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StyleGAN2 - Official TensorFlow Implementation with practical improvements
Home Page: http://arxiv.org/abs/1912.04958
License: Other
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Link to pre-trained ResNet encoder-model?
In https://github.com/pbaylies/stylegan2/blob/6ad0842e3af3712e565ed39ff7a2d4dbda00faf0/StyleGAN_Encoder_Tutorial.ipynb there is a line saying:
Remember, there's a pre-trained model linked in the repo that works with the FFHQ faces StyleGAN model)
What link is it? I see @rolux providing one in rolux#2 (comment). Is there another one?
Thank you!
[Request] Add DiffAugment option
ref:https://github.com/mit-han-lab/data-efficient-gans
And could you also add this?
https://github.com/genforce/sefa
Still no official code release but I saw an implementation of it (from:https://github.com/rosinality/stylegan2-pytorch) but I can't get this stylegan2-pytorch working on my computer even with latest pytorch, so I would appreciate if this TF repo could implement(or convert) it instead.
Lazy path regularization for G fails
Hey absolutely love the functionalities of this repo!! Thx so much!
However, I'm getting issues when running this on my local RTX 2080 Ti machine..
All the training ops run fine, except for the G_reg_op
The weird thing is that I just tested with the code from the official StyleGAN2 repo, and there everything seems to work perfectly (including G_reg_op)...
To summarize, the core of the error seems to originate in the custom cuda functions for upsampling:
- I have tested that nvcc test_nvcc.cu -o test_nvcc -run runs correctly
- Running Cuda 10.0, TF 1.14.0 and Python 3.6
- When I switch off lazy regularization, all runs fine again
- Running a config-f model on 1024x1024 res with (8, 8, res_log2 = 7) setting
Here's the full traceback:
``dnnlib: Running training.training_loop.training_loop() on localhost...
Streaming data using training.dataset.TFRecordDataset...
####################################################
TFREC dir: datasets/fractal_tf_rec
['fractal_tf_rec-r07.tfrecords']
####################################################
Dataset shape = [3, 1024, 1024]
Dynamic range = [0, 255]
Label size = 0
Constructing networks...
Setting up TensorFlow plugin "fused_bias_act.cu": Preprocessing... Loading... Done.
Setting up TensorFlow plugin "upfirdn_2d.cu": Preprocessing... Loading... Done.
G Params OutputShape WeightShape
--- --- --- ---
latents_in - (?, 512) -
labels_in - (?, 0) -
lod - () -
dlatent_avg - (512,) -
G_mapping/latents_in - (?, 512) -
G_mapping/labels_in - (?, 0) -
G_mapping/Normalize - (?, 512) -
G_mapping/Dense0 262656 (?, 512) (512, 512)
G_mapping/Dense1 262656 (?, 512) (512, 512)
G_mapping/Dense2 262656 (?, 512) (512, 512)
G_mapping/Dense3 262656 (?, 512) (512, 512)
G_mapping/Dense4 262656 (?, 512) (512, 512)
G_mapping/Dense5 262656 (?, 512) (512, 512)
G_mapping/Dense6 262656 (?, 512) (512, 512)
G_mapping/Dense7 262656 (?, 512) (512, 512)
G_mapping/Broadcast - (?, 16, 512) -
G_mapping/dlatents_out - (?, 16, 512) -
Truncation/Lerp - (?, 16, 512) -
G_synthesis/dlatents_in - (?, 16, 512) -
G_synthesis/8x8/Const 32768 (?, 512, 8, 8) (1, 512, 8, 8)
G_synthesis/8x8/Conv 2622465 (?, 512, 8, 8) (3, 3, 512, 512)
G_synthesis/8x8/ToRGB 264195 (?, 3, 8, 8) (1, 1, 512, 3)
G_synthesis/16x16/Conv0_up 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Conv1 2622465 (?, 512, 16, 16) (3, 3, 512, 512)
G_synthesis/16x16/Upsample - (?, 3, 16, 16) -
G_synthesis/16x16/ToRGB 264195 (?, 3, 16, 16) (1, 1, 512, 3)
G_synthesis/32x32/Conv0_up 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Conv1 2622465 (?, 512, 32, 32) (3, 3, 512, 512)
G_synthesis/32x32/Upsample - (?, 3, 32, 32) -
G_synthesis/32x32/ToRGB 264195 (?, 3, 32, 32) (1, 1, 512, 3)
G_synthesis/64x64/Conv0_up 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Conv1 2622465 (?, 512, 64, 64) (3, 3, 512, 512)
G_synthesis/64x64/Upsample - (?, 3, 64, 64) -
G_synthesis/64x64/ToRGB 264195 (?, 3, 64, 64) (1, 1, 512, 3)
G_synthesis/128x128/Conv0_up 1442561 (?, 256, 128, 128) (3, 3, 512, 256)
G_synthesis/128x128/Conv1 721409 (?, 256, 128, 128) (3, 3, 256, 256)
G_synthesis/128x128/Upsample - (?, 3, 128, 128) -
G_synthesis/128x128/ToRGB 132099 (?, 3, 128, 128) (1, 1, 256, 3)
G_synthesis/256x256/Conv0_up 426369 (?, 128, 256, 256) (3, 3, 256, 128)
G_synthesis/256x256/Conv1 213249 (?, 128, 256, 256) (3, 3, 128, 128)
G_synthesis/256x256/Upsample - (?, 3, 256, 256) -
G_synthesis/256x256/ToRGB 66051 (?, 3, 256, 256) (1, 1, 128, 3)
G_synthesis/512x512/Conv0_up 139457 (?, 64, 512, 512) (3, 3, 128, 64)
G_synthesis/512x512/Conv1 69761 (?, 64, 512, 512) (3, 3, 64, 64)
G_synthesis/512x512/Upsample - (?, 3, 512, 512) -
G_synthesis/512x512/ToRGB 33027 (?, 3, 512, 512) (1, 1, 64, 3)
G_synthesis/1024x1024/Conv0_up 51297 (?, 32, 1024, 1024) (3, 3, 64, 32)
G_synthesis/1024x1024/Conv1 25665 (?, 32, 1024, 1024) (3, 3, 32, 32)
G_synthesis/1024x1024/Upsample - (?, 3, 1024, 1024) -
G_synthesis/1024x1024/ToRGB 16515 (?, 3, 1024, 1024) (1, 1, 32, 3)
G_synthesis/images_out - (?, 3, 1024, 1024) -
G_synthesis/noise0 - (1, 1, 8, 8) -
G_synthesis/noise1 - (1, 1, 16, 16) -
G_synthesis/noise2 - (1, 1, 16, 16) -
G_synthesis/noise3 - (1, 1, 32, 32) -
G_synthesis/noise4 - (1, 1, 32, 32) -
G_synthesis/noise5 - (1, 1, 64, 64) -
G_synthesis/noise6 - (1, 1, 64, 64) -
G_synthesis/noise7 - (1, 1, 128, 128) -
G_synthesis/noise8 - (1, 1, 128, 128) -
G_synthesis/noise9 - (1, 1, 256, 256) -
G_synthesis/noise10 - (1, 1, 256, 256) -
G_synthesis/noise11 - (1, 1, 512, 512) -
G_synthesis/noise12 - (1, 1, 512, 512) -
G_synthesis/noise13 - (1, 1, 1024, 1024) -
G_synthesis/noise14 - (1, 1, 1024, 1024) -
images_out - (?, 3, 1024, 1024) -
--- --- --- ---
Total 24885511
D Params OutputShape WeightShape
--- --- --- ---
images_in - (?, 3, 1024, 1024) -
labels_in - (?, 0) -
1024x1024/FromRGB 128 (?, 32, 1024, 1024) (1, 1, 3, 32)
1024x1024/Conv0 9248 (?, 32, 1024, 1024) (3, 3, 32, 32)
1024x1024/Conv1_down 18496 (?, 64, 512, 512) (3, 3, 32, 64)
1024x1024/Skip 2048 (?, 64, 512, 512) (1, 1, 32, 64)
512x512/Conv0 36928 (?, 64, 512, 512) (3, 3, 64, 64)
512x512/Conv1_down 73856 (?, 128, 256, 256) (3, 3, 64, 128)
512x512/Skip 8192 (?, 128, 256, 256) (1, 1, 64, 128)
256x256/Conv0 147584 (?, 128, 256, 256) (3, 3, 128, 128)
256x256/Conv1_down 295168 (?, 256, 128, 128) (3, 3, 128, 256)
256x256/Skip 32768 (?, 256, 128, 128) (1, 1, 128, 256)
128x128/Conv0 590080 (?, 256, 128, 128) (3, 3, 256, 256)
128x128/Conv1_down 1180160 (?, 512, 64, 64) (3, 3, 256, 512)
128x128/Skip 131072 (?, 512, 64, 64) (1, 1, 256, 512)
64x64/Conv0 2359808 (?, 512, 64, 64) (3, 3, 512, 512)
64x64/Conv1_down 2359808 (?, 512, 32, 32) (3, 3, 512, 512)
64x64/Skip 262144 (?, 512, 32, 32) (1, 1, 512, 512)
32x32/Conv0 2359808 (?, 512, 32, 32) (3, 3, 512, 512)
32x32/Conv1_down 2359808 (?, 512, 16, 16) (3, 3, 512, 512)
32x32/Skip 262144 (?, 512, 16, 16) (1, 1, 512, 512)
16x16/Conv0 2359808 (?, 512, 16, 16) (3, 3, 512, 512)
16x16/Conv1_down 2359808 (?, 512, 8, 8) (3, 3, 512, 512)
16x16/Skip 262144 (?, 512, 8, 8) (1, 1, 512, 512)
8x8/MinibatchStddev - (?, 513, 8, 8) -
8x8/Conv 2364416 (?, 512, 8, 8) (3, 3, 513, 512)
8x8/Dense0 16777728 (?, 512) (32768, 512)
Output 513 (?, 1) (512, 1)
scores_out - (?, 1) -
--- --- --- ---
Total 36613665
Building TensorFlow graph...
Initializing logs...
Training for 20000 kimg...
Running with: minibatch_size_in: 64.00 -- minibatch_gpu_in: 1.00
Running with gradient accumulation
Trying G_reg_op...
Traceback (most recent call last):
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1356, in _do_call
return fn(*args)
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1341, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1429, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InternalError: cudaErrorInvalidConfiguration
[[{{node GPU0/G_loss/PathReg/G/G_synthesis/16x16/Upsample/UpFirDn2D}}]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "run_training.py", line 216, in <module>
main()
File "run_training.py", line 211, in main
run(**vars(args))
File "run_training.py", line 136, in run
dnnlib.submit_run(**kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/submit.py", line 343, in submit_run
return farm.submit(submit_config, host_run_dir)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/internal/local.py", line 22, in submit
return run_wrapper(submit_config)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/submit.py", line 280, in run_wrapper
run_func_obj(**submit_config.run_func_kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/training_loop.py", line 347, in training_loop
tflib.run(G_reg_op, feed_dict)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/tflib/tfutil.py", line 31, in run
return tf.get_default_session().run(*args, **kwargs)
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 950, in run
run_metadata_ptr)
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1173, in _run
feed_dict_tensor, options, run_metadata)
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1350, in _do_run
run_metadata)
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1370, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: cudaErrorInvalidConfiguration
[[node GPU0/G_loss/PathReg/G/G_synthesis/16x16/Upsample/UpFirDn2D (defined at <string>:110) ]]
Errors may have originated from an input operation.
Input Source operations connected to node GPU0/G_loss/PathReg/G/G_synthesis/16x16/Upsample/UpFirDn2D:
GPU0/G_loss/PathReg/G/G_synthesis/16x16/Upsample/Reshape (defined at /home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/tflib/ops/upfirdn_2d.py:358)
GPU0/G_loss/PathReg/G/G_synthesis/16x16/Upsample/Const (defined at /home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/tflib/ops/upfirdn_2d.py:123)
Original stack trace for 'GPU0/G_loss/PathReg/G/G_synthesis/16x16/Upsample/UpFirDn2D':
File "run_training.py", line 216, in <module>
main()
File "run_training.py", line 211, in main
run(**vars(args))
File "run_training.py", line 136, in run
dnnlib.submit_run(**kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/submit.py", line 343, in submit_run
return farm.submit(submit_config, host_run_dir)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/internal/local.py", line 22, in submit
return run_wrapper(submit_config)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/submit.py", line 280, in run_wrapper
run_func_obj(**submit_config.run_func_kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/training_loop.py", line 256, in training_loop
G_loss, G_reg = dnnlib.util.call_func_by_name(G=G_gpu, D=D_gpu, opt=G_opt, training_set=training_set, minibatch_size=minibatch_gpu_in, **G_loss_args)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/util.py", line 256, in call_func_by_name
return func_obj(*args, **kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/loss.py", line 164, in G_logistic_ns_pathreg
fake_images_out, fake_dlatents_out = G.get_output_for(pl_latents, pl_labels, is_training=True, return_dlatents=True)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/tflib/network.py", line 221, in get_output_for
out_expr = self._build_func(*final_inputs, **build_kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/networks_stylegan2.py", line 238, in G_main
images_out = components.synthesis.get_output_for(dlatents, is_training=is_training, force_clean_graph=is_template_graph, **kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/tflib/network.py", line 221, in get_output_for
out_expr = self._build_func(*final_inputs, **build_kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/networks_stylegan2.py", line 544, in G_synthesis_stylegan2
y = upsample(y)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/networks_stylegan2.py", line 499, in upsample
return upsample_2d(y, k=resample_kernel)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/tflib/ops/upfirdn_2d.py", line 198, in upsample_2d
return _simple_upfirdn_2d(x, k, up=factor, pad0=(p+1)//2+factor-1, pad1=p//2, data_format=data_format, impl=impl)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/tflib/ops/upfirdn_2d.py", line 359, in _simple_upfirdn_2d
y = upfirdn_2d(y, k, upx=up, upy=up, downx=down, downy=down, padx0=pad0, padx1=pad1, pady0=pad0, pady1=pad1, impl=impl)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/tflib/ops/upfirdn_2d.py", line 62, in upfirdn_2d
return impl_dict[impl](x=x, k=k, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/tflib/ops/upfirdn_2d.py", line 140, in _upfirdn_2d_cuda
return func(x)
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/ops/custom_gradient.py", line 162, in decorated
return _graph_mode_decorator(f, *args, **kwargs)
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/ops/custom_gradient.py", line 183, in _graph_mode_decorator
result, grad_fn = f(*args)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/tflib/ops/upfirdn_2d.py", line 132, in func
y = _get_plugin().up_fir_dn2d(x=x, k=kc, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1)
File "<string>", line 110, in up_fir_dn2d
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3616, in create_op
op_def=op_def)
File "/home/rednax/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2005, in __init__
self._traceback = tf_stack.extract_stack()
``
Bug: the discriminator is using the generator's learning rate
Credit to this bug goes to @shawwn who reported it on Twitter. I know you're already aware of the issue, but thought it important to document here for anyone searching for it.
Basically, the learning rate for the discriminator can be written to, but it is never read. Instead, the lrate_in used by both networks reads from G_lrate, here.
Also, once this is fixed, a quality of life feature I'd like to suggest would be to change the --lr argument for run_training.py to two new ones that set the G and D learning rates separately. Something like --lrG=0.003 and --lrD=0.0015 would be handy.
Artist/genre/style categories not working
Great model, really enjoying playing with art generation with the following notebook:
WikiArt Example Generation.ipynb
(models: network-snapshot-006746.pkl, network-snapshot-011041.pkl, network-snapshot-012052.pkl)
But the categories of artist/genre/style don't seem to make sense. All generated images are great but appear to be totally random.
I was wondering if the categories are being mapped incorrectly. Has anyone had the same problem, or got the categories working somehow?
Unable to use create_from_images_raw in training
Whenever I create a dataset with create_from_images_raw, and then run a training job, I run into the following error:
Traceback (most recent call last):
File "run_training.py", line 213, in <module>
main()
File "run_training.py", line 208, in main
run(**vars(args))
File "run_training.py", line 133, in run
dnnlib.submit_run(**kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/submit.py", line 343, in submit_run
return farm.submit(submit_config, host_run_dir)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/internal/local.py", line 22, in submit
return run_wrapper(submit_config)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/submit.py", line 280, in run_wrapper
run_func_obj(**submit_config.run_func_kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/training_loop.py", line 158, in training_loop
training_set = dataset.load_dataset(data_dir=dnnlib.convert_path(data_dir), verbose=True, **dataset_args)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/dataset.py", line 246, in load_dataset
dataset = dnnlib.util.get_obj_by_name(class_name)(**adjusted_kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/dataset.py", line 119, in __init__
tfr_shapes.append(parse_tfrecord_np(record).shape)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/dataset.py", line 45, in parse_tfrecord_np
data = ex.features.feature["data"].bytes_list.value[0] # temporary pylint workaround # pylint: disable=no-member
IndexError: list index (0) out of range
The first issue was that training/dataset.py refers to (line 44)
data = ex.features.feature["data"].bytes_list.value[0]
instead of
data = ex.features.feature["img"].bytes_list.value[0]
But even with that adjustment the contents of the tf_records file seem to have wrong dimensions:
Traceback (most recent call last):
File "run_training.py", line 213, in <module>
main()
File "run_training.py", line 208, in main
run(**vars(args))
File "run_training.py", line 133, in run
dnnlib.submit_run(**kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/submit.py", line 343, in submit_run
return farm.submit(submit_config, host_run_dir)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/internal/local.py", line 22, in submit
return run_wrapper(submit_config)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/dnnlib/submission/submit.py", line 280, in run_wrapper
run_func_obj(**submit_config.run_func_kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/training_loop.py", line 158, in training_loop
training_set = dataset.load_dataset(data_dir=dnnlib.convert_path(data_dir), verbose=True, **dataset_args)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/dataset.py", line 246, in load_dataset
dataset = dnnlib.util.get_obj_by_name(class_name)(**adjusted_kwargs)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/dataset.py", line 119, in __init__
tfr_shapes.append(parse_tfrecord_np(record).shape)
File "/home/rednax/Desktop/music_vr/StyleGAN_training/stylegan2/training/dataset.py", line 48, in parse_tfrecord_np
return np.fromstring(data, np.uint8).reshape(shape)
ValueError: cannot reshape array of size 228962 into shape (3,1024,1024)
Reordering of img.shape in create_from_images causes this if statement to catch every time
Line 718 in 1bb72ef
Not sure what this is supposed to catch but reordering of img shape from HWC -> CHW causes mismatch between original shape of image and transposed version every time. I assume grayscale should catch with new dimension as well. Commented this out and dataset built fine.
Non-Square images meaning?
Supports Non-Square images, for example, 768x512, which basically as 6x4 (x2^7), or 640x384 as 5x3 (x2^7), etc.
Can you explain what you mean by this?
how is this calculated?
Readmd
Documentation needs updating for completely switch to TPU INSTEAD OF GPU
AttributeError: module 'dnnlib' has no attribute 'SubmitConfig'
!python run_training.py --num-gpus=1 --data-dir=./dataset --config=config-f --dataset=lex --mirror-augment=true --metric=none --total-kimg=10000
Traceback (most recent call last):
File "run_training.py", line 230, in
main()
File "run_training.py", line 225, in main
run(**vars(args))
File "run_training.py", line 46, in run
sc = dnnlib.SubmitConfig() # Options for dnnlib.submit_run().
AttributeError: module 'dnnlib' has no attribute 'SubmitConfig'
Error in Dataset Creation with B&W Images
I ran into an error while creating a dataset with black and white image. The 'Out of Index' error happens at the following line:
Line 94 in 358a046
A quick fix for B&W images is to add img = np.expand_dims(img, axis=-1) at line 697 of dataset_tool.py as such:
img = np.asarray(PIL.Image.open(image_filenames[0]))
#resolution = img.shape[0]
channels = img.shape[2] if img.ndim == 3 else 1
no learnable_dlatents in stylegan2
Hi,
checking out the generator there is no learnable_dlatents in the stylegan2 model
how can we fix this?
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