github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-08-20 13:33:24 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge commit '62f1d57871' into merge-from-upstream/2021-06-02

Browse source 
# Conflicts:
#	README.md
This commit is contained in:
Jeremy Hummel 2021-06-15 14:35:53 -07:00
commit 68552d3b7a
6 changed files with 35 additions and 30 deletions
Download patch file Download diff file Expand all files Collapse all files

8
README.md
View file

@ -1,4 +1,4 @@
[![Patreon](https://c5.patreon.com/external/logo/become_a_patron_button@2x.png)](https://www.patreon.com/bePatron?u=22997465) [![Patreon](https://c5.patreon.com/external/logo/become_a_patron_button@2x.png)](https://www.patreon.com/bePatron?u=22997465)
# CHANGELOG # CHANGELOG
### [View most recent changes](CHANGELOG.md) ### [View most recent changes](CHANGELOG.md)
@ -36,8 +36,8 @@ More than 95% of deepfake videos are created with DeepFaceLab.
DeepFaceLab is used by such popular youtube channels as DeepFaceLab is used by such popular youtube channels as
|![](doc/tiktok_icon.png) [deeptomcruise](https://www.tiktok.com/@deeptomcruise)| |![](doc/tiktok_icon.png) [deeptomcruise](https://www.tiktok.com/@deeptomcruise)|![](doc/tiktok_icon.png) [1facerussia](https://www.tiktok.com/@1facerussia)|![](doc/tiktok_icon.png) [arnoldschwarzneggar](https://www.tiktok.com/@arnoldschwarzneggar)
|---| |---|---|---|
|![](doc/youtube_icon.png) [Ctrl Shift Face](https://www.youtube.com/channel/UCKpH0CKltc73e4wh0_pgL3g)|![](doc/youtube_icon.png) [VFXChris Ume](https://www.youtube.com/channel/UCGf4OlX_aTt8DlrgiH3jN3g/videos)|![](doc/youtube_icon.png) [Sham00k](https://www.youtube.com/channel/UCZXbWcv7fSZFTAZV4beckyw/videos)| |![](doc/youtube_icon.png) [Ctrl Shift Face](https://www.youtube.com/channel/UCKpH0CKltc73e4wh0_pgL3g)|![](doc/youtube_icon.png) [VFXChris Ume](https://www.youtube.com/channel/UCGf4OlX_aTt8DlrgiH3jN3g/videos)|![](doc/youtube_icon.png) [Sham00k](https://www.youtube.com/channel/UCZXbWcv7fSZFTAZV4beckyw/videos)|
|---|---|---| |---|---|---|
@ -201,7 +201,7 @@ Unfortunately, there is no "make everything ok" button in DeepFaceLab. You shoul
</td></tr> </td></tr>
<tr><td align="right"> <tr><td align="right">
<a href="https://tinyurl.com/87vwbtr4">Windows (magnet link)</a> <a href="https://tinyurl.com/4tb2tn4w">Windows (magnet link)</a>
</td><td align="center">Last release. Use torrent client to download.</td></tr> </td><td align="center">Last release. Use torrent client to download.</td></tr>
<tr><td align="right"> <tr><td align="right">

44
core/leras/layers/Conv2D.py
View file

@ -23,28 +23,13 @@ class Conv2D(nn.LayerBase):
if padding == "SAME": if padding == "SAME":
padding = ( (kernel_size - 1) * dilations + 1 ) // 2 padding = ( (kernel_size - 1) * dilations + 1 ) // 2
elif padding == "VALID": elif padding == "VALID":
padding = 0 padding = None
else: else:
raise ValueError ("Wrong padding type. Should be VALID SAME or INT or 4x INTs") raise ValueError ("Wrong padding type. Should be VALID SAME or INT or 4x INTs")
if isinstance(padding, int):
if padding != 0:
if nn.data_format == "NHWC":
padding = [ [0,0], [padding,padding], [padding,padding], [0,0] ]
else:
padding = [ [0,0], [0,0], [padding,padding], [padding,padding] ]
else:
padding = None
if nn.data_format == "NHWC":
strides = [1,strides,strides,1]
else: else:
strides = [1,1,strides,strides] padding = int(padding)
if nn.data_format == "NHWC":
dilations = [1,dilations,dilations,1]
else:
dilations = [1,1,dilations,dilations]
self.in_ch = in_ch self.in_ch = in_ch
self.out_ch = out_ch self.out_ch = out_ch
@ -93,10 +78,27 @@ class Conv2D(nn.LayerBase):
if self.use_wscale: if self.use_wscale:
weight = weight * self.wscale weight = weight * self.wscale
if self.padding is not None: padding = self.padding
x = tf.pad (x, self.padding, mode='CONSTANT') if padding is not None:
if nn.data_format == "NHWC":
padding = [ [0,0], [padding,padding], [padding,padding], [0,0] ]
else:
padding = [ [0,0], [0,0], [padding,padding], [padding,padding] ]
x = tf.pad (x, padding, mode='CONSTANT')
x = tf.nn.conv2d(x, weight, self.strides, 'VALID', dilations=self.dilations, data_format=nn.data_format) strides = self.strides
if nn.data_format == "NHWC":
strides = [1,strides,strides,1]
else:
strides = [1,1,strides,strides]
dilations = self.dilations
if nn.data_format == "NHWC":
dilations = [1,dilations,dilations,1]
else:
dilations = [1,1,dilations,dilations]
x = tf.nn.conv2d(x, weight, strides, 'VALID', dilations=dilations, data_format=nn.data_format)
if self.use_bias: if self.use_bias:
if nn.data_format == "NHWC": if nn.data_format == "NHWC":
bias = tf.reshape (self.bias, (1,1,1,self.out_ch) ) bias = tf.reshape (self.bias, (1,1,1,self.out_ch) )

4
merger/MergeMasked.py
View file

@ -57,7 +57,9 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC) prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC)
prd_face_dst_mask_a_0 = cv2.resize (prd_face_dst_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC) prd_face_dst_mask_a_0 = cv2.resize (prd_face_dst_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC)
if cfg.mask_mode == 1: #dst if cfg.mask_mode == 0: #full
wrk_face_mask_a_0 = np.ones_like(dst_face_mask_a_0)
elif cfg.mask_mode == 1: #dst
wrk_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), interpolation=cv2.INTER_CUBIC) wrk_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), interpolation=cv2.INTER_CUBIC)
elif cfg.mask_mode == 2: #learned-prd elif cfg.mask_mode == 2: #learned-prd
wrk_face_mask_a_0 = prd_face_mask_a_0 wrk_face_mask_a_0 = prd_face_mask_a_0

3
merger/MergerConfig.py
View file

@ -81,7 +81,8 @@ mode_dict = {0:'original',
mode_str_dict = { mode_dict[key] : key for key in mode_dict.keys() } mode_str_dict = { mode_dict[key] : key for key in mode_dict.keys() }
mask_mode_dict = {1:'dst', mask_mode_dict = {0:'full',
1:'dst',
2:'learned-prd', 2:'learned-prd',
3:'learned-dst', 3:'learned-dst',
4:'learned-prd*learned-dst', 4:'learned-prd*learned-dst',

2
models/Model_AMP/Model.py
View file

@ -586,7 +586,7 @@ class AMPModel(ModelBase):
gpu_src_dst_code = tf.concat( ( tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, ae_dims_slice , lowest_dense_res, lowest_dense_res]), gpu_src_dst_code = tf.concat( ( tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, ae_dims_slice , lowest_dense_res, lowest_dense_res]),
tf.slice(gpu_dst_inter_dst_code, [0,ae_dims_slice,0,0], [-1,ae_dims-ae_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 ) tf.slice(gpu_dst_inter_dst_code, [0,ae_dims_slice,0,0], [-1,ae_dims-ae_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_dst_inter_src_code) gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
_, gpu_pred_dst_dstm = self.decoder(gpu_dst_inter_dst_code) _, gpu_pred_dst_dstm = self.decoder(gpu_dst_inter_dst_code)
def AE_merge(warped_dst, morph_value): def AE_merge(warped_dst, morph_value):

2
samplelib/SampleLoader.py
View file

@ -23,7 +23,7 @@ class SampleLoader:
try: try:
samples = samplelib.PackedFaceset.load(samples_path) samples = samplelib.PackedFaceset.load(samples_path)
except: except:
io.log_err(f"Error occured while loading samplelib.PackedFaceset.load {str(samples_dat_path)}, {traceback.format_exc()}") io.log_err(f"Error occured while loading samplelib.PackedFaceset.load {str(samples_path)}, {traceback.format_exc()}")
if samples is None: if samples is None:
raise ValueError("packed faceset not found.") raise ValueError("packed faceset not found.")