Whenever I trying faceswap, it always stuck on this stage. Can anyone please help me.
Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}}
inswapper-shape: [1, 3, 128, 128]
Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}}
find model: /root/.insightface/models/buffalo_l/1k3d68.onnx landmark_3d_68 ['None', 3, 192, 192] 0.0 1.0
Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}}
find model: /root/.insightface/models/buffalo_l/2d106det.onnx landmark_2d_106 ['None', 3, 192, 192] 0.0 1.0
Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}}
find model: /root/.insightface/models/buffalo_l/det_10g.onnx detection [1, 3, '?', '?'] 127.5 128.0
Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}}
find model: /root/.insightface/models/buffalo_l/genderage.onnx genderage ['None', 3, 96, 96] 0.0 1.0
Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}}
find model: /root/.insightface/models/buffalo_l/w600k_r50.onnx recognition ['None', 3, 112, 112] 127.5 127.5
set det-size: (640, 640)
Reading video frames for face swap...
/opt/conda/lib/python3.10/site-packages/insightface/utils/transform.py:68: FutureWarning: rcond parameter will change to the default of machine precision times max(M, N) where M and N are the input matrix dimensions.
To use the future default and silence this warning we advise to pass rcond=None, to keep using the old, explicitly pass rcond=-1.
P = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4
Using cuda for inference.122 of 122 -
Reading video frames...
Number of frames available for inference: 122
(80, 401)
Length of mel chunks: 122
0%| | 0/1 [00:00<?, ?it/s]
Whenever I trying faceswap, it always stuck on this stage. Can anyone please help me.
Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}} inswapper-shape: [1, 3, 128, 128] Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}} find model: /root/.insightface/models/buffalo_l/1k3d68.onnx landmark_3d_68 ['None', 3, 192, 192] 0.0 1.0 Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}} find model: /root/.insightface/models/buffalo_l/2d106det.onnx landmark_2d_106 ['None', 3, 192, 192] 0.0 1.0 Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}} find model: /root/.insightface/models/buffalo_l/det_10g.onnx detection [1, 3, '?', '?'] 127.5 128.0 Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}} find model: /root/.insightface/models/buffalo_l/genderage.onnx genderage ['None', 3, 96, 96] 0.0 1.0 Applied providers: ['CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}} find model: /root/.insightface/models/buffalo_l/w600k_r50.onnx recognition ['None', 3, 112, 112] 127.5 127.5 set det-size: (640, 640) Reading video frames for face swap... /opt/conda/lib/python3.10/site-packages/insightface/utils/transform.py:68: FutureWarning:
rcond
parameter will change to the default of machine precision timesmax(M, N)
where M and N are the input matrix dimensions. To use the future default and silence this warning we advise to passrcond=None
, to keep using the old, explicitly passrcond=-1
. P = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4 Using cuda for inference.122 of 122 - Reading video frames... Number of frames available for inference: 122 (80, 401) Length of mel chunks: 122 0%| | 0/1 [00:00<?, ?it/s]