extract unaligned faces S3FD best GPU.bat" 7) train AVATAR.bat stage 1, tune batch size to maximum for your card (32 for 6GB), train to 50k+ iters. 8) train AVATAR.bat stage 2, tune batch size to maximum for your card (4 for 6GB), train to decent sharpness. 9) convert AVATAR.bat 10) converted to mp4.bat updated versions of modules
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iperov
parent
3f0bf2e994
commit
407ce3b1ca
46 changed files with 2394 additions and 1659 deletions
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@ -25,11 +25,11 @@ class ModelBase(object):
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def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None, pretraining_data_path=None, debug = False, device_args = None,
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ask_enable_autobackup=True,
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ask_write_preview_history=True,
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ask_target_iter=True,
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ask_batch_size=True,
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ask_write_preview_history=True,
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ask_target_iter=True,
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ask_batch_size=True,
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ask_sort_by_yaw=True,
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ask_random_flip=True,
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ask_random_flip=True,
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ask_src_scale_mod=True):
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device_args['force_gpu_idx'] = device_args.get('force_gpu_idx',-1)
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@ -55,7 +55,7 @@ class ModelBase(object):
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self.training_data_src_path = training_data_src_path
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self.training_data_dst_path = training_data_dst_path
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self.pretraining_data_path = pretraining_data_path
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self.src_images_paths = None
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self.dst_images_paths = None
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self.src_yaw_images_paths = None
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@ -106,7 +106,7 @@ class ModelBase(object):
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choose_preview_history = io.input_bool("Randomly choose new image for preview history? (y/n ?:help skip:%s) : " % (yn_str[False]), False, help_message="Preview image history will stay stuck with old faces if you reuse the same model on different celebs. Choose no unless you are changing src/dst to a new person")
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else:
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choose_preview_history = False
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if ask_target_iter:
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if (self.iter == 0 or ask_override):
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self.options['target_iter'] = max(0, io.input_int("Target iteration (skip:unlimited/default) : ", 0))
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@ -121,7 +121,7 @@ class ModelBase(object):
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else:
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self.options['batch_size'] = self.options.get('batch_size', 0)
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if ask_sort_by_yaw:
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if ask_sort_by_yaw:
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if (self.iter == 0 or ask_override):
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default_sort_by_yaw = self.options.get('sort_by_yaw', False)
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self.options['sort_by_yaw'] = io.input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:%s) : " % (yn_str[default_sort_by_yaw]), default_sort_by_yaw, help_message="NN will not learn src face directions that don't match dst face directions. Do not use if the dst face has hair that covers the jaw." )
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@ -139,7 +139,7 @@ class ModelBase(object):
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self.options['src_scale_mod'] = np.clip( io.input_int("Src face scale modifier % ( -30...30, ?:help skip:0) : ", 0, help_message="If src face shape is wider than dst, try to decrease this value to get a better result."), -30, 30)
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else:
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self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
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self.autobackup = self.options.get('autobackup', False)
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if not self.autobackup and 'autobackup' in self.options:
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self.options.pop('autobackup')
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@ -180,10 +180,10 @@ class ModelBase(object):
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else:
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self.preview_history_path = self.model_path / ( '%d_%s_history' % (self.device_args['force_gpu_idx'], self.get_model_name()) )
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self.autobackups_path = self.model_path / ( '%d_%s_autobackups' % (self.device_args['force_gpu_idx'], self.get_model_name()) )
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if self.autobackup:
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self.autobackup_current_hour = time.localtime().tm_hour
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if not self.autobackups_path.exists():
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self.autobackups_path.mkdir(exist_ok=True)
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@ -202,7 +202,7 @@ class ModelBase(object):
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if not isinstance(generator, SampleGeneratorBase):
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raise ValueError('training data generator is not subclass of SampleGeneratorBase')
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if self.sample_for_preview is None or choose_preview_history:
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if self.sample_for_preview is None or choose_preview_history:
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if choose_preview_history and io.is_support_windows():
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wnd_name = "[p] - next. [enter] - confirm."
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io.named_window(wnd_name)
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@ -221,25 +221,25 @@ class ModelBase(object):
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break
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elif key == ord('p'):
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break
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try:
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io.process_messages(0.1)
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except KeyboardInterrupt:
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choosed = True
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io.destroy_window(wnd_name)
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else:
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self.sample_for_preview = self.generate_next_sample()
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else:
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self.sample_for_preview = self.generate_next_sample()
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self.last_sample = self.sample_for_preview
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###Generate text summary of model hyperparameters
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#Find the longest key name and value string. Used as column widths.
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width_name = max([len(k) for k in self.options.keys()] + [17]) + 1 # Single space buffer to left edge. Minimum of 17, the length of the longest static string used "Current iteration"
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width_value = max([len(str(x)) for x in self.options.values()] + [len(str(self.iter)), len(self.get_model_name())]) + 1 # Single space buffer to right edge
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if not self.device_config.cpu_only: #Check length of GPU names
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width_value = max([len(nnlib.device.getDeviceName(idx))+1 for idx in self.device_config.gpu_idxs] + [width_value])
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width_value = max([len(nnlib.device.getDeviceName(idx))+1 for idx in self.device_config.gpu_idxs] + [width_value])
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width_total = width_name + width_value + 2 #Plus 2 for ": "
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model_summary_text = []
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model_summary_text += [f'=={" Model Summary ":=^{width_total}}=='] # Model/status summary
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model_summary_text += [f'=={" "*width_total}==']
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@ -247,13 +247,13 @@ class ModelBase(object):
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model_summary_text += [f'=={" "*width_total}==']
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model_summary_text += [f'=={"Current iteration": >{width_name}}: {str(self.iter): <{width_value}}=='] # Iter
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model_summary_text += [f'=={" "*width_total}==']
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model_summary_text += [f'=={" Model Options ":-^{width_total}}=='] # Model options
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model_summary_text += [f'=={" "*width_total}==']
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for key in self.options.keys():
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model_summary_text += [f'=={key: >{width_name}}: {str(self.options[key]): <{width_value}}=='] # self.options key/value pairs
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model_summary_text += [f'=={" "*width_total}==']
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model_summary_text += [f'=={" Running On ":-^{width_total}}=='] # Training hardware info
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model_summary_text += [f'=={" "*width_total}==']
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if self.device_config.multi_gpu:
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@ -266,10 +266,10 @@ class ModelBase(object):
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model_summary_text += [f'=={"Device index": >{width_name}}: {idx: <{width_value}}=='] # GPU hardware device index
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model_summary_text += [f'=={"Name": >{width_name}}: {nnlib.device.getDeviceName(idx): <{width_value}}=='] # GPU name
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vram_str = f'{nnlib.device.getDeviceVRAMTotalGb(idx):.2f}GB' # GPU VRAM - Formated as #.## (or ##.##)
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model_summary_text += [f'=={"VRAM": >{width_name}}: {vram_str: <{width_value}}==']
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model_summary_text += [f'=={"VRAM": >{width_name}}: {vram_str: <{width_value}}==']
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model_summary_text += [f'=={" "*width_total}==']
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model_summary_text += [f'=={"="*width_total}==']
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if not self.device_config.cpu_only and self.device_config.gpu_vram_gb[0] <= 2: # Low VRAM warning
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model_summary_text += ["/!\\"]
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model_summary_text += ["/!\\ WARNING:"]
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@ -277,7 +277,7 @@ class ModelBase(object):
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model_summary_text += ["/!\\ If training does not start, close all programs and try again."]
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model_summary_text += ["/!\\ Also you can disable Windows Aero Desktop to increase available VRAM."]
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model_summary_text += ["/!\\"]
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model_summary_text = "\n".join (model_summary_text)
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self.model_summary_text = model_summary_text
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io.log_info(model_summary_text)
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@ -323,6 +323,11 @@ class ModelBase(object):
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def get_model_filename_list(self):
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return []
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#overridable
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def get_ConverterConfig(self):
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#return ConverterConfig() for the model
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raise NotImplementedError
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#overridable
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def get_converter(self):
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raise NotImplementedError
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@ -372,9 +377,9 @@ class ModelBase(object):
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}
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self.model_data_path.write_bytes( pickle.dumps(model_data) )
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bckp_filename_list = [ self.get_strpath_storage_for_file(filename) for _, filename in self.get_model_filename_list() ]
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bckp_filename_list += [ str(summary_path), str(self.model_data_path) ]
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bckp_filename_list = [ self.get_strpath_storage_for_file(filename) for _, filename in self.get_model_filename_list() ]
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bckp_filename_list += [ str(summary_path), str(self.model_data_path) ]
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if self.autobackup:
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current_hour = time.localtime().tm_hour
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if self.autobackup_current_hour != current_hour:
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@ -383,20 +388,20 @@ class ModelBase(object):
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for i in range(15,0,-1):
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idx_str = '%.2d' % i
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next_idx_str = '%.2d' % (i+1)
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idx_backup_path = self.autobackups_path / idx_str
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next_idx_packup_path = self.autobackups_path / next_idx_str
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if idx_backup_path.exists():
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if i == 15:
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if i == 15:
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Path_utils.delete_all_files(idx_backup_path)
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else:
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next_idx_packup_path.mkdir(exist_ok=True)
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Path_utils.move_all_files (idx_backup_path, next_idx_packup_path)
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if i == 1:
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idx_backup_path.mkdir(exist_ok=True)
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for filename in bckp_filename_list:
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idx_backup_path.mkdir(exist_ok=True)
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for filename in bckp_filename_list:
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shutil.copy ( str(filename), str(idx_backup_path / Path(filename).name) )
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previews = self.get_previews()
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@ -440,7 +445,7 @@ class ModelBase(object):
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model.save_weights( filename + '.tmp' )
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rename_list = model_filename_list
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"""
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#unused
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, optimizer_filename_list=[]
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@ -464,7 +469,7 @@ class ModelBase(object):
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except Exception as e:
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print ("Unable to save ", opt_filename)
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"""
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for _, filename in rename_list:
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filename = self.get_strpath_storage_for_file(filename)
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source_filename = Path(filename+'.tmp')
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@ -473,7 +478,7 @@ class ModelBase(object):
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if target_filename.exists():
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target_filename.unlink()
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source_filename.rename ( str(target_filename) )
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def debug_one_iter(self):
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images = []
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for generator in self.generator_list:
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@ -579,8 +584,8 @@ class ModelBase(object):
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lh_height = 100
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lh_img = np.ones ( (lh_height,w,c) ) * 0.1
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if len(loss_history) != 0:
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if len(loss_history) != 0:
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loss_count = len(loss_history[0])
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lh_len = len(loss_history)
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