Comments (9)
Thanks a lot for your interests in this work. We may not be able to provide the processed data trying to avoid any potential LICENSE issue.
exceed off quota limit
is a common issue as GDrive does not support the command line downloading well (I encountered the same thing). You can use the browser to download the data. I know it is a little bit cumbersome but this may be the only way (I also use this tedious way to download the data).
Hope this helps.
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@VIROBO-15 We will check with legal and see if we can release the pre-processed datasets. Please stay tuned.
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Hi, Can you Please provide the FFHQ data as, I am unable to download the ffhq dataset from the given google drive (https://drive.google.com/drive/folders/1LTBpJ0W_WLjqza3zdayligS8Dh1V1gA6) due to exceed off quota limit.
In the past for issues like this, I've cloned the drive to my own google drive and then you avoid the data caps.
(Also I just downloaded it outside of my google account with no issues)
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@3dsf
Thank You for helping but I have also made a copy of the drive in my own google drive but It still shows the same issue.
Can you Please help me out in this...
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Thanks a lot for your interests in this work. We may not be able to provide the processed data trying to avoid any potential LICENSE issue.
exceed off quota limit
is a common issue as GDrive does not support the command line downloading well (I encountered the same thing). You can use the browser to download the data. I know it is a little bit cumbersome but this may be the only way (I also use this tedious way to download the data).Hope this helps.
Thanks for your amazing work, I also encounter the same problem when I download the tf-records, but I have the ffhq-image 1024*1024 files (https://drive.google.com/drive/folders/1tZUcXDBeOibC6jcMCtgRRz67pzrAHeHL). So did the tf-recoards data contains the same 1024 resolution faces as ffhq-image 1024*1024 ?
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@cnnlstm My gut is that should be fine but I have not tried that. Just two things:
-
The officially-provided 1024*1024 images (the link you shared) use folder name as
69000
. However, if you follow StyleGAN2-ADA's processing script to extract data from the TFRecords, the folder name is00069
. Our processed pose file use the same folder name as00069
. You need to slightly modify thedataset
class:a) We directly read image from zipfile, you need to change it to read from disk:
Line 49 in 566e9c5
b) Modify the folder name to point to the right path when you read data:
Lines 62 to 63 in 566e9c5
-
The zipfile of 1024x1024 occupies ~200GB. However, the officially provided 1024x1024 images (the link you shared) only occupies ~90GB. I guess it is because of no compression in the zipfile but I am not 100% sure: see this line.
Overall, I think you can definitely train GMPI with the data you already have.
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@VIROBO-15 We will check with legal and see if we can release the pre-processed datasets. Please stay tuned.
Unfortunately we were informed by Apple's legal team that we could not release these pre-processed datasets because of privacy concerns.
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@cnnlstm My gut is that should be fine but I have not tried that. Just two things:
The officially-provided 1024*1024 images (the link you shared) use folder name as
69000
. However, if you follow StyleGAN2-ADA's processing script to extract data from the TFRecords, the folder name is00069
. Our processed pose file use the same folder name as00069
. You need to slightly modify thedataset
class:
a) We directly read image from zipfile, you need to change it to read from disk:
Line 49 in 566e9c5
b) Modify the folder name to point to the right path when you read data:
Lines 62 to 63 in 566e9c5
The zipfile of 1024x1024 occupies ~200GB. However, the officially provided 1024x1024 images (the link you shared) only occupies ~90GB. I guess it is because of no compression in the zipfile but I am not 100% sure: see this line.
Overall, I think you can definitely train GMPI with the data you already have.
Thanks for your detailed reply.
I convert the ffhq-image 1024*1024 to tf_recoard files, and obtain different resolution files, just like files in the Nvidia released tf_recoard (https://drive.google.com/drive/folders/1M24jfI-Ylb-k2EGhELSnxssWi9wGUokg).
Then I download small tf_recoard file (
ffhq-r07.tfrecords
) from the link, and uncompress them to images, meanwhile uncompress my own ffhq-r07.tfrecords files to images.
I check the corresponding images and found that they are exactly the same. So I believe the
ffhq-r10.recoards
data contains the same 1024 resolution faces as ffhq-image 1024*1024.
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@cnnlstm Thanks a lot for your time in checking this. I think this will surely be useful for others. Hope you enjoy this repo.
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Related Issues (19)
- environment.yml error HOT 13
- Problem In training the FFHQ1024 model HOT 3
- Great Work!!!!!! Few Queries HOT 7
- "RuntimeError: output is too large" when extracting meshes with 1024x1024 models HOT 2
- Problem in Importing upfirdn2d_plugin.so HOT 2
- RuntimeError: Unable to find a valid cuDNN algorithm to run convolution HOT 10
- RuntimeError: "slow_conv_transpose2d_out_cpu" not implemented for 'Half' HOT 1
- I got error when I start to train HOT 9
- download FFHQ tfrecord dataset related issue HOT 1
- Problem in reproducing fid score HOT 9
- Maximum range of yaw and pitches HOT 1
- 感觉真是充满坑点 HOT 1
- Great Work!!!! Few queries regarding the evaluation code HOT 3
- Query Regarding the evaluation code for FFHOQ512 HOT 6
- Problem in calculating the Identity metric
- Query in calculating the depth and pose metric HOT 1
- Code for estimating pose for preprocessing (used in "Deep3DFaceRecon" directory) HOT 1
- Problem in Reproducing the Depth score HOT 5
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