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Now you can replace the head. Example: https://www.youtube.com/watch?v=xr5FHd0AdlQ Requirements: Post processing skill in Adobe After Effects or Davinci Resolve. Usage: 1) Find suitable dst footage with the monotonous background behind head 2) Use “extract head” script 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor 5) Train XSeg 6) Apply trained XSeg mask for src and dst headsets 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. You can use pretrained model for head. Minimum recommended resolution for head is 224. 8) Extract multiple tracks, using Merger: a. Raw-rgb b. XSeg-prd mask c. XSeg-dst mask 9) Using AAE or DavinciResolve, do: a. Hide source head using XSeg-prd mask: content-aware-fill, clone-stamp, background retraction, or other technique b. Overlay new head using XSeg-dst mask Warning: Head faceset can be used for whole_face or less types of training only with XSeg masking. XSegEditor: added button ‘view trained XSeg mask’, so you can see which frames should be masked to improve mask quality.
216 lines
No EOL
10 KiB
Python
216 lines
No EOL
10 KiB
Python
import multiprocessing
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import operator
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from functools import partial
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import numpy as np
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from core import mathlib
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from core.interact import interact as io
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from core.leras import nn
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from facelib import FaceType, XSegNet
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from models import ModelBase
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from samplelib import *
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class XSegModel(ModelBase):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, force_model_class_name='XSeg', **kwargs)
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#override
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def on_initialize_options(self):
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self.set_batch_size(4)
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ask_override = self.ask_override()
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if not self.is_first_run() and ask_override:
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if io.input_bool(f"Restart training?", False, help_message="Reset model weights and start training from scratch."):
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self.set_iter(0)
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default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'wf')
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if self.is_first_run():
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self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf','head'], help_message="Half / mid face / full face / whole face / head. Choose the same as your deepfake model.").lower()
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#override
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def on_initialize(self):
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device_config = nn.getCurrentDeviceConfig()
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self.model_data_format = "NCHW" if len(device_config.devices) != 0 and not self.is_debug() else "NHWC"
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nn.initialize(data_format=self.model_data_format)
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tf = nn.tf
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device_config = nn.getCurrentDeviceConfig()
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devices = device_config.devices
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self.resolution = resolution = 256
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self.face_type = {'h' : FaceType.HALF,
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'mf' : FaceType.MID_FULL,
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'f' : FaceType.FULL,
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'wf' : FaceType.WHOLE_FACE,
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'head' : FaceType.HEAD}[ self.options['face_type'] ]
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place_model_on_cpu = len(devices) == 0
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models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0'
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bgr_shape = nn.get4Dshape(resolution,resolution,3)
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mask_shape = nn.get4Dshape(resolution,resolution,1)
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# Initializing model classes
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self.model = XSegNet(name='XSeg',
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resolution=resolution,
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load_weights=not self.is_first_run(),
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weights_file_root=self.get_model_root_path(),
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training=True,
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place_model_on_cpu=place_model_on_cpu,
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optimizer=nn.RMSprop(lr=0.0001, lr_dropout=0.3, name='opt'),
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data_format=nn.data_format)
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if self.is_training:
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# Adjust batch size for multiple GPU
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gpu_count = max(1, len(devices) )
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bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
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self.set_batch_size( gpu_count*bs_per_gpu)
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# Compute losses per GPU
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gpu_pred_list = []
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gpu_losses = []
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gpu_loss_gvs = []
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for gpu_id in range(gpu_count):
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with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
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with tf.device(f'/CPU:0'):
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# slice on CPU, otherwise all batch data will be transfered to GPU first
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batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
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gpu_input_t = self.model.input_t [batch_slice,:,:,:]
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gpu_target_t = self.model.target_t [batch_slice,:,:,:]
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# process model tensors
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gpu_pred_logits_t, gpu_pred_t = self.model.flow(gpu_input_t)
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gpu_pred_list.append(gpu_pred_t)
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gpu_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=gpu_target_t, logits=gpu_pred_logits_t), axis=[1,2,3])
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gpu_losses += [gpu_loss]
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gpu_loss_gvs += [ nn.gradients ( gpu_loss, self.model.get_weights() ) ]
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# Average losses and gradients, and create optimizer update ops
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with tf.device (models_opt_device):
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pred = nn.concat(gpu_pred_list, 0)
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loss = tf.reduce_mean(gpu_losses)
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loss_gv_op = self.model.opt.get_update_op (nn.average_gv_list (gpu_loss_gvs))
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# Initializing training and view functions
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def train(input_np, target_np):
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l, _ = nn.tf_sess.run ( [loss, loss_gv_op], feed_dict={self.model.input_t :input_np, self.model.target_t :target_np })
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return l
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self.train = train
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def view(input_np):
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return nn.tf_sess.run ( [pred], feed_dict={self.model.input_t :input_np})
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self.view = view
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# initializing sample generators
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cpu_count = min(multiprocessing.cpu_count(), 8)
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src_dst_generators_count = cpu_count // 2
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src_generators_count = cpu_count // 2
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dst_generators_count = cpu_count // 2
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srcdst_generator = SampleGeneratorFaceXSeg([self.training_data_src_path, self.training_data_dst_path],
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debug=self.is_debug(),
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batch_size=self.get_batch_size(),
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resolution=resolution,
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face_type=self.face_type,
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generators_count=src_dst_generators_count,
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data_format=nn.data_format)
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src_generator = SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(random_flip=False),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':False, 'channel_type' : SampleProcessor.ChannelType.BGR, 'border_replicate':False, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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generators_count=src_generators_count,
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raise_on_no_data=False )
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dst_generator = SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(random_flip=False),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':False, 'channel_type' : SampleProcessor.ChannelType.BGR, 'border_replicate':False, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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generators_count=dst_generators_count,
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raise_on_no_data=False )
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self.set_training_data_generators ([srcdst_generator, src_generator, dst_generator])
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#override
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def get_model_filename_list(self):
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return self.model.model_filename_list
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#override
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def onSave(self):
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self.model.save_weights()
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#override
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def onTrainOneIter(self):
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image_np, mask_np = self.generate_next_samples()[0]
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loss = self.train (image_np, mask_np)
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return ( ('loss', loss ), )
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#override
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def onGetPreview(self, samples):
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n_samples = min(4, self.get_batch_size(), 800 // self.resolution )
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srcdst_samples, src_samples, dst_samples = samples
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image_np, mask_np = srcdst_samples
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I, M, IM, = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([image_np,mask_np] + self.view (image_np) ) ]
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M, IM, = [ np.repeat (x, (3,), -1) for x in [M, IM] ]
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green_bg = np.tile( np.array([0,1,0], dtype=np.float32)[None,None,...], (self.resolution,self.resolution,1) )
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result = []
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st = []
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for i in range(n_samples):
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ar = I[i]*M[i]+0.5*I[i]*(1-M[i])+0.5*green_bg*(1-M[i]), IM[i], I[i]*IM[i]+0.5*I[i]*(1-IM[i]) + 0.5*green_bg*(1-IM[i])
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st.append ( np.concatenate ( ar, axis=1) )
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result += [ ('XSeg training faces', np.concatenate (st, axis=0 )), ]
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if len(src_samples) != 0:
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src_np, = src_samples
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D, DM, = [ np.clip(nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([src_np] + self.view (src_np) ) ]
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DM, = [ np.repeat (x, (3,), -1) for x in [DM] ]
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st = []
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for i in range(n_samples):
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ar = D[i], DM[i], D[i]*DM[i] + 0.5*D[i]*(1-DM[i]) + 0.5*green_bg*(1-DM[i])
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st.append ( np.concatenate ( ar, axis=1) )
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result += [ ('XSeg src faces', np.concatenate (st, axis=0 )), ]
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if len(dst_samples) != 0:
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dst_np, = dst_samples
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D, DM, = [ np.clip(nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([dst_np] + self.view (dst_np) ) ]
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DM, = [ np.repeat (x, (3,), -1) for x in [DM] ]
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st = []
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for i in range(n_samples):
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ar = D[i], DM[i], D[i]*DM[i] + 0.5*D[i]*(1-DM[i]) + 0.5*green_bg*(1-DM[i])
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st.append ( np.concatenate ( ar, axis=1) )
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result += [ ('XSeg dst faces', np.concatenate (st, axis=0 )), ]
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return result
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Model = XSegModel |