iglict / deepfaceediting-jittor Goto Github PK

Hi, thank you for showing such a great project. I am very interested in the work you have done, can you please share the training code and the sketch dataset needed for the training?

Hi, thanks for your excellent work. But now I found that the link for downloading pre-trained model was unavailable. Could you please share it again? Thanks a lot!!!

Hi, I recently tested this model by using some hand-drawn sketches (rather sketches by sketch generators). I found the generated images look much worse than using generated sketches.

For example when the input sketch is

Your model will just output such an immature unrealistic facial image.

To make sure this is not the problem with my drawing, I also tested the sketches shown on your paper (e.g., Fig. 15). The generated results are still much worse than the ones shown on the paper.

For example, this sketched head shown in Fig. 15

generates the face below,

this is completely different from the results in Fig. 15.

Could you please explain why such differences exist, and how to generate the correct images.

Thanks in advance.

Hi:

My env:

• OS: Ubuntu 20.04
• Cuda: 11.3
• cudnn 8.2.0
• gcc/g++: 10.2

Failed to run test code as:

  DeepFaceEditing-Jittor git:(main) python test_model.py --geo ./results/sketch_gen.png --appear ./images/appearance.png --output ./results/sketch_result.png --geo_type sketch
[i 0512 11:37:12.017235 64 compiler.py:851] Jittor(1.2.2.71) src: ~/.local/lib/python3.8/site-packages/jittor
[i 0512 11:37:12.017286 64 compiler.py:852] g++ at /usr/bin/g++
[i 0512 11:37:12.017319 64 compiler.py:853] cache_path: ~/.cache/jittor/default/g++
[i 0512 11:37:12.019891 64 __init__.py:257] Found /usr/local/cuda/bin/nvcc(11.3.58) at /usr/local/cuda/bin/nvcc.
[i 0512 11:37:12.070950 64 __init__.py:257] Found gdb(9.2) at /usr/bin/gdb.
[i 0512 11:37:12.074234 64 __init__.py:257] Found addr2line(2.34) at /usr/bin/addr2line.
[i 0512 11:37:12.108394 64 compiler.py:893] pybind_include: -I/usr/include/python3.8 -I~/.local/lib/python3.8/site-packages/pybind11/include
[i 0512 11:37:12.116724 64 compiler.py:895] extension_suffix: .cpython-38-x86_64-linux-gnu.so
[i 0512 11:37:12.207914 64 __init__.py:169] Total mem: 31.27GB, using 10 procs for compiling.
terminate called after throwing an instance of 'std::runtime_error'
  what():  [f 0512 11:37:12.404950 64 helper_cuda.h:126] CUDA error at ~/.local/lib/python3.8/site-packages/jittor/src/ops/array_op.cc:32  code=222( cudaErrorUnsupportedPtxVersion ) cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking)

Any suggestions?

I followed your instructions to run this code, there is no issues, but the result images what I obtained is blank. Any suggestion about it?

Here is the running process:

(deepface) lisicheng@lisicheng-Z590-S01:~/DeepFaceEditing-Jittor$ python test_model.py --geo ./images/geometry.png --output ./results/sketch_gen.png --gen_sketch
[i 1021 21:22:46.292933 44 compiler.py:941] Jittor(1.3.1.9) src: /home/lisicheng/anaconda3/envs/deepface/lib/python3.7/site-packages/jittor
[i 1021 21:22:46.294142 44 compiler.py:942] g++ at /usr/local/bin/g++(7.5.0)
[i 1021 21:22:46.294178 44 compiler.py:943] cache_path: /home/lisicheng/.cache/jittor/jt1.3.1/g++7.5.0/py3.7.11/Linux-5.11.0-3x6e/11thGenIntelRCxdc/default
[i 1021 21:22:46.296077 44 init.py:372] Found nvcc(10.0.130) at /usr/local/cuda-10.0/bin/nvcc.
[i 1021 21:22:46.361783 44 init.py:372] Found gdb(9.2) at /usr/bin/gdb.
[i 1021 21:22:46.365229 44 init.py:372] Found addr2line(2.34) at /usr/bin/addr2line.
[i 1021 21:22:46.442669 44 compiler.py:993] cuda key:cu10.0.130_sm_86
[i 1021 21:22:46.564720 44 init.py:187] Total mem: 31.22GB, using 10 procs for compiling.
[i 1021 21:22:46.629541 44 jit_compiler.cc:27] Load cc_path: /usr/local/bin/g++
[i 1021 21:22:46.730638 44 init.cc:61] Found cuda archs: [86,]
[w 1021 21:22:46.808636 44 compiler.py:1326] CUDA arch(86)>75 will be backward-compatible
[i 1021 21:22:46.812635 44 compile_extern.py:497] mpicc not found, distribution disabled.
[i 1021 21:22:46.854007 44 compile_extern.py:29] found /usr/local/cuda-10.0/include/cublas.h
[i 1021 21:22:46.856392 44 compile_extern.py:29] found /usr/local/cuda-10.0/lib64/libcublas.so
[i 1021 21:23:35.793154 44 compile_extern.py:29] found /usr/local/cuda-10.0/include/cudnn.h
[i 1021 21:23:35.798631 44 compile_extern.py:29] found /usr/local/cuda-10.0/lib64/libcudnn.so
[i 1021 21:32:56.286736 44 compile_extern.py:29] found /usr/local/cuda-10.0/include/curand.h
[i 1021 21:32:56.305076 44 compile_extern.py:29] found /usr/local/cuda-10.0/lib64/libcurand.so
[i 1021 21:32:59.990031 44 cuda_flags.cc:32] CUDA enabled.

Just notice that this released demo uses fixed bounding boxes for eyes, nose, mouth (https://github.com/IGLICT/DeepFaceEditing-Jittor/blob/master/combine_model.py#L15).

I wonder how do you define such bounding boxes in more general cases? Do you use the same fixed values as the ones shown in this released demo, or use some keypoint detection to generate such boxes?

Congratulations. But I have a little request, can you share your training details? I reproduced it according to your paper, but it didn't work. Thanks.

Hello, nice job! I have a question, why don't use the encoder(including downsample and resnet-blocks) part of GlobalGenerator with ngf=32 as Geometry Encoder directly, but train a GeometryEncoder with ngf=64. Thanks.