mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
76ca79216e
Removed the wait at first launch for most graphics cards. Increased speed of training by 10-20%, but you have to retrain all models from scratch. SAEHD: added option 'use float16' Experimental option. Reduces the model size by half. Increases the speed of training. Decreases the accuracy of the model. The model may collapse or not train. Model may not learn the mask in large resolutions. true_face_training option is replaced by "True face power". 0.0000 .. 1.0 Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination. Comparison - https://i.imgur.com/czScS9q.png
324 lines
No EOL
13 KiB
Python
324 lines
No EOL
13 KiB
Python
import operator
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from pathlib import Path
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import cv2
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import numpy as np
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from core.leras import nn
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class FaceEnhancer(object):
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"""
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x4 face enhancer
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"""
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def __init__(self, place_model_on_cpu=False):
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nn.initialize(data_format="NHWC")
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tf = nn.tf
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class FaceEnhancer (nn.ModelBase):
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def __init__(self, name='FaceEnhancer'):
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super().__init__(name=name)
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def on_build(self):
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self.conv1 = nn.Conv2D (3, 64, kernel_size=3, strides=1, padding='SAME')
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self.dense1 = nn.Dense (1, 64, use_bias=False)
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self.dense2 = nn.Dense (1, 64, use_bias=False)
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self.e0_conv0 = nn.Conv2D (64, 64, kernel_size=3, strides=1, padding='SAME')
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self.e0_conv1 = nn.Conv2D (64, 64, kernel_size=3, strides=1, padding='SAME')
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self.e1_conv0 = nn.Conv2D (64, 112, kernel_size=3, strides=1, padding='SAME')
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self.e1_conv1 = nn.Conv2D (112, 112, kernel_size=3, strides=1, padding='SAME')
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self.e2_conv0 = nn.Conv2D (112, 192, kernel_size=3, strides=1, padding='SAME')
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self.e2_conv1 = nn.Conv2D (192, 192, kernel_size=3, strides=1, padding='SAME')
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self.e3_conv0 = nn.Conv2D (192, 336, kernel_size=3, strides=1, padding='SAME')
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self.e3_conv1 = nn.Conv2D (336, 336, kernel_size=3, strides=1, padding='SAME')
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self.e4_conv0 = nn.Conv2D (336, 512, kernel_size=3, strides=1, padding='SAME')
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self.e4_conv1 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
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self.center_conv0 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
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self.center_conv1 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
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self.center_conv2 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
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self.center_conv3 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
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self.d4_conv0 = nn.Conv2D (1024, 512, kernel_size=3, strides=1, padding='SAME')
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self.d4_conv1 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
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self.d3_conv0 = nn.Conv2D (848, 512, kernel_size=3, strides=1, padding='SAME')
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self.d3_conv1 = nn.Conv2D (512, 512, kernel_size=3, strides=1, padding='SAME')
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self.d2_conv0 = nn.Conv2D (704, 288, kernel_size=3, strides=1, padding='SAME')
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self.d2_conv1 = nn.Conv2D (288, 288, kernel_size=3, strides=1, padding='SAME')
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self.d1_conv0 = nn.Conv2D (400, 160, kernel_size=3, strides=1, padding='SAME')
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self.d1_conv1 = nn.Conv2D (160, 160, kernel_size=3, strides=1, padding='SAME')
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self.d0_conv0 = nn.Conv2D (224, 96, kernel_size=3, strides=1, padding='SAME')
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self.d0_conv1 = nn.Conv2D (96, 96, kernel_size=3, strides=1, padding='SAME')
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self.out1x_conv0 = nn.Conv2D (96, 48, kernel_size=3, strides=1, padding='SAME')
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self.out1x_conv1 = nn.Conv2D (48, 3, kernel_size=3, strides=1, padding='SAME')
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self.dec2x_conv0 = nn.Conv2D (96, 96, kernel_size=3, strides=1, padding='SAME')
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self.dec2x_conv1 = nn.Conv2D (96, 96, kernel_size=3, strides=1, padding='SAME')
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self.out2x_conv0 = nn.Conv2D (96, 48, kernel_size=3, strides=1, padding='SAME')
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self.out2x_conv1 = nn.Conv2D (48, 3, kernel_size=3, strides=1, padding='SAME')
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self.dec4x_conv0 = nn.Conv2D (96, 72, kernel_size=3, strides=1, padding='SAME')
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self.dec4x_conv1 = nn.Conv2D (72, 72, kernel_size=3, strides=1, padding='SAME')
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self.out4x_conv0 = nn.Conv2D (72, 36, kernel_size=3, strides=1, padding='SAME')
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self.out4x_conv1 = nn.Conv2D (36, 3 , kernel_size=3, strides=1, padding='SAME')
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def forward(self, inp):
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bgr, param, param1 = inp
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x = self.conv1(bgr)
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a = self.dense1(param)
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a = tf.reshape(a, (-1,1,1,64) )
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b = self.dense2(param1)
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b = tf.reshape(b, (-1,1,1,64) )
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x = tf.nn.leaky_relu(x+a+b, 0.1)
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x = tf.nn.leaky_relu(self.e0_conv0(x), 0.1)
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x = e0 = tf.nn.leaky_relu(self.e0_conv1(x), 0.1)
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x = tf.nn.avg_pool(x, [1,2,2,1], [1,2,2,1], "VALID")
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x = tf.nn.leaky_relu(self.e1_conv0(x), 0.1)
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x = e1 = tf.nn.leaky_relu(self.e1_conv1(x), 0.1)
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x = tf.nn.avg_pool(x, [1,2,2,1], [1,2,2,1], "VALID")
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x = tf.nn.leaky_relu(self.e2_conv0(x), 0.1)
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x = e2 = tf.nn.leaky_relu(self.e2_conv1(x), 0.1)
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x = tf.nn.avg_pool(x, [1,2,2,1], [1,2,2,1], "VALID")
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x = tf.nn.leaky_relu(self.e3_conv0(x), 0.1)
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x = e3 = tf.nn.leaky_relu(self.e3_conv1(x), 0.1)
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x = tf.nn.avg_pool(x, [1,2,2,1], [1,2,2,1], "VALID")
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x = tf.nn.leaky_relu(self.e4_conv0(x), 0.1)
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x = e4 = tf.nn.leaky_relu(self.e4_conv1(x), 0.1)
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x = tf.nn.avg_pool(x, [1,2,2,1], [1,2,2,1], "VALID")
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x = tf.nn.leaky_relu(self.center_conv0(x), 0.1)
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x = tf.nn.leaky_relu(self.center_conv1(x), 0.1)
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x = tf.nn.leaky_relu(self.center_conv2(x), 0.1)
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x = tf.nn.leaky_relu(self.center_conv3(x), 0.1)
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x = tf.concat( [nn.tf_upsample2d_bilinear(x), e4], -1 )
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x = tf.nn.leaky_relu(self.d4_conv0(x), 0.1)
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x = tf.nn.leaky_relu(self.d4_conv1(x), 0.1)
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x = tf.concat( [nn.tf_upsample2d_bilinear(x), e3], -1 )
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x = tf.nn.leaky_relu(self.d3_conv0(x), 0.1)
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x = tf.nn.leaky_relu(self.d3_conv1(x), 0.1)
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x = tf.concat( [nn.tf_upsample2d_bilinear(x), e2], -1 )
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x = tf.nn.leaky_relu(self.d2_conv0(x), 0.1)
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x = tf.nn.leaky_relu(self.d2_conv1(x), 0.1)
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x = tf.concat( [nn.tf_upsample2d_bilinear(x), e1], -1 )
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x = tf.nn.leaky_relu(self.d1_conv0(x), 0.1)
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x = tf.nn.leaky_relu(self.d1_conv1(x), 0.1)
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x = tf.concat( [nn.tf_upsample2d_bilinear(x), e0], -1 )
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x = tf.nn.leaky_relu(self.d0_conv0(x), 0.1)
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x = d0 = tf.nn.leaky_relu(self.d0_conv1(x), 0.1)
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x = tf.nn.leaky_relu(self.out1x_conv0(x), 0.1)
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x = self.out1x_conv1(x)
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out1x = bgr + tf.nn.tanh(x)
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x = d0
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x = tf.nn.leaky_relu(self.dec2x_conv0(x), 0.1)
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x = tf.nn.leaky_relu(self.dec2x_conv1(x), 0.1)
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x = d2x = nn.tf_upsample2d_bilinear(x)
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x = tf.nn.leaky_relu(self.out2x_conv0(x), 0.1)
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x = self.out2x_conv1(x)
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out2x = nn.tf_upsample2d_bilinear(out1x) + tf.nn.tanh(x)
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x = d2x
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x = tf.nn.leaky_relu(self.dec4x_conv0(x), 0.1)
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x = tf.nn.leaky_relu(self.dec4x_conv1(x), 0.1)
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x = d4x = nn.tf_upsample2d_bilinear(x)
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x = tf.nn.leaky_relu(self.out4x_conv0(x), 0.1)
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x = self.out4x_conv1(x)
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out4x = nn.tf_upsample2d_bilinear(out2x) + tf.nn.tanh(x)
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return out4x
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model_path = Path(__file__).parent / "FaceEnhancer.npy"
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if not model_path.exists():
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raise Exception("Unable to load FaceEnhancer.npy")
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e = tf.device("/CPU:0") if place_model_on_cpu else None
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if e is not None: e.__enter__()
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self.model = FaceEnhancer()
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self.model.load_weights (model_path)
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if e is not None: e.__exit__(None,None,None)
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self.model.build_for_run ([ (tf.float32, nn.get4Dshape (192,192,3) ),
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(tf.float32, (None,1,) ),
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(tf.float32, (None,1,) ),
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])
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def enhance (self, inp_img, is_tanh=False, preserve_size=True):
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if not is_tanh:
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inp_img = np.clip( inp_img * 2 -1, -1, 1 )
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param = np.array([0.2])
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param1 = np.array([1.0])
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up_res = 4
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patch_size = 192
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patch_size_half = patch_size // 2
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ih,iw,ic = inp_img.shape
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h,w,c = ih,iw,ic
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th,tw = h*up_res, w*up_res
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t_padding = 0
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b_padding = 0
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l_padding = 0
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r_padding = 0
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if h < patch_size:
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t_padding = (patch_size-h)//2
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b_padding = (patch_size-h) - t_padding
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if w < patch_size:
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l_padding = (patch_size-w)//2
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r_padding = (patch_size-w) - l_padding
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if t_padding != 0:
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inp_img = np.concatenate ([ np.zeros ( (t_padding,w,c), dtype=np.float32 ), inp_img ], axis=0 )
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h,w,c = inp_img.shape
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if b_padding != 0:
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inp_img = np.concatenate ([ inp_img, np.zeros ( (b_padding,w,c), dtype=np.float32 ) ], axis=0 )
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h,w,c = inp_img.shape
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if l_padding != 0:
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inp_img = np.concatenate ([ np.zeros ( (h,l_padding,c), dtype=np.float32 ), inp_img ], axis=1 )
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h,w,c = inp_img.shape
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if r_padding != 0:
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inp_img = np.concatenate ([ inp_img, np.zeros ( (h,r_padding,c), dtype=np.float32 ) ], axis=1 )
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h,w,c = inp_img.shape
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i_max = w-patch_size+1
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j_max = h-patch_size+1
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final_img = np.zeros ( (h*up_res,w*up_res,c), dtype=np.float32 )
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final_img_div = np.zeros ( (h*up_res,w*up_res,1), dtype=np.float32 )
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x = np.concatenate ( [ np.linspace (0,1,patch_size_half*up_res), np.linspace (1,0,patch_size_half*up_res) ] )
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x,y = np.meshgrid(x,x)
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patch_mask = (x*y)[...,None]
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j=0
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while j < j_max:
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i = 0
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while i < i_max:
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patch_img = inp_img[j:j+patch_size, i:i+patch_size,:]
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x = self.model.run( [ patch_img[None,...], [param], [param1] ] )[0]
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final_img [j*up_res:(j+patch_size)*up_res, i*up_res:(i+patch_size)*up_res,:] += x*patch_mask
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final_img_div[j*up_res:(j+patch_size)*up_res, i*up_res:(i+patch_size)*up_res,:] += patch_mask
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if i == i_max-1:
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break
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i = min( i+patch_size_half, i_max-1)
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if j == j_max-1:
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break
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j = min( j+patch_size_half, j_max-1)
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final_img_div[final_img_div==0] = 1.0
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final_img /= final_img_div
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if t_padding+b_padding+l_padding+r_padding != 0:
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final_img = final_img [t_padding*up_res:(h-b_padding)*up_res, l_padding*up_res:(w-r_padding)*up_res,:]
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if preserve_size:
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final_img = cv2.resize (final_img, (iw,ih), cv2.INTER_LANCZOS4)
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if not is_tanh:
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final_img = np.clip( final_img/2+0.5, 0, 1 )
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return final_img
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"""
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def enhance (self, inp_img, is_tanh=False, preserve_size=True):
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if not is_tanh:
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inp_img = np.clip( inp_img * 2 -1, -1, 1 )
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param = np.array([0.2])
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param1 = np.array([1.0])
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up_res = 4
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patch_size = 192
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patch_size_half = patch_size // 2
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h,w,c = inp_img.shape
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th,tw = h*up_res, w*up_res
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preupscale_rate = 1.0
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if h < patch_size or w < patch_size:
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preupscale_rate = 1.0 / ( max(h,w) / patch_size )
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if preupscale_rate != 1.0:
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inp_img = cv2.resize (inp_img, ( int(w*preupscale_rate), int(h*preupscale_rate) ), cv2.INTER_LANCZOS4)
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h,w,c = inp_img.shape
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i_max = w-patch_size+1
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j_max = h-patch_size+1
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final_img = np.zeros ( (h*up_res,w*up_res,c), dtype=np.float32 )
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final_img_div = np.zeros ( (h*up_res,w*up_res,1), dtype=np.float32 )
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x = np.concatenate ( [ np.linspace (0,1,patch_size_half*up_res), np.linspace (1,0,patch_size_half*up_res) ] )
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x,y = np.meshgrid(x,x)
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patch_mask = (x*y)[...,None]
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j=0
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while j < j_max:
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i = 0
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while i < i_max:
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patch_img = inp_img[j:j+patch_size, i:i+patch_size,:]
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x = self.model.run( [ patch_img[None,...], [param], [param1] ] )[0]
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final_img [j*up_res:(j+patch_size)*up_res, i*up_res:(i+patch_size)*up_res,:] += x*patch_mask
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final_img_div[j*up_res:(j+patch_size)*up_res, i*up_res:(i+patch_size)*up_res,:] += patch_mask
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if i == i_max-1:
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break
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i = min( i+patch_size_half, i_max-1)
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if j == j_max-1:
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break
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j = min( j+patch_size_half, j_max-1)
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final_img_div[final_img_div==0] = 1.0
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final_img /= final_img_div
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if preserve_size:
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final_img = cv2.resize (final_img, (w,h), cv2.INTER_LANCZOS4)
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else:
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if preupscale_rate != 1.0:
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final_img = cv2.resize (final_img, (tw,th), cv2.INTER_LANCZOS4)
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if not is_tanh:
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final_img = np.clip( final_img/2+0.5, 0, 1 )
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return final_img
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""" |