I have completed the first few steps of Quick Start,Download the datasetd and Preprocess the dataset. Train.py step has not been done because of the GPU problem then I start to try test.py. as your suggestion,$your_input_path can be either a image file or a folder of images. then I try python core/test.py --config configs/celeba-hq.yaml --checkpoint configs/checkpoint_256_celeba-hq.pt--input_path CelebAMask-HQ/CelebAMask-HQ/CelebA-HQ-img --output_path result test.py: error: unrecognized arguments: CelebAMask-HQ/CelebAMask-HQ/CelebA-HQ-img or python core/test.py --config configs/celeba-hq.yaml --checkpoint configs/checkpoint_256_celeba-hq.pt--input_path CelebAMask-HQ/CelebAMask-HQ/CelebA-HQ-img/0.jpg --output_path result test.py: error: unrecognized arguments: CelebAMask-HQ/CelebAMask-HQ/CelebA-HQ-img/0.jpg I don't know if it's because I haven't yet modified the 'steps' dict in the first few lines in 'core/test.py' .If it is for this reason, can you tell me how to modify the 'steps' dict?As a junior who has just studied deep-learning for one or two months, it's really a bit difficult for me,thanks a lot.

Sorry to bother you, I'm doing some work on your model and would like to ask a few questions about training skills

1. As for the number of training times, I combine the loss function with the process graph generated in the training process. When the loss function declines to be stable and the generated graph looks ok, it means that the training is done. Do you have any additional comments?

2. Regarding the problem of model collapse, can you give some examples in this task?

3. Do you have any skills in setting loss function coefficients?

Looking forward to your reply~

` class Dis(nn.Module): def init(self, hyperparameters): super().init() self.tags = hyperparameters['tags'] channels = hyperparameters['discriminators']['channels'] #[64, 128, 256, 512, 1024, 2048] self.conv = nn.Sequential( nn.Conv2d(hyperparameters['input_dim'], channels[0], 1, 1, 0), *[DownBlock(channels[i], channels[i + 1]) for i in range(len(channels) - 1)], nn.AdaptiveAvgPool2d(1), ) self.fcs = nn.ModuleList([nn.Sequential( nn.Conv2d(channels[-1] + #2048 # ALI part which is not shown in the original submission but help disentangle the extracted style. #ALI部分未在原始提交中显示，但有助于解耦提取到的 style。 hyperparameters['style_dim'] + #256 # Tag-irrelevant part. Sec.3.4 self.tags[i]['tag_irrelevant_conditions_dim'], #2 2 2 # One for translated, one for cycle. Eq.4 len(self.tags[i]['attributes'] * 2), 1, 1, 0), #4 4 6 ) for i in range(len(self.tags))]) #这里的i控制的是三个tag里面的哪个

def forward(self, x, s, y, i):
    f = self.conv(x)
    fsy = torch.cat([f, tile_like(s, f), tile_like(y, f)], 1)
    #按照第一维度，也就是列维度，叠加起来，也就是横着串起来
    return self.fcs[i](fsy).view(f.size(0), 2, -1) `

作者你好，关于判别器我有几个不太懂的点，还希望您可以教教我

1. 对于判别器是怎么不去改变两个无关标签我不是很理解这其中的过程

2. 判别器的forward那边最后的.view(f.size(0), 2, -1)，第一维是batch_size，第二维我不懂是什么，为啥是2，第三维是控制的属性吗，这边看不太懂

3. 关于计算生成器的对抗损失这边，为什么真实图片取的[:,0]和[:,1]的平均之和，而两张fake图片分别取的[:,0]和[:,1]的平均？这边不太理解。代码如下： ` def calc_gen_loss_real(self, x, s, y, i, j):# loss = 0 out = self.forward(x, s, y, i)[:, :, j]#选到那个属性 #比如是[8, 2, 2], 截取[:,:,1] 就变成了[8, 2]了 loss += out[:, 0].mean() loss += out[:, 1].mean() return loss

   def calc_gen_loss_fake_trg(self, x, s, y, i, j): out = self.forward(x, s, y, i)[:, :, j] loss = - out[:, 0].mean() return loss

   def calc_gen_loss_fake_cyc(self, x, s, y, i, j): out = self.forward(x, s, y, i)[:, :, j] loss = - out[:, 1].mean() return loss ` 希望您可以解答我的疑惑，谢谢作者！

作者您好： 十分感谢您之前对我提出的问题的耐心回复，我现在正在您的框架的基础上进展一些工作，现在我需要做一些对比实验，我看到您的论文中使用了SDIT进行了reference-guided实验，我也看了这篇论文，发现是用的一个随机向量z，放入鉴别器中进行映射，然后用鉴别器去约束这个style。 我现在也想做reference-guided实验，是否是，将原图x 放入鉴别器，获得style，再将生成器所需的内容放入生成器生成图片呢，还望指点一下，感谢！

Hi, your work is so impressive that I want to reproduce your experiment. But I got a problem about the config of the batchsize, I use a single GTX 1080Ti GPU as you did，but the batchsize of 8 seems too big for it, I can just set batchsize to 5 or 4. Can you tell me the solution about this problem,thanks a lot

tags:

name: Bangs
tag_irrelevant_conditions_dim: 2
attributes: 
  -
    name: 'with'
    filename: datasets/Bangs_with.txt
  -
    name: 'without'
    filename: datasets/Bangs_without.txt

• name: Eyeglasses tag_irrelevant_conditions_dim: 2 attributes:

  name: 'with'
  filename: datasets/Eyeglasses_with.txt
  

  • name: 'without' filename: datasets/Eyeglasses_without.txt

if I want to make experiment on Multi-tag task, how to replace Multi-tag task on celeba-hq.yaml setting?

Hi, thanks for sharing the codes for the awesome work. I am wondering whether there is a typo in trainer.py L66. loss_gen_adv = self.dis.calc_gen_loss_real(x, s, y, i, j) + \ self.dis.calc_gen_loss_fake_trg(x_trg, s_trg.detach(), y, i, j_trg) + \ self.dis.calc_gen_loss_fake_cyc(x_cyc, s.detach(), y, i, j) When updating the generator, why use the real image for calculating the loss?

Thanks for your great works. I have some questions about the training configuration. There are differences between celeba-hq.yaml and celeba-hq_256.yaml in configs folder, such as the normalization in different channels. What's the reason here? If I try to train the model based on the size of 512 or 1024, how should I set? It is appreciated if I can receive your reple

Thank you for your open source. This code is very helpful to my knowledge of face attribute transfer. You have opened the checkpoint of 256 size, could you open the checkpoint of 1024 size for learning and use. Thank you very much.

In your paper, you have designed non-translation , self-translation, and cycle-translation. In non-translation generator, it just encode the input, e = encoder(input), and then decode e, output = decoder(e). Certainly you have already achieve great results with HiSD designed like this. I am a beginner of GAN, and I am just wondering why don't you set stylecode as 0, and use a translator in your non-translation, which means, e = encoder(input), e_trg = translator(e, 0), output = decoder(e_trg), I have seen other works did like this. Could it make any difference?

Hi， thanks for your beautiful work, I want to konw the reason for the design about the m、f of the translator , Is there any reference work , or you design this by experiment? And as your paper mentioned "The attention mask in our translator is both spatial- wise and channel-wise." can you explain specifically ？

作者您好，作为一名初学者，您的工作让我受益良多。有几个问题想请教一下：

1. 在复现论文的过程中，我发现在对Young个glasses这两个属性分别进行转换，训练到11万次左右的时候出现了如下情况（在对其他组合训练的时候则不会产生）：请问这是由于什么原因导致的？

2. 基于问题1，不同的标签组合是否会对训练结果产生影响？
3. 是否训练的次数越多效果就一定越好呢？还是说最好的结果出现在哪里要通过生成的结果直观查看
4. 在做reference-guided时是否可以通过标签对参考图像进行约束？

期待您的回复，感谢感谢！祝您科研顺利

Dear Author, I appreciate your work, but i have some questions about the data imbalance. The face number with glasses is only 1377 as the numbe without glasses is 25622. I want to known the effects of the data imbalance. Thanks~

Thank you for your research ! But now, I try to generate a new "checkpoint_512_celeba-hq.pt" for 512x512 size image. However, it still something wrong through test.py phase.

The error message shows that : RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED

How can I do now ? Do you have the checkpoint_512_celeba-hq.pt file ?