Comments (7)
That may be a little difference with the statement in paper. You can also try to implement yourself and make a PR.
from beautygan_pytorch.
That may be a little difference with the statement in paper. You can also try to implement yourself and make a PR.
Hi, I have implemented makeuploss myself before, almost the same as your implement work. But it seems that the color of HM result is sometimes far away from the reference image.
from beautygan_pytorch.
That may be a little difference with the statement in paper. You can also try to implement yourself and make a PR.
Hi, I have implemented makeuploss myself before, almost the same as your implement work. But it seems that the color of HM result is sometimes far away from the reference image.
Yes, your observation is normal. Because HM is only a coarse gudiance, which need adv training to refine. If the results are perfect, there is no need to propose BeautyGAN.
from beautygan_pytorch.
That may be a little difference with the statement in paper. You can also try to implement yourself and make a PR.
Hi, I have implemented makeuploss myself before, almost the same as your implement work. But it seems that the color of HM result is sometimes far away from the reference image.
Yes, your observation is normal. Because HM is only a coarse gudiance, which need adv training to refine. If the results are perfect, there is no need to propose BeautyGAN.
Okay. Could you please tell me how to balance the weights of all losses? I have tried many times before...
from beautygan_pytorch.
That may be a little difference with the statement in paper. You can also try to implement yourself and make a PR.
Hi, I have implemented makeuploss myself before, almost the same as your implement work. But it seems that the color of HM result is sometimes far away from the reference image.
Yes, your observation is normal. Because HM is only a coarse gudiance, which need adv training to refine. If the results are perfect, there is no need to propose BeautyGAN.
Okay. Could you please tell me how to balance the weights of all losses? I have tried many times before...
lambda_his = 1
lambda_his_lip = 1
lambda_skin = 0.1
lambda_eye = 1
The default settings at 'train.py'
from beautygan_pytorch.
That may be a little difference with the statement in paper. You can also try to implement yourself and make a PR.
Hi, I have implemented makeuploss myself before, almost the same as your implement work. But it seems that the color of HM result is sometimes far away from the reference image.
Yes, your observation is normal. Because HM is only a coarse gudiance, which need adv training to refine. If the results are perfect, there is no need to propose BeautyGAN.
Okay. Could you please tell me how to balance the weights of all losses? I have tried many times before...
lambda_his = 1
lambda_his_lip = 1
lambda_skin = 0.1
lambda_eye = 1
The default settings at 'train.py'
Thank you. It is the same as the description in paper. And I wonder how you get that weights, and the weights of GANLoss.
from beautygan_pytorch.
Does it work? How do I import the data set? I have a problem importing it
from beautygan_pytorch.
Related Issues (15)
- Confused in dataloader HOT 17
- Why choose 6 residual blocks in the generator but not 9 as in CycleGAN? HOT 2
- Excuse , I have a question, what does the para --cls_list means ? HOT 1
- MT-dataset HOT 2
- Could you please share MTdataset? HOT 1
- The process of face parsing HOT 1
- Makeup Transfer Dataset link was broken HOT 1
- Is there no code to extract the mask or I missed it? HOT 4
- [Errno 2] No such file or directory: 'addings/vgg_conv.pth'
- About update the code.
- pretrained vgg weight HOT 1
- How to run the code
- A Guide To Run The Code HOT 44
- Pretrained model
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