This project forked from nvlabs/stylegan3
Modifications of the official PyTorch implementation of StyleGAN3. Let's easily generate images and videos with StyleGAN2/2-ADA/3!
License: Other
I was reading this paper and the augmentation strategy looks quite interesting. The new method preserves the intraclass diversity with class-consistency in image generation and it outperforms other methods by ∼19% FID. Would you considering implementing it into this repo?
https://rangwani-harsh.github.io/NoisyTwins/
https://github.com/val-iisc/NoisyTwins
https://arxiv.org/abs/2304.05866
Have you considered augments for small sample generation tasks
Describe the bug
When using my run command: python train.py --outdir C:\Users\User\Documents\machinelearning\6\styleganfunresults --cfg=stylegan2 --data C:\Users\User\Documents\machinelearning\6\styleganfunganimages --gamma=1 --snap=3 --metrics=none --mbstd-group=20 --gpus=1 --batch=20
I get this error:
Usage: train.py [OPTIONS]
Try 'train.py --help' for help.
Error: Invalid value for '--data': File 'C:\Users\User\Documents\machinelearning\6\styleganfunganimages' is a directory.
(styleganfun) C:\Users\User\Documents\machinelearning\stylegan3-fun>
To Reproduce
I have 2k png images with transparent backgrounds and used the dataset_tool.py first with the below command.
python dataset_tool.py --source C:\Users\User\Documents\machinelearning\5\512croppedCopy --dest C:\Users\User\Documents\machinelearning\6\styleganfunganimages
then i tried to train on that data with
python train.py --outdir C:\Users\User\Documents\machinelearning\6\styleganfunresults --cfg=stylegan2 --data C:\Users\User\Documents\machinelearning\6\styleganfunganimages --gamma=1 --snap=3 --metrics=none --mbstd-group=20 --gpus=1 --batch=20
and received that above error?
Expected behavior
Obviously it should just accept that being a directory? not sure why it wouldn't be a directory? even the flags in the train.py file says it should be a directory
Screenshots
Desktop (please complete the following information):
Describe the bug
I tried doing this but it gives me an error (see below)
when resume kimg with the normal dataset of images it doesn't give me this error.
I have checked if all the images are 1024px and they are.
it seems to start training but fails after the first tick.
input code
python train.py --cfg=stylegan3-t --data=C:\deepdream-test\stylegan3-fun\dataset22\images\1024.zip --aug=ada --augpipe=bg --target=0.7 --gpus=1 --batch=8 --batch-gpu=8 --mbstd-group=8 --gamma=6.6 --mirror=1 --kimg=25000 --snap=1 --metrics=none --resume=C:\deepdream-test\stylegan3-fun\training-runs\network-snapshot-005832.pkl --resume-kimg=5832
error code
Setting up augmentation...
Distributing across 1 GPUs...
Setting up training phases...
Exporting sample images...
Initializing logs...
Training for 25000 kimg...
tick 0 kimg 5832.0 time 1m 34s sec/tick 20.5 sec/kimg 2557.87 maintenance 73.5 cpumem 4.52 gpumem 16.10 reserved 19.92 augment 0.000
Traceback (most recent call last):
File "c:\deepdream-test\stylegan3-fun\train.py", line 324, in <module>
main() # pylint: disable=no-value-for-parameter
File "C:\Users\Gebruiker\anaconda3\lib\site-packages\click\core.py", line 1130, in __call__
return self.main(*args, **kwargs)
File "C:\Users\Gebruiker\anaconda3\lib\site-packages\click\core.py", line 1055, in main
rv = self.invoke(ctx)
File "C:\Users\Gebruiker\anaconda3\lib\site-packages\click\core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "C:\Users\Gebruiker\anaconda3\lib\site-packages\click\core.py", line 760, in invoke
return __callback(*args, **kwargs)
File "c:\deepdream-test\stylegan3-fun\train.py", line 317, in main
launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
File "c:\deepdream-test\stylegan3-fun\train.py", line 95, in launch_training
subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
File "c:\deepdream-test\stylegan3-fun\train.py", line 50, in subprocess_fn
training_loop.training_loop(rank=rank, **c)
File "c:\deepdream-test\stylegan3-fun\training\training_loop.py", line 260, in training_loop
phase_real_img, phase_real_c = next(training_set_iterator)
File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 521, in __next__
data = self._next_data()
File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 1229, in _process_data
data.reraise()
File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\_utils.py", line 425, in reraise
raise self.exc_type(msg)
AssertionError: Caught AssertionError in DataLoader worker process 1.
Original Traceback (most recent call last):
File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\_utils\worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "c:\deepdream-test\stylegan3-fun\training\dataset.py", line 99, in __getitem__
assert list(image.shape) == self.image_shape
AssertionError
Describe the bug
some files in the zip get corrupted when trying to zip it
how can i use it without zip files?
tick 0 kimg 11252.0 time 1m 39s sec/tick 22.9 sec/kimg 2864.63 maintenance 75.8 cpumem 4.66 gpumem 17.19 reserved 19.70 augment 11.202
tick 1 kimg 11256.0 time 17m 46s sec/tick 953.9 sec/kimg 238.48 maintenance 13.3 cpumem 4.71 gpumem 14.83 reserved 18.82 augment 11.186
tick 2 kimg 11260.0 time 33m 31s sec/tick 931.1 sec/kimg 232.77 maintenance 13.6 cpumem 4.71 gpumem 14.88 reserved 18.82 augment 11.170
tick 3 kimg 11264.0 time 49m 06s sec/tick 922.5 sec/kimg 230.63 maintenance 13.2 cpumem 4.72 gpumem 14.85 reserved 18.82 augment 11.155
tick 4 kimg 11268.0 time 1h 04m 52s sec/tick 932.2 sec/kimg 233.06 maintenance 13.8 cpumem 4.72 gpumem 15.01 reserved 18.82 augment 11.140
tick 5 kimg 11272.0 time 1h 20m 43s sec/tick 936.9 sec/kimg 234.21 maintenance 13.8 cpumem 4.72 gpumem 14.96 reserved 18.82 augment 11.132
tick 6 kimg 11276.0 time 1h 36m 30s sec/tick 933.1 sec/kimg 233.28 maintenance 13.6 cpumem 4.72 gpumem 14.76 reserved 18.82 augment 11.117
Traceback (most recent call last):
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\StyleGAN3\train.py", line 330, in <module>
main() # pylint: disable=no-value-for-parameter
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\click\core.py", line 1130, in __call__
return self.main(*args, **kwargs)
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\click\core.py", line 1055, in main
rv = self.invoke(ctx)
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\click\core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\click\core.py", line 760, in invoke
return __callback(*args, **kwargs)
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\StyleGAN3\train.py", line 323, in main
launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\StyleGAN3\train.py", line 92, in launch_training
subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\StyleGAN3\train.py", line 50, in subprocess_fn
training_loop.training_loop(rank=rank, **c)
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\StyleGAN3\training\training_loop.py", line 260, in training_loop
phase_real_img, phase_real_c = next(training_set_iterator)
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\torch\utils\data\dataloader.py", line 521, in __next__
data = self._next_data()
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\torch\utils\data\dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\torch\utils\data\dataloader.py", line 1229, in _process_data
data.reraise()
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\torch\_utils.py", line 425, in reraise
raise self.exc_type(msg)
zipfile.BadZipFile: Caught BadZipFile in DataLoader worker process 2.
Original Traceback (most recent call last):
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\torch\utils\data\_utils\worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\StyleGAN3\training\dataset.py", line 97, in __getitem__
image = self._load_raw_image(self._raw_idx[idx])
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\StyleGAN3\training\dataset.py", line 227, in _load_raw_image
image = np.array(PIL.Image.open(f))
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\PIL\Image.py", line 719, in __array__
new["data"] = self.tobytes()
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\PIL\Image.py", line 762, in tobytes
self.load()
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\PIL\ImageFile.py", line 239, in load
s = read(self.decodermaxblock)
File "C:\Users\Gebruiker\AppData\Roaming\Visions of Chaos\Examples\MachineLearning\venv\voc_base\lib\site-packages\PIL\PngImagePlugin.py", line 921, in load_read
self.fp.
read(4) # CRC
File "C:\Python\lib\zipfile.py", line 922, in read
data = self._read1(n)
File "C:\Python\lib\zipfile.py", line 1012, in _read1
self._update_crc(data)
File "C:\Python\lib\zipfile.py", line 940, in _update_crc
raise BadZipFile("Bad CRC-32 for file %r" % self.name)
zipfile.BadZipFile: Bad CRC-32 for file '00006/img00006513.png'
Hi！Thank you for your excellent work and summary！
I would like to know how to use multi-modal Based Truncation.
As we know, freeze the mapping network and affine transformation layer during fine-tuning phase to better preserve semantic. The official repository only supports freezeD. if add freeze M and A, I think it is useful to explore the trained models without unnecessary headaches.
is it possible to activate this in the generate video code?
Describe the bug
the storage of the remote pc was full so it stopped training but it's already pretty far into training
i'd like to resume without it blurring the new gens
maybe it's possible with the log file?
tick 1587 kimg 6348.0 time 7d 08h 07m sec/tick 396.5 sec/kimg 99.12 maintenance 0.3 cpumem 6.35 gpumem 30.74 reserved 41.21 augment 34.266
Is your feature request related to a problem? Please describe.
Is it possible to load a pretrained model at different resolution? I have a pretrained at 512x512 and I would start from it to train a new one at 256x256.
Describe the solution you'd like
Automatic recovery of previously trained layers, when they match
Describe alternatives you've considered
Resize images, but train at 512 require too much time
Traceback (most recent call last):
File "train.py", line 369, in
main() # pylint: disable=no-value-for-parameter
File "/root/miniconda3/lib/python3.8/site-packages/click/core.py", line 1157, in call
return self.main(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/click/core.py", line 1078, in main
rv = self.invoke(ctx)
File "/root/miniconda3/lib/python3.8/site-packages/click/core.py", line 1434, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/root/miniconda3/lib/python3.8/site-packages/click/core.py", line 783, in invoke
return __callback(*args, **kwargs)
File "train.py", line 362, in main
launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
File "train.py", line 94, in launch_training
torch.multiprocessing.spawn(fn=subprocess_fn, args=(c, temp_dir), nprocs=c.num_gpus)
File "/root/miniconda3/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 230, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/root/miniconda3/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 188, in start_processes
while not context.join():
File "/root/miniconda3/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 150, in join
raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException:
-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/root/miniconda3/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 59, in wrap
fn(i, *args)
File "/root/autodl-tmp/stylegan3-fun-main/train.py", line 50, in subprocess_fn
training_loop.training_loop(rank=rank, **c)
File "/root/autodl-tmp/stylegan3-fun-main/training/training_loop.py", line 163, in training_loop
misc.copy_params_and_buffers(resume_data[name], module, require_all=False)
File "/root/autodl-tmp/stylegan3-fun-main/torch_utils/misc.py", line 162, in copy_params_and_buffers
tensor.copy(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
RuntimeError: The size of tensor a (4) must match the size of tensor b (3) at non-singleton dimension 1
The previous training ended at 140 rounds, I used the parameter "--resume-kimg=140" to continue this training, training to 260 rounds, but I found that the two tensorboard output log files did not lose equal when training to 140 rounds, what is the reason.
Or should I use "--resume-kimg=264" instead of 260 for my next follow-up training?
This is the parameter I used for training
This is the display panel of tesnorboard
Running train.py results in the following runtime error:
RuntimeError: aten::grid_sampler_2d_backward() is missing value for argument 'output_mask'. Declaration: aten::grid_sampler_2d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor)
Changes from the below commit to conv2d_gradfix.py & grid_sample_gradfix.py will correct issues:
NVlabs@407db86
Describe the bug
Creating environment results in pytorch CPU being downloaded
Clip by openAI addition results in torch 1.7.1 being downloaded, unsure if that was cause for pytorch CPU version
To Reproduce
I would run "Conda clean -a" and "pip cache purge"
Then attemp to build environment. Doing so would not allow me to train using
"python train.py --outdir=C:\AI\output\stylegan --cfg=stylegan3-r --data=C:\AI\data\data-512x512.zip --gpus=1 --batch=12 --gamma=8.2 --mirror=1"
or similar commands
Expected behavior
running train.py not erroring out
Screenshots
Desktop (please complete the following information):
When I try to start training using --cfg=stylegan2-ext then it errors out with the following message:
"TypeError: __init__() got an unexpected keyword argument 'extended_sgan2'"
I'm seeing an error when running the Circular Interpolation portion of the generate.py script. Check out the error pasted below.
I don't believe that I'm passing any illegal values or character into the attributes. Am I missing something simple or is it a bug?
Here is a Google Colab notebook showcasing the issue (logs included). Included at the bottom of the notebook is a proof-of-concept test using just the required attributes, and then also a more realistic use case scenario. Both return the same error.
https://colab.research.google.com/drive/1VADM8w2b9fSnO_25axSJB44hMY8jnCB2?usp=sharing
Traceback (most recent call last):
File "generate.py", line 743, in <module>
main() # pylint: disable=no-value-for-parameter
File "/usr/local/lib/python3.7/dist-packages/click/core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/usr/local/lib/python3.7/dist-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/usr/local/lib/python3.7/dist-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/usr/local/lib/python3.7/dist-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/click/decorators.py", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File "generate.py", line 704, in circular_video
videoclip = moviepy.editor.VideoClip(make_frame, duration=duration_sec)
File "/usr/local/lib/python3.7/dist-packages/moviepy/video/VideoClip.py", line 86, in __init__
self.size = self.get_frame(0).shape[:2][::-1]
File "<decorator-gen-10>", line 2, in get_frame
File "/usr/local/lib/python3.7/dist-packages/moviepy/decorators.py", line 89, in wrapper
return f(*new_a, **new_kw)
File "/usr/local/lib/python3.7/dist-packages/moviepy/Clip.py", line 94, in get_frame
return self.make_frame(t)
File "generate.py", line 689, in make_frame
dlatents = gen_utils.z_to_dlatent(G, latents, label, truncation_psi=1.0) # Get the pure dlatent
TypeError: z_to_dlatent() got an unexpected keyword argument 'truncation_psi'
I'm pretty sure i wouldn't get any support in official SG3 repo, because it all looks abandoned, the issues of this repo mostly remain silently unanswered.
I noticed that you provided some community support by answering to some issues, for the users in it, i think this is important contribution for StyleGAN community, so props to you.
I think this is the only place where this problem might be unraveled, and i thought you could shred some light on it.
Recently i've tried to train conditional model, and i'm super hyped about it, because i've been playing with SG for quite a while already, and it is the first time i was trying conditional model. The power that conditioning is able to provide is just super cool to me.
Also, turned out SG supports multiple labels out of the box, which was quite unexpected for me, and i'm even more hyped to try that out.
Papers of SG/SG2/SG3 doesn't seems to have even a single word about conditioning, but the code has it.
I was trying to find something related to it in the papers, but no luck.
Describe the bug
Everything related to the bug is already described here:
NVlabs#209
Thanks a lot in advance.
Describe the bug
my guess is that it tries to create the resume outdir twice because when I run the code it does create the outdir in the map but then gives the error below because it probably can't override it or something like that.
i also tried to leave outdir empty ad your code creates the outdir automatically but it gives the same error.
Expected behavior
only create the outdir once so it doesn't create the error
Screenshots
Output directory: C:\deepdream-test\stylegan3-fun\training-runs\00027-stylegan3-t-datasets-gpus1-batch8-gamma6.6-resume_custom
Number of GPUs: 1
Batch size: 8 images
Training duration: 25000 kimg
Dataset path: C:\deepdream-test\stylegan3-fun\dataset22\datasets.zip
Dataset size: 5953 images
Dataset resolution: 512
Dataset labels: False
Dataset x-flips: True
Dataset y-flips: False
Creating output directory...
Traceback (most recent call last):
File "C:\deepdream-test\stylegan3-fun\train.py", line 324, in <module>
main() # pylint: disable=no-value-for-parameter
File "C:\python\lib\site-packages\click\core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "C:\python\lib\site-packages\click\core.py", line 782, in main
rv = self.invoke(ctx)
File "C:\python\lib\site-packages\click\core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "C:\python\lib\site-packages\click\core.py", line 610, in invoke
return callback(*args, **kwargs)
File "C:\deepdream-test\stylegan3-fun\train.py", line 317, in main
launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
File "C:\deepdream-test\stylegan3-fun\train.py", line 86, in launch_training
os.makedirs(c.run_dir)
File "C:\python\lib\os.py", line 225, in makedirs
mkdir(name, mode)
FileExistsError: [WinError 183] Cannot create a file when that file already exists: 'C:\\deepdream-test\\stylegan3-fun\\training-runs\\00027-stylegan3-t-datasets-gpus1-batch8-gamma6.6-resume_custom'
thanks for fixing the keyerror: none and activating issues on your repo :)
stylegan3-fun/torch_utils/gen_utils.py
Line 582 in 9e61f3a
hello,
so i've been thinking on how to implement the animating of the stylegan to music in the visualizer.
this should be fairly easy to be honest.
i found this BPM detector on github. (it's pretty fast, calculates the bpm in +-2 seconds)
https://github.com/scaperot/the-BPM-detector-python/tree/4156ea7ba0f0883ff8ff3fa52fd386aa93ff9478
the code for running this python file is
python bpm_detection.py --filename song_name.wav
the native animation speed of the visualizer is 0.25 (4 seconds per fully new image), if we calculate this into beats per minute this is
60 (seconds) * 0.25 (anim speed) = 15 BPM
so basically an "anim speed of 1" is "1 second" so "60 BPM"
so if we want to calculate the anim speed for a generated bpm we do this (for example BPM=101.626)
101.626(generated BPM) divided by 60(seconds) = 1.6938(anim speed)
so basically the calculation is
"generated BPM" divided by "60 seconds" = "anim speed" (the 60 seconds always remains constant because it's beats per Minute but could be divided or multiplied by a factor of 2 to keep the sync but make the animation faster or slower (so multiply the anim speed connected to bpm by 0.125, 0.25, 0.5, 1, 2, 4, 8)
so basically we could connect the generated anim speed as a button to connect to the anim speed with then a couple more buttons to multiply or divide the anim speed by 2 to make it faster or slower but still matching the beat.
let me know what you think, i saw that u were planning to do something like this in your TODO list so i thought i'd drop it here :)
Describe the bug
Unidentified AssertionError when I run the projector.py.
To Reproduce
Steps to reproduce the behavior:
Expected behavior
I don't know what I should expect to happen, but I definitely know there's something wrong.
Error Information
Setting up PyTorch plugin "bias_act_plugin"... /home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/scipy/init.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.3
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
projector.py:447: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
target_pil = target_pil.resize((G.img_resolution, G.img_resolution), PIL.Image.LANCZOS)
Done.
Projecting in W latent space...
Starting from W midpoint using 10000 samples...
Setting up PyTorch plugin "upfirdn2d_plugin"... Done.
Traceback (most recent call last):
File "projector.py", line 549, in
run_projection() # pylint: disable=no-value-for-parameter
File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/click/core.py", line 1128, in call
return self.main(*args, **kwargs)
File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/click/core.py", line 1053, in main
rv = self.invoke(ctx)
File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/click/core.py", line 1395, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/click/core.py", line 754, in invoke
return __callback(*args, **kwargs)
File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/click/decorators.py", line 26, in new_func
return f(get_current_context(), *args, **kwargs)
File "projector.py", line 456, in run_projection
projected_w_steps, run_config = project(
File "projector.py", line 178, in project
synth_features = vgg16(synth_images, resize_images=False, return_lpips=True)
File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "", line 71, in forward
AssertionError
Environment
BTW, I use the slurm to submit my work to the lab's server. I have successfully done the training on my own dataset. And the dataset is not about human faces, the images in my dataset are grayscale digital elevation maps (DEM) with a resolution of 1024x1024. This error is unidentified through the log. Any effort on solving this error is appreciated.
I tried to train images with transparency using train.py from argb branch, but it still fails, here are some error messages, can you help me with it?
Setting up PyTorch plugin "bias_act_plugin"... Done.
Setting up PyTorch plugin "filtered_lrelu_plugin"... Done.
Generator Parameters Buffers Output shape Datatype
--- --- --- --- ---
mapping.fc0 262656 - [32, 512] float32
mapping.fc1 262656 - [32, 512] float32
mapping - 512 [32, 16, 512] float32
synthesis.input.affine 2052 - [32, 4] float32
synthesis.input 262144 1545 [32, 512, 36, 36] float32
synthesis.L0_36_512.affine 262656 - [32, 512] float32
synthesis.L0_36_512 2359808 25 [32, 512, 36, 36] float16
synthesis.L1_36_512.affine 262656 - [32, 512] float32
synthesis.L1_36_512 2359808 25 [32, 512, 36, 36] float16
synthesis.L2_36_512.affine 262656 - [32, 512] float32
synthesis.L2_36_512 2359808 25 [32, 512, 36, 36] float16
synthesis.L3_36_512.affine 262656 - [32, 512] float32
synthesis.L3_36_512 2359808 25 [32, 512, 36, 36] float16
synthesis.L4_52_512.affine 262656 - [32, 512] float32
synthesis.L4_52_512 2359808 37 [32, 512, 52, 52] float16
synthesis.L5_52_512.affine 262656 - [32, 512] float32
synthesis.L5_52_512 2359808 25 [32, 512, 52, 52] float16
synthesis.L6_52_512.affine 262656 - [32, 512] float32
synthesis.L6_52_512 2359808 25 [32, 512, 52, 52] float16
synthesis.L7_52_512.affine 262656 - [32, 512] float32
synthesis.L7_52_512 2359808 25 [32, 512, 52, 52] float16
synthesis.L8_84_512.affine 262656 - [32, 512] float32
synthesis.L8_84_512 2359808 37 [32, 512, 84, 84] float16
synthesis.L9_84_512.affine 262656 - [32, 512] float32
synthesis.L9_84_512 2359808 25 [32, 512, 84, 84] float16
synthesis.L10_84_512.affine 262656 - [32, 512] float32
synthesis.L10_84_512 2359808 25 [32, 512, 84, 84] float16
synthesis.L11_84_512.affine 262656 - [32, 512] float32
synthesis.L11_84_512 2359808 25 [32, 512, 84, 84] float16
synthesis.L12_84_512.affine 262656 - [32, 512] float32
synthesis.L12_84_512 2359808 25 [32, 512, 84, 84] float16
synthesis.L13_64_512.affine 262656 - [32, 512] float32
synthesis.L13_64_512 2359808 25 [32, 512, 64, 64] float16
synthesis.L14_64_4.affine 262656 - [32, 512] float32
synthesis.L14_64_4 2052 1 [32, 4, 64, 64] float16
synthesis - - [32, 4, 64, 64] float32
--- --- --- --- ---
Total 37768712 2432 - -
Setting up PyTorch plugin "upfirdn2d_plugin"... Done.
Discriminator Parameters Buffers Output shape Datatype
--- --- --- --- ---
b64.fromrgb 2560 16 [32, 512, 64, 64] float16
b64.skip 262144 16 [32, 512, 32, 32] float16
b64.conv0 2359808 16 [32, 512, 64, 64] float16
b64.conv1 2359808 16 [32, 512, 32, 32] float16
b64 - 16 [32, 512, 32, 32] float16
b32.skip 262144 16 [32, 512, 16, 16] float16
b32.conv0 2359808 16 [32, 512, 32, 32] float16
b32.conv1 2359808 16 [32, 512, 16, 16] float16
b32 - 16 [32, 512, 16, 16] float16
b16.skip 262144 16 [32, 512, 8, 8] float16
b16.conv0 2359808 16 [32, 512, 16, 16] float16
b16.conv1 2359808 16 [32, 512, 8, 8] float16
b16 - 16 [32, 512, 8, 8] float16
b8.skip 262144 16 [32, 512, 4, 4] float16
b8.conv0 2359808 16 [32, 512, 8, 8] float16
b8.conv1 2359808 16 [32, 512, 4, 4] float16
b8 - 16 [32, 512, 4, 4] float16
b4.mbstd - - [32, 513, 4, 4] float32
b4.conv 2364416 16 [32, 512, 4, 4] float32
b4.fc 4194816 - [32, 512] float32
b4.out 513 - [32, 1] float32
--- --- --- --- ---
Total 26489345 288 - -
Setting up augmentation...
Distributing across 1 GPUs...
Setting up training phases...
Exporting sample images...
Initializing logs...
Skipping tfevents export: No module named 'tensorboard'
Training for 10000 kimg...
C:\Users\John\Desktop\New\stylegan3-fun-rgba\training\augment.py:231: UserWarning: Specified kernel cache directory could not be created! This disables kernel caching. Specified directory is C:\Users\John\AppData\Local\Temp/torch/kernels. This warning will appear only once per process. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\cuda\jit_utils.cpp:860.)
s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std)
Traceback (most recent call last):
File "train.py", line 330, in <module>
main() # pylint: disable=no-value-for-parameter
File "C:\Users\John\AppData\Local\Programs\Python\Python38\lib\site-packages\click\core.py", line 1130, in __call__
return self.main(*args, **kwargs)
File "C:\Users\John\AppData\Local\Programs\Python\Python38\lib\site-packages\click\core.py", line 1055, in main
rv = self.invoke(ctx)
File "C:\Users\John\AppData\Local\Programs\Python\Python38\lib\site-packages\click\core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "C:\Users\John\AppData\Local\Programs\Python\Python38\lib\site-packages\click\core.py", line 760, in invoke
return __callback(*args, **kwargs)
File "train.py", line 323, in main
launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
File "train.py", line 92, in launch_training
subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
File "train.py", line 50, in subprocess_fn
training_loop.training_loop(rank=rank, **c)
File "C:\Users\John\Desktop\New\stylegan3-fun-rgba\training\training_loop.py", line 279, in training_loop
loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg)
File "C:\Users\John\Desktop\New\stylegan3-fun-rgba\training\loss.py", line 75, in accumulate_gradients
gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma)
File "C:\Users\John\Desktop\New\stylegan3-fun-rgba\training\loss.py", line 59, in run_D
img = self.augment_pipe(img)
File "C:\Users\John\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Users\John\Desktop\New\stylegan3-fun-rgba\training\augment.py", line 370, in forward
raise ValueError('Image must be RGB (3 channels) or L (1 channel)')
ValueError: Image must be RGB (3 channels) or L (1 channel)```
I have been struggling to utilize 2 GPU's when training. After executing the code below, everything loads as usual, and then it stalls when reaching the training step. But when I execute the code below using <--gpus=1> then it run perfectly.
python train.py --outdir=results --cfg=stylegan2 --metrics=None --data=escher-512.zip --kimg=5000 --gamma=10 --gpus=2 --batch=32 --batch-gpu=8 --resume=stylegan2-ffhq-512x512.pkl
I'm not running out of VRAM (x2: Quadro RTX 5000 16GB) or RAM (32GB). Here is a screenshot where you can see both GPU's have 0% load for an extended time:
I believe that both GPU's are correctly setup and StyleGAN2 should be able to use them both. Here is a screenshot after having run:
nvidia-smi
I was doing some googling to see if anyone else has had a similar issue... And interestingly this recent issue over on the original repository seems to describe my problem precisely. Yet when I tried out the suggested fix then I still experienced the same problem as before with it stalling upon reaching the training step.
Am I missing some detail or is this a bug? Thanks!
It was not clear to me how I could specify the latent space variables to generate.py, because it seems you can only provide a seed. Can I provide arguments or ranges to generate.py corresponding to the two latent space variables that one can control in visualizer.py?
Thank you & sorry if this is an obvious question.
When using the StyleGAN3 interactive visualization tool, you can checkmark specific nodes to visualize the internal representations of the model. Here is an example -
https://github.com/NVlabs/stylegan3/blob/main/docs/stylegan3-teaser-1920x1006.png
https://nvlabs-fi-cdn.nvidia.com/_web/stylegan3/videos/video_8_internal_activations.mp4
But it is possible to specify and visualize these nodes using the gen_video.py script? I'm using StyleGAN3 within Google Colab and would like to render out video of the internal representations of a specific sequence of seeds.
Also, thank you for releasing this amazing fork! I've been using it to train very small datasets (500 to 1500 images) and so the added mirrorY attribute has been useful, along with the "stabilize-video" attribute too. Here is some of my projects if you're curious.
I lost hours to this "trolling to Pillow" since I'm actually working with a grayscale SG network 😢
Is there a better way to name this or at least call attention to the fact this is for 3-channel models?
Thanks!
if convert_to_grayscale:
image = image.convert('L').convert('RGB') # We do a little trolling to Pillow (so we have a 3-channel image)
I have been training a gans with the stylegans3-r configuration for about 7kimgs. I am using your fork and have taken advice from some of the other issues on the main repo that you have commented on in the past.
I have been using these arguments: --gpus=8 --batch=32 --gamma=32 --aug=ada --augpipe=bg --target=0.8 --initstrength=[last training round augment score] --snap=10 --img-snap=10 --mirror=1 --metrics=none --resume-kimg=[foobar k imgs]
At a little over 5kimgs the generated images began to be rotated 90 degrees right, then after some time they transitioned to being rotated 90 degrees the opposite direction. They have currently been rotated upside down.
Is this a normal part of the training progression in which the generated images will return to the proper orientation, or is this some type of mode collapse or augmentation leak?
In case its useful the images were non square so white bars have been added to each side to make them square.
The objects in the images themselves are not perfectly symmetrical especial since there is various high and low perspectives.
The images themselves look ok but definitely not converged.
As stylegans3 training is significantly slower than 2 is I have put the training on hiatus until I know if I can continue training this model or if I should start over from scratch.
Is your feature request related to a problem? Please describe.
I'd like to be able to generate images using the Metal Performance Shaders (MPS) pytorch acceleration for M1 macs
Describe the solution you'd like
Run the generate.py script with device=mps
Describe alternatives you've considered
I've modified the code and am able to run the function, but the resulting image is completely gray. I'm curious if anyone else has tried and succeeded.
The issue seems to be in w_to_img -> G.synthesis(). The output of that function does not match the output when I run with device=cpu. Up until that point everything matched (for example, the output of get_w_from_seed was correct with mps).
The code changes were:
if torch.cuda.is_available() and device == 'cuda':
device = torch.device('cuda')
elif torch.backends.mps.is_available() and device == "mps":
device = torch.device('mps')
else:
device = torch.device('cpu')
z = z.astype(np.float32)
w = G.mapping(torch.from_numpy(z).to(device), None)
(https://github.com/rinongal/StyleGAN-nada/blob/StyleGAN3-NADA/stylegan3_nada.ipynb)
i'm making some tweaks to my stylegan3 network with this but it seems like i'm not able to import it to the visualizer
any way to get the visualizer to support these?
the generating of images in this repo is a little whacky but it's such a good tweak to stylegan, would be great if it worked :)
there is a fix for converting stylegan2-nada files to stylegan2 but not for 3 yet :/
https://github.com/eps696/stylegan2ada (this is the one for stylegan2 that converts the .pt files to .pkl files again)
I would like to generate a video from a projected w vector and specify the number of frames between this interpolation. The current image generator permits the option to ==projected-w, however, this does not seem possible for video. Is this currently possible?
Describe the solution you'd like
Project images as npz file (vs npy) -> combine multiple vectors into a single npz file -> generate interpolation video between projected images.
Describe alternatives you've considered
Resembles this generator - https://github.com/dvschultz/stylegan2-ada-pytorch/blob/main/generate.py, or the colab : https://colab.research.google.com/github/dvschultz/stylegan2-ada-pytorch/blob/main/SG2_ADA_PyTorch.ipynb#scrollTo=4cgezYN8Dsyh
@ !python generate.py --process=interpolation --interpolation=linear --easing=easeInOutQuad --space=w --network=/content/ladiesblack.pkl --outdir=/content/combined-proj/ --projected-w=/content/npz/combined.npz --frames=120
Amazing set of features! Thank you @PDillis
I'm trying to use dataset_tool.py to pack an ARGB image into a dataset, but when the progress bar completes, I don't see any output for the path defined by --dest, can you help me?
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