Comments (2)
moono
commented on June 25, 2024
1
I used followings to train on FFHQ dataset,
- Refer to FFHQ dataset repo to create
*.tfrecordfor resolution of your interest. - Please use
dockerfor training envrionment.- Recommended docker image :
nvcr.io/nvidia/tensorflow:21.03-tf2-py3
- Recommended docker image :
- Train with
train.py- example 256x256 resolution
# adjust shuffle_buffer_size and batch_size_per_replica to fit your hardware
~$ python train.py \
--allow_memory_growth false \
--use_tf_function true \
--use_custom_cuda true \
--model_base_dir <directory-of-output-trained-model> \
--tfrecord_dir <FFHQ-tfrecord-dir> \
--train_res 256 \
--shuffle_buffer_size 5000 \
--batch_size_per_replica 16from stylegan2-tf-2.x.
Akila-Ayanthi
commented on June 25, 2024
Thank you.
from stylegan2-tf-2.x.
Related Issues (15)
- convert official weights HOT 3
- Cannot convert pkl file to ckpt when the size is 256 or 512. HOT 5
- checkpoint of FFHQ 256*256?
- Error
- when training in 256x256, the output is 256x512 pixels HOT 1
- Memory leak
- 10 errors detected in the compilation of upfirdn_2d.cu
- different resolution pkl file HOT 1
- Please update readme HOT 3
- Training on custom dataset failed HOT 9
- Training crash randomly HOT 2
- What are the "labels" in the generator and discriminator? HOT 2
- Need help understanding the role of the 'upfirdn' after the transpose_conv HOT 1
- NHWC Support HOT 2
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from stylegan2-tf-2.x.