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Disentangled Makeup Transfer with Generative Adversarial Network
hello, thanks for this wonderful work, when i implement this project, i find the generated result is not good, so can you share some training tips, thanks!
Hello,
I tried the code on colab a few weeks ago (only changing a few lines for compatibility), and it worked perfectly fine.
Now, it only outputs black images. The images have the correct dimensions, but they contain only black pixels.
Compatibility change in 'main.py':
# ~ import tensorflow as tf # here
import tensorflow.compat.v1 as tf; tf.disable_v2_behavior() # here
import numpy as np
from imageio import imread, imsave
import os, glob, cv2
imsave = cv2.imwrite # and here
As I said, it worked after I made the changes, so these are not responsible for the strange behavior.
Would you have any idea?
Best regards.
Hi,Honlan,i am appreciate it for your work,and i am trying to De-make up,How should i do it ?
Hi,
Thanks for the insightful research. I would like to reproduce your research but I cannot find the code?
Please advise
Hi, really good job.
But, why reference and source image sizes have to be same?
Hello,
Thank you very much for your research, it is very interesting. I would like to reproduce your research and reading the paper I though the code would be here, but I cannot seem to find it. Could you please point me in the right direction to find the code?
Thank you very much.
The effect is so bad, and I am embarrassed to fake open source, please don't lose your face abroad!
SB!
This repo is lack of information. For example, it doesn't have any installation guide for users to run. There's no describing what packages to be install
An excellent job!I’m really interested in your experiment,Could you provide your project codes for public available?Thanks a lot!
Interesting work. Is DMT good for realtime processing in a phone? like many of those popular iphone apps doing similar things.
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