ai-forever / ghost Goto Github PK
View Code? Open in Web Editor NEWA new one shot face swap approach for image and video domains
License: Apache License 2.0
A new one shot face swap approach for image and video domains
License: Apache License 2.0
I'm experimenting other super resolution models instead of the pix2pix. However, this extra step is not necessary if the output is 512 resolution. Did anyone try to modify the model to output HD photos?
The HD training dataset is available, VGGface2HD, just like the simswap which support 512 resolution.
Trying to run the repo on Windows 10.
Installed mxnet-cu102-2.0.0b20201108.
The code execution stops on line 115 in image_infer.py file
model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
Full error: AttributeError: module 'mxnet' has no attribute 'mod'
I find the model is more effective in faceswap for video during medium or long-shots, but it really breaks down during close up shots (where the person's face takes up most of the screen).
Any advice on how to improve this?
I find that even when I increase inference rate to 60, it does not improve much.
Thanks!
$ py -m pip install mxnet
Collecting mxnet
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Installing collected packages: urllib3, idna, chardet, numpy, graphviz, certifi, requests, mxnet
WARNING: The script chardetect.exe is installed in 'C:\Users\Administrator\AppData\Local\Programs\Python\Python311\Scripts' which is not on PATH.
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Attempting uninstall: numpy
Found existing installation: numpy 1.24.0
Uninstalling numpy-1.24.0:
Successfully uninstalled numpy-1.24.0
DEPRECATION: numpy is being installed using the legacy 'setup.py install' method, because it does not have a 'pyproject.toml' and the 'wheel' package is not installed. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at pypa/pip#8559
Running setup.py install for numpy: started
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[276 lines of output]
Running from numpy source directory.
Note: if you need reliable uninstall behavior, then install
with pip instead of using setup.py install
:
- `pip install .` (from a git repo or downloaded source
release)
- `pip install numpy` (last NumPy release on PyPi)
C:\Users\Administrator\AppData\Local\Temp\pip-install-_vqy6jc2\numpy_50df99d6d0ce4aa7b689d7b0148eae3d\numpy\distutils\misc_util.py:476: SyntaxWarning: "is" with a literal. Did you mean "=="?
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error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
Rolling back uninstall of numpy
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error: legacy-install-failure
Encountered error while trying to install package.
numpy
note: This is an issue with the package mentioned above, not pip.
hint: See above for output from the failure.
this is what?
I have installed the project on windows and its working, but I noticed while my input images are both high resolution images, the swapped output face is quite blurry. It doesn't look similar to the example images. Is this a known issue, or is it is a matter of configuration? Any thoughts would be helpful.
К сожалению не умею правильно делать пулл-реквесты, поэтому прошу разработчиков поправить или сделать пулл-реквест @AlexanderGroshev
sberbank-ai/sber-swap/utils/inference/masks.py
строка 7 lmrks = np.array( lmrks.copy(), dtype=np.int )
нужно исправить на lmrks = np.array( lmrks.copy(), dtype=np.int32)
. Это уберёт предупреждения вроде Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations lmrks = np.array( lmrks.copy(), dtype=np.int )
которых реально чересчур много и которые сильно забивают полезный вывод в консоль.sber-swap/utils/inference/video_processing.py
строки 29 и 30 - в os.system(f"ffmpeg -i
добавить -v -8
, чтобы получилось os.system(f"ffmpeg -v -8 -i {video_with_sound} -vn...
Это уберёт вывод работы FFmpeg и сделает вывод в консоль более чище и не будет забивать его лишней информацией.sber-swap/utils/inference/video_processing.py
строка 156 - исправить cv2.VideoWriter_fourcc(*'MP4V')
на cv2.VideoWriter_fourcc(*'mp4v')
- это решить проблему OpenCV: FFMPEG: tag 0x5634504d/'MP4V'
и связанные с ней.Остальное что нашёл в пулл-реквесте поправил @AlexanderGroshev. Спасибо!
Stuck at:
input mean and std: 127.5 127.5
find model: ./insightface_func/models\antelope\glintr100.onnx recognition
find model: ./insightface_func/models\antelope\scrfd_10g_bnkps.onnx detection
set det-size: (640, 640)
loading ./coordinate_reg/model/2d106det 0
input mean and std: 127.5 127.5
find model: ./insightface_func/models\antelope\glintr100.onnx recognition
find model: ./insightface_func/models\antelope\scrfd_10g_bnkps.onnx detection
set det-size: (640, 640)
Tried both the inference.py and SberSwapInference.ipynb
I am on a windows machine with RTX 3070 ti
Hi guys, great job with the sber-swap implementation. The results are the best of any framework I've seen so far. The masking has been the only problem. Would it possible for you to implement anything similar to what Simswap has done for masking?
Type Error on Colab In Inference Section
TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.
Hi, firstly, thanks for sharing this amazing project, it looks really promising!
I have some questions regarding the training configurations you used for your checkpoints. I'm new to deep learning and GANs, therefore if you could answer my questions as simple as possible I would be really grateful.
I was wondering how many epochs have you trained and how long it took in order to get to the checkpoints you achieved and if you think it's possible to improve the reconstruction result (sometimes the face in the target video and source picture is not exactly aligned, or there are both source and target eyebrows visible in the end result, or when the source person is bearded the beard is only half transferred to the target video). In addition, could you share the weights you used for adversarial loss, attributes, identity loss, reconstruction loss and eye loss and in what manner I could adjust these values in order to put more weight into source person and their eyes if possible?
Sorry for bombarding you with questions, thanks in advance!
Hello! Please make it possible to save the final frames to a separate folder during video processing, as it is implemented in SimSwap. This is convenient when you can view a frame if a long video is being processed so as not to waste time if the result turns out to be bad. Or it will give the opportunity to make a GIF from these frames yourself. Thanks!
Running the colab example locally, I get this result:
As you can see, this is just the source image again with no swap. Here is my run log:
SBER Example.txt
Why is no actual swapping occurring?
I am trying to run python inference.py
but I am getting this error:
OSError: libcudart.so.11.0
I tried to first install Cuda toolkit version 10, given that requirements.txt focuses on that version. However, I got some errors around dll libraries, so I ran: pip install onnxruntime-gpu==1.8.0 mxnet-cu112==1.9.1
. This worked fine for those errors but now I am getting this one: OSError: libcudart.so.11.0
.
I tried then to install some nvidia dependencies using this command: sudo apt install nvidia-cuda-toolkit
. But the error persists.
I am using a WSL environment / Ubuntu on Windows.
Are there some methods to approach this error? Am I missing a specific version for pytorch? I have this Cuda version: CUDA Version: 11.6
.
i got this error "ModuleNotFoundError: No module named 'utils.inference'",when i run the commond
"python inference.py --source_paths "1.png" --target_image "mark.jpg" --image_to_image True"
Hi all,
We have created a Discord community for Single shot face swapping like SberSwap and SimSwap. We are working on code optimizatiobs, new models, etc. Please join us at:
colab
does not detect some faces get "Bad source images"
the faces are jpg
r/local/lib/python3.7/dist-packages/torch/nn/functional.py:3680: UserWarning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
"The default behavior for interpolate/upsample with float scale_factor changed "
Bad source images
100%|██████████| 1/1 [00:00<00:00, 38.86it/s]
1it [00:00, 391.37it/s]
100%|██████████| 1/1 [00:00<00:00, 19.83it/s]
100%|██████████| 1/1 [00:00<00:00, 2481.84it/s]
1it [00:00, 6.08it/s]
I've been attempting to fine-tune this model using the instructions provided, but the generated images seem to diverge after just a few thousand steps. See the attached image for generated samples
I've experimented with reducing/turning off all losses one-by-one. For example
Default - Diverges
weight_adv = 1
weight_attr = 10
weight_id = 20
weight_rec = 10
weight_eyes = 0
No Attr - Diverges
weight_adv = 1
weight_attr = 0
weight_id = 20
weight_rec = 10
weight_eyes = 0
No ID - Diverges
weight_adv = 1
weight_attr = 10
weight_id = 0
weight_rec = 10
weight_eyes = 0
No Reconstruction - Diverges
weight_adv = 1
weight_attr = 10
weight_id = 20
weight_rec = 0
weight_eyes = 0
The only run that produced decent results was when Adversarial loss was completely turned off. But turning off adversarial loss has it's own problems (see attached image)
weight_adv = 1
weight_attr = 10
weight_id = 20
weight_rec = 10
weight_eyes = 0
I've experimented with setting discr_force as True, this did not help produce good results.
I've experimented with reducing LR to 4e-5 (10% of default) and adding LR schedulers. This helped somewhat, but the model would still diverge after 10K-20K steps.
Was curious if you had any other suggestions - any help would be greatly appreciated!
Great work on this paper - very eager to see if we can fine-tuning to work so I can play around with some additional loss functions. I can keep sharing results here if that's helpful!
Hello! Please accept my congratulations on the publication on IEEE Xplore!) There have been no updates for a long time, do you plan to develop the repository further, or has it lost interest?
I don't mind if it's slow, as long it works. I can use other tools that also utilizes pytorch like Real-ESRGAN, with no problems on my Radeon, but this one i couldn't figure it out how to.
Hi! How to change mask height or exclude specific parts of face from swapping? There are situations when you need to exclude poorly or incorrectly formed parts of the face or cut the mask in width or height. Please, сan you tell me where in the code this can be changed? Thank you very much for your help!
Is there a way to run the swap on a folder of target/source images instead of specific pic-by-pic? This would make working with this repo a lot quicker and easier.
Hi!
Firstly, thank you for sharing this project! It looks amazing.
Having some trouble with pip install -r requirements.txt
_ERROR: Cannot install -r requirements.txt (line 10), -r requirements.txt (line 12) and requests==2.25.1 because these package versions have conflicting dependencies.
The conflict is caused by:
The user requested requests==2.25.1
insightface 0.2.1 depends on requests
mxnet-cu101mkl 1.5.0 depends on requests<2.19.0 and >=2.18.4
To fix this you could try to:
Do you have any suggestion on how to fix this conflict? Have the wrong version numbers been added in requirements.txt, perhaps?
Looking forward to a solution. Thank you again!
How do you modify the code (either the inference.py document or the code itself in the Collab Notebook) to swap multiple faces when running in Google Collab?
Hi FaceSwap,
Please state the correct versions in requirements.txt:
Hi,
Does anyone have a solution how to run this with RTX 30xx cards?
"d:\anaconda\envs\sber\lib\site-packages\torch\cuda_init_.py:125: UserWarning:
NVIDIA GeForce RTX 3070 with CUDA capability sm_86 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_61 sm_70 sm_75 compute_37."
My understanding is that to use CUDA + 30xx cards, we need CUDA 11.x .... but then after installing Torch with Cuda 11, mxnet fails to load, since it isn't supported by Cuda 11.
So... is it even possible to run this using RTX 30xx series?
Hey! Thank you very much for the work done, we hope that the code will continue to be developed and finalized. There is one question. In the description of the --target_faces_paths option, you noted that you can skip this option and then any face in the photo or video will be selected. Unfortunately, the ability to choose whether to use a face from a screenshot or to automatically select any face does not work. If you skip this parameter, then there will always be an error (for a photo or video)
List of source paths: ['/content/sber-swap/examples/images/elon_musk.jpg']
List of target paths: ['examples/images/1.png', 'examples/images/2.png', 'examples/images/3.png']
Traceback (most recent call last):
File "inference.py", line 153, in <module>
main(args)
File "inference.py", line 88, in main
img = crop_face(img, app, args.crop_size)[0]
File "/content/sber-swap/utils/inference/image_processing.py", line 16, in crop_face
image, _ = app.get(image_full, crop_size)
File "/content/sber-swap/insightface_func/face_detect_crop_multi.py", line 58, in get
metric='default')
File "/usr/local/lib/python3.7/dist-packages/insightface/model_zoo/scrfd.py", line 204, in detect
im_ratio = float(img.shape[0]) / img.shape[1]
AttributeError: 'NoneType' object has no attribute 'shape'
You can skip this parameter only if you remove default=['examples/images/1.png', 'examples/images/2.png', 'examples/images/3.png'], nargs='+'
. from it. But then, if necessary, the parameter itself will not work ... Add the ability to choose whether to use automatic face selection or from a screenshot from the video.
Привет! Спасибо большое за проделанную работу, надеемся что код и дальше будет развиваться и дорабатываться. Есть один вопрос. В описании опции --target_faces_paths вы отметили что можно пропустить эту опцию и тогда будет выбираться любое лицо на фото или видео. К сожалению возможность выбирать - использовать лицо со скриншота или чтобы автоматически выбиралось любое лицо (если оно всего одно например) не работает. Если скипнуть этот параметр то всегда (для фото или видео) будет ошибка. Скипнуть этот параметр можно только если убрать из него default=['examples/images/1.png', 'examples/images/2.png', 'examples/images/3.png'], nargs='+'
. Но тогда если будет нужно - не будет работать сам параметр...Добавьте возможность выбирать, использовать автоматический выбор лица или со скриншота с видео.
I cant get the versions working on my side. I tried some of the version but none of them working
File "C:\Users\Administrator\Desktop\Ghost\sber-swap\coordinate_reg\image_infer.py", line 4, in
import mxnet as mx
ModuleNotFoundError: No module named 'mxnet'
ANYONE HAS a solution for this error?
Dear sberbank-ai, I try to train on my datasets. and want to know how to set the weight_eyes? should I put 10 for weitght_eyes?
thanks!
Hello, how can I use sber swap on windows 10 can anyone help?
I have tried to use sberswap on anaconda with gpu and I found this issue! any suggestion?
Traceback (most recent call last):
File "inference.py", line 8, in
from utils.inference.image_processing import crop_face, get_final_image
File "/content/sber-swap/utils/inference/image_processing.py", line 10, in
from insightface.utils import face_align
File "/usr/local/lib/python3.8/site-packages/insightface/init.py", line 8, in
import onnxruntime
File "/usr/local/lib/python3.8/site-packages/onnxruntime/init.py", line 13, in
from onnxruntime.capi._pybind_state import get_all_providers, get_available_providers, get_device, set_seed,
File "/usr/local/lib/python3.8/site-packages/onnxruntime/capi/_pybind_state.py", line 9, in
import onnxruntime.capi._ld_preload # noqa: F401
File "/usr/local/lib/python3.8/site-packages/onnxruntime/capi/_ld_preload.py", line 12, in
_libcublas = CDLL("libcublas.so.10", mode=RTLD_GLOBAL)
File "/usr/local/lib/python3.8/ctypes/init.py", line 373, in init
self._handle = _dlopen(self._name, mode)
OSError: libcublas.so.10: cannot open shared object file: No such file or directory
I've noticed that for some source image (private images, not for distribution or sharing), the reconstructed face has a mouth looking way too red. Have anyone seen the same effect and maybe found a way to correct it?
Not an issue but I think this could come handy for someone :)
Dockerfile
FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04
RUN apt-get update && apt-get install -y software-properties-common
RUN add-apt-repository ppa:deadsnakes/ppa -y
RUN apt-get update && apt-get install -y \
wget \
python3.8 \
python3.8-distutils \
ffmpeg \
libsm6 \
libxext6
RUN wget https://bootstrap.pypa.io/get-pip.py
RUN python3.8 get-pip.py
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt
python3.8 inference.py --target_path {PATH_TO_IMAGE} --image_to_image True
Type Error on Colab In Inference Section, also the batch_size parameter Is highlighting as error
TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.
Hi, thanks for your sharing. I want to know which dataset is used to train the arcface model. And another question is that have you ever compared the results of using different identity extractor?
Igot this error message. whats seems to be the problem
Traceback (most recent call last):
File "C:\Users\Administrator\Desktop\ghost\preprocess_vgg.py", line 5, in
from insightface_func.face_detect_crop_single import Face_detect_crop
File "C:\Users\Administrator\Desktop\ghost\insightface_func\face_detect_crop_single.py", line 8, in
from insightface.model_zoo import model_zoo
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python39\lib\site-packages\insightface_init_.py", line 16, in
from . import model_zoo
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python39\lib\site-packages\insightface\model_zoo_init_.py", line 1, in
from .model_zoo import get_model
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python39\lib\site-packages\insightface\model_zoo\model_zoo.py", line 11, in
from .arcface_onnx import *
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python39\lib\site-packages\insightface\model_zoo\arcface_onnx.py", line 10, in
import onnx
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python39\lib\site-packages\onnx_init_.py", line 20, in
import onnx.helper # noqa
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python39\lib\site-packages\onnx\helper.py", line 17, in
from onnx import mapping
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python39\lib\site-packages\onnx\mapping.py", line 27, in
int(TensorProto.STRING): np.dtype(np.object)
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python39\lib\site-packages\numpy_init_.py", line 284, in getattr
raise AttributeError("module {!r} has no attribute "
AttributeError: module 'numpy' has no attribute 'object'
ANd this
Traceback (most recent call last):
File "C:\Users\Administrator\Desktop\ghost\train.py", line 27, in
from utils.training.detector import detect_landmarks, paint_eyes
File "C:\Users\Administrator\Desktop\ghost\utils\training\detector.py", line 6, in
from AdaptiveWingLoss.utils.utils import get_preds_fromhm
ModuleNotFoundError: No module named 'AdaptiveWingLoss.utils'
Hey guys, wondering if anyone else is having this issue. Get up to about 43% and then get an coom error. Im using a 1650 with 4gbVram and 32gb Ram on a Linux laptop. Simswap works fine, but having an issue with this.
$ pip install -r requirements.txt
Looking in links: https://download.pytorch.org/whl/torch_stable.html, https://download.pytorch.org/whl/torch_stable.html
Collecting numpy
Using cached numpy-1.22.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB)
ERROR: Could not find a version that satisfies the requirement torch==1.6.0+cu101 (from versions: none)
ERROR: No matching distribution found for torch==1.6.0+cu101
Hello ! When I run from coordinate_reg.image_infer import Handler:
OSError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_21280\3719535116.py in
----> 1 from coordinate_reg.image_infer import Handler
~\sber-swap\coordinate_reg\image_infer.py in
2 import numpy as np
3 import os
----> 4 import mxnet as mx
5 from skimage import transform as trans
6 import insightface
~\anaconda3\envs\original\lib\site-packages\mxnet_init_.py in
22 from future import absolute_import
23
---> 24 from .context import Context, current_context, cpu, gpu, cpu_pinned
25 from . import engine
26 from .base import MXNetError
~\anaconda3\envs\original\lib\site-packages\mxnet\context.py in
22 import warnings
23 import ctypes
---> 24 from .base import classproperty, with_metaclass, _MXClassPropertyMetaClass
25 from .base import _LIB
26 from .base import check_call
~\anaconda3\envs\original\lib\site-packages\mxnet\base.py in
211 version = libinfo.version
212 # library instance of mxnet
--> 213 _LIB = _load_lib()
214
215 # type definitions
~\anaconda3\envs\original\lib\site-packages\mxnet\base.py in _load_lib()
202 """Load library by searching possible path."""
203 lib_path = libinfo.find_lib_path()
--> 204 lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_LOCAL)
205 # DMatrix functions
206 lib.MXGetLastError.restype = ctypes.c_char_p
~\anaconda3\envs\original\lib\ctypes_init_.py in init(self, name, mode, handle, use_errno, use_last_error)
354
355 if handle is None:
--> 356 self._handle = _dlopen(self._name, mode)
357 else:
358 self._handle = handle
OSError: [WinError 126] Не найден указанный модуль
I have tried to swap multi faces, here is the issue I have encountered. Please share thoughts, if anyone is able to replicate the issue or has fixed it.
python3 inference.py --source_paths multispecific/SRC_01.png multispecific/SRC_02.png multispecific/SRC_03.png --target_faces_paths multispecific/DST_01.jpg multispecific/DST_02.jpg multispecific/DST_03.jpg --target_videomultispecific/multi_3s.mp4 --out_video_name out.mp4
Traceback (most recent call last):
File "inference.py", line 153, in <module>
main(args)
File "inference.py", line 104, in main
BS=args.batch_size)
File "/content/drive/MyDrive/sber-swap/utils/inference/core.py", line 63, in model_inference
target_batch_rs = transform_target_to_torch(resized_frs, half=half)
File "/content/drive/MyDrive/sber-swap/utils/inference/core.py", line 18, in transform_target_to_torch
target_batch_rs = target_batch_rs[:, :, :, [2,1,0]]/255.
IndexError: too many indices for tensor of dimension 1
@AlexanderGroshev could you please discover a little details on how you train it?
I have PyTorch CUDA 11.6 already installed, and cannot install older versions because of other projects. What do we do?
ERROR: Could not find a version that satisfies the requirement torch==1.6.0+cu101 (from versions: 1.7.1, 1.7.1+cpu, 1.7.1+cu101, 1.7.1+cu110, 1.8.0, 1.8.0+cpu, 1.8.0+cu101, 1.8.0+cu111, 1.8.1, 1.8.1+cpu, 1.8.1+cu101, 1.8.1+cu102, 1.8.1+cu111, 1.9.0, 1.9.0+cpu, 1.9.0+cu102, 1.9.0+cu111, 1.9.1, 1.9.1+cpu, 1.9.1+cu102, 1.9.1+cu111, 1.10.0, 1.10.0+cpu, 1.10.0+cu102, 1.10.0+cu111, 1.10.0+cu113, 1.10.1, 1.10.1+cpu, 1.10.1+cu102, 1.10.1+cu111, 1.10.1+cu113, 1.10.2, 1.10.2+cpu, 1.10.2+cu102, 1.10.2+cu111, 1.10.2+cu113, 1.11.0, 1.11.0+cpu, 1.11.0+cu113, 1.11.0+cu115, 1.12.0, 1.12.0+cpu, 1.12.0+cu113, 1.12.0+cu116, 1.12.1, 1.12.1+cpu, 1.12.1+cu113, 1.12.1+cu116, 1.13.0, 1.13.0+cpu, 1.13.0+cu116, 1.13.0+cu117, 1.13.1, 1.13.1+cpu, 1.13.1+cu116, 1.13.1+cu117)
ERROR: No matching distribution found for torch==1.6.0+cu101
Hi,
I've tested the video swap functionality using the provided Colab, and in every test, the face "jitters". It looks like the scale of the face is changing by a very small amount. Sometimes the eyebrows are not aligned, either. So we have two copies of the eyebrows (in the instance that the original video raises their eyebrows).
Also, the mouth & eyes do not correctly match the source.
This video shows an example of the jittery face & eyes not pointing correctly.
And this shows how the mouth should look in the source, how SimSwap correctly translates it, and how sber-swap does not have the correct eyes or mouth.
Are these issues with the approach, or would training the model for longer resolve the issues?
This project is great result at that pixel, but so sad its cant work with long video duration. Any plan to make it long ?
Even if I use a child's face image in Colab, it becomes Bad source images and I cannot exchange faces.
Hi! If possible, please correct the output of the result in the next update for a cleaner one! There are a lot of warnings that just clog up the useful output on the console. I didn't copy everything, the rest of the output (warnings) is identical. Thanks!
/usr/local/lib/python3.7/dist-packages/kornia/augmentation/augmentation.py:1833: DeprecationWarning: GaussianBlur is no longer maintained and will be removed from the future versions. Please use RandomGaussianBlur instead.
category=DeprecationWarning,
/usr/local/lib/python3.7/dist-packages/scipy/fft/__init__.py:97: DeprecationWarning: The module numpy.dual is deprecated. Instead of using dual, use the functions directly from numpy or scipy.
from numpy.dual import register_func
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.
supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
and
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
16% 46/289 [00:05<00:09, 25.51it/s]/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
17% 49/289 [00:05<00:09, 25.90it/s]/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
18% 52/289 [00:05<00:09, 26.29it/s]/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
19% 55/289 [00:05<00:08, 26.87it/s]/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
20% 58/289 [00:05<00:08, 27.16it/s]/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
21% 61/289 [00:05<00:08, 27.57it/s]/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
22% 64/289 [00:05<00:08, 25.83it/s]/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
23% 67/289 [00:05<00:08, 26.18it/s]/content/sber-swap/utils/inference/masks.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
lmrks = np.array( lmrks.copy(), dtype=np.int )
Hi FaceSwap ! When I run this code:
python inference.py --source_paths {PATH_TO_IMAGE} --target_faces_paths {PATH_TO_IMAGE} --target_video {PATH_TO_VIDEO}
I have the following error:
TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates
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