Peterou

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> ![Snipaste_2021-12-31_17-51-42](https://user-images.githubusercontent.com/96617198/147816513-97d98a00-e766-4759-8abc-7735ce7247c1.png) What module is this? I lack this module after installing the environment,Thank you for your reply! Sorry, it's my fault. You could fix the error by removing the...

> Thank you for reading my message. I am very impressive by your work of CIPS. If you have time, please reply to me. Thank you very much.I am very...

![capture](https://user-images.githubusercontent.com/26176709/155250674-2a40945e-7871-4cc5-af17-f2856e854430.JPG) There are three interpolated models above. Which model do you want?

No problem. I plan to write a demo in next week.

OK, I have updated the repo. Preparing AFHQ dataset: ```bash # Prepare training dataset. python scripts/dataset_tool.py \ --source=datasets/AFHQv2/AFHQv2 \ --dest=datasets/AFHQv2/AFHQv2_stylegan2.zip ``` The pre-trained FFHQ checkpoints: [https://github.com/PeterouZh/CIPS-3D#pre-trained-checkpoints-including-generator-and-discriminator](https://github.com/PeterouZh/CIPS-3D#pre-trained-checkpoints-including-generator-and-discriminator). The start script: `exp/cips3d/bash/finetuning_exp/finetune_afhq.sh`....

Hi. Thanks for your interest in our work! "Class supervision" refers to the class label of an image. In fact, both projection-based loss and omni-loss employ class labels. But projection-based...

![image](https://user-images.githubusercontent.com/26176709/104691235-cd5ed600-5740-11eb-8926-03914d3e121b.png) Suppose there is a real image (x_real, y_real) and a generated image (x_fake, y_fake). y is the corresponding class label of x. Note that the original projection-based discriminator is...

> Sorry for the late reply. It is more common to calculate the FID between all training data and 50k generated data. Please refer to this site https://github.com/NVlabs/stylegan2-ada-pytorch#quality-metrics

I fix this issue by replacing "f3 = self.frame.iloc[:, 1:] > 0" [url](https://github.com/yingcong/HomoInterpGAN/blob/4b61ad1c2810f4bf129942bbd7d5a351d15526d1/data/attributeDataset.py#L220) with "f3 = self.frame.iloc[:, 3:-1] > 0".

Change the target_network_pkl to "AFHQ_r256_v1".