An official implementation of MobileStyleGAN in PyTorch

Hi, thank you for sharing code! As the default batch_size=2, training is fine. But when use batch_size = 8 , get error as bellow: Hoping for your reply!

Hi @bes-dev,

In issue https://github.com/bes-dev/MobileStyleGAN.pytorch/issues/36 you told me that compute_mean_style method can be used to compute latent average vector for MobileStyleGAN. But the image produced using this vector via student network is all gray.

I ran the following code to produce image using latent average vector.

# Import libraries.
import cv2
from core.utils import load_cfg, load_weights, tensor_to_img
from core.distiller import Distiller

# Load configuration.
cfg = load_cfg("configs/mobile_stylegan_ffhq.json")

distiller = Distiller(cfg)
style_mean = distiller.compute_mean_style(style_dim=512, wsize=23)
out = distiller.student(style_mean)['img']
cv2.imwrite('style_mean.png', tensor_to_img(out[0]))

I get the following average image as output.

Furthermore, every call to compute_mean_style returns a different vector. It's very weird. Shouldn't it be the same? Also larger batch_size doesn't make any difference. :(

style_mean1 = distiller.compute_mean_style(style_dim=512, wsize=23)
style_mean2 = distiller.compute_mean_style(style_dim=512, wsize=23)
style_mean3 = distiller.compute_mean_style(style_dim=512, wsize=23)

# Comparing different means. 😅 Shouldn't they all be the same 'cause it's just an average. right?
style_mean1 == style_mean2
style_mean1 == style_mean3
style_mean2 == style_mean3

Output

tensor([[[False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         ...,
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False]]])
tensor([[[False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         ...,
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False]]])
tensor([[[False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         ...,
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False]]])

I could be missing something. Please let me know what could be the issue on my side.

Regards Rahul Bhalley

Hi @bes-dev, thanks for your work!

I am actually not able to convert MobileStyleGAN correctly. I am using the following command:

python train.py --cfg configs/mobile_stylegan_ffhq.json --checkpoint_dir . --ckpt mobilestylegan_ffhq_v2.ckpt --export-model coreml

But the generated image is all black. If I replace the synthesis network with the one provided here then the image does get generated fine. Meaning that MappingNetwork.mlmodel's parameters are loaded but not of SynthesisNetwork.mlmodel. Note that I do download the mobilestylegan_ffhq_v2.ckpt checkpoint and save it in root directory of this repository.

Hi~Thanks for your MobileStyleGAN! I notice that you offer convert_rosinality_ckpt.py,but the parameters entered by this code do not have the path to the. Pt file. It just has the parameter '--ckpt',but the output of styleGAN2 is .pt file. So I still don't know how to change my .pt file...Please give me some help,thanks.

After the conversion, the model generates the blur images. I use the file "convert_rosinality_ckpt.py" to convert the "550000.pt"(the pretrained model on 256 provided by "https://github.com/rosinality/stylegan2-pytorch") model. In order to see the generated images by styleganv2 pretrained parameters, I modified the https://github.com/bes-dev/MobileStyleGAN.pytorch/blob/d67b3d4189b8f74bf23577e649dcbc9db1b452a6/core/distiller.py#L160 with "img = self.synthesis_net(style)["img"]" to generate some images. Here are some images below. I don't know what caused this. Have you ever encountered this problem? @bes-dev

Can I use MobileStyleGan's latent space to perform face editing like anycost-gan? I have read both papers, and I hope to be able to do face editing with mobilestylegan, since mobilestylegan has faster inference time.

Hello @bes-dev,

How can I return latents from MobileStyleGAN just like StyleGAN2 does here as following:

if return_latents:
    return image, latent

How can I get the latent variable similar to this in StyleGAN2? Looks like there's some manipulation involved.

Hi!

I want to sample images from W+ space using PyTorch checkpoints. But there doesn't seem to exist any argument to generate.py script for that. Could you please guide me regarding this?

The images I sampled from CoreML's W+ space models (using both Mapping and Synthesis) were weirdly in bluish color. These models were exported using --export-w-plus argument. I've attached few of them here.

When I use W space in CoreML models then the samples are colored correctly.

Any help is highly appreciated!

Regards Rahul Bhalley

Traceback (most recent call last):
File "train.py", line 60, in main(args) File "train.py", line 42, in main trainer.fit(distiller) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 458, in fit self._run(model) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 756, in _run self.dispatch() File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 797, in dispatch self.accelerator.start_training(self) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 96, in start_training self.training_type_plugin.start_training(trainer) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 144, in start_training self._results = trainer.run_stage() File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 807, in run_stage return self.run_train() File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 869, in run_train self.train_loop.run_training_epoch() File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 489, in run_training_epoch batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 728, in run_training_batch self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 432, in optimizer_step using_lbfgs=is_lbfgs, File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/core/lightning.py", line 1403, in optimizer_step optimizer.step(closure=optimizer_closure) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/core/optimizer.py", line 214, in step self.__optimizer_step(*args, closure=closure, profiler_name=profiler_name, **kwargs) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/core/optimizer.py", line 134, in __optimizer_step trainer.accelerator.optimizer_step(optimizer, self._optimizer_idx, lambda_closure=closure, **kwargs) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 329, in optimizer_step self.run_optimizer_step(optimizer, opt_idx, lambda_closure, **kwargs) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 336, in run_optimizer_step self.training_type_plugin.optimizer_step(optimizer, lambda_closure=lambda_closure, **kwargs) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 193, in optimizer_step optimizer.step(closure=lambda_closure, **kwargs) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 26, in decorate_context return func(*args, **kwargs) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/torch/optim/adam.py", line 66, in step loss = closure() File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 723, in train_step_and_backward_closure split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 813, in training_step_and_backward result = self.training_step(split_batch, batch_idx, opt_idx, hiddens) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 280, in training_step training_step_output = self.trainer.accelerator.training_step(args) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 204, in training_step return self.training_type_plugin.training_step(*args) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 155, in training_step return self.lightning_module.training_step(*args, **kwargs) File "/home/opu/MobileStyleGAN/core/distiller.py", line 70, in training_step loss = self.generator_step(batch, batch_nb) File "/home/opu/MobileStyleGAN/core/distiller.py", line 107, in generator_step style, pred_t, gt_images = self.make_sample(batch) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 26, in decorate_context return func(*args, **kwargs) File "/home/opu/MobileStyleGAN/core/distiller.py", line 122, in make_sample gt = self.synthesis_net(style) File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/home/opu/MobileStyleGAN/core/models/synthesis_network.py", line 96, in forward img = self.upsample(img) + rgb File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/home/opu/MobileStyleGAN/core/models/modules/legacy.py", line 40, in forward out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) File "/home/opu/MobileStyleGAN/core/models/modules/ops/upfirdn2d.py", line 14, in upfirdn2d input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]) File "/home/opu/MobileStyleGAN/core/models/modules/ops/upfirdn2d_cuda.py", line 99, in forward ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) RuntimeError: CUDA error: an illegal memory access was encountered Exception ignored in: <bound method tqdm.del of <pytorch_lightning.callbacks.progress.tqdm object at 0x7f944c6c4240>> Traceback (most recent call last): File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/tqdm/std.py", line 1145, in del File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/tqdm/std.py", line 1299, in close File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/tqdm/std.py", line 1492, in display File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/tqdm/std.py", line 1148, in str File "/home/opu/anaconda3/envs/mobilestylegan/lib/python3.6/site-packages/tqdm/std.py", line 1450, in format_dict TypeError: 'NoneType' object is not iterable

when I tried to run the project, this error raised. I need help

stylegan2 has (18,512) latent vector and mobileStyleGAN map (1,512) vector to (1,512) style. i dont get it how can i encode the image to the (1,512) style that the model takes as input. thanks for help

Hi Sergei, thank you for your amazing research and this repo. By following the readme, I was able to generate the CoreML model and I noticed that the generated model fully runs on ANE in my testing which is amazing.

So far so great. My goal is to use MobileStyleGAN inside GFP-GAN, which looks like this:

From the diagram, the green "Pretrained GAN as prior" is StyleGAN2 with some additions. Their StyleGAN2 has some additions called CS-SFT that transforms the output at each layer. This StyleGAN2 takes 2 inputs style & conditions:

style.shape = [
    (16,512)
]

conditions.shape = [
    (1,256,8,8),
    (1,256,8,8),
    (1,256,16,16),
    (1,256,16,16),
    (1,256,32,32),
    (1,256,32,32),
    (1,256,64,64),
    (1,256,64,64),
    (1,128,128,128),
    (1,128,128,128),
    (1,64,256,256),
    (1,64,256,256),
    (1,32,512,512),
    (1,32,512,512)
]

So, from what you can tell, is it possible to use MobileStyleGAN in GFP-GAN? I've been playing with your repo for the past few days. If you can advise on if this will work I would really appreciate it.

If you think it's not possible then I will stop here.

Thank you.

Is there a way to use MobileStyleGAN as an image-to-image (A->B) style transfer model, similar to CycleGAN, rather than just an image synthesizer from no input? I have a custom dataset of cat faces, one set is real (domain A) and the other set are fake (domain B). I want an input of A to translate into domain B.

Hello, you pointed out in the paper that The whole network contains 8.01M parameters, has a computational complexity of 15.09 GMAC. But the size of your shared .ckpt file (mobilestylegan_ffhq_v2.ckpt) is about 689M. Can you give me some advice and why there is such a big difference? Thanks again for your guidance.

Hi, In MobileStyleGAN.pytorch, inappropriate dependency versioning constraints can cause risks.

Below are the dependencies and version constraints that the project is using

wheel
torch
pytorch-lightning==1.0.2
gdown==3.12.2
addict==2.2.1
piq==0.5.2
numpy==1.17.5
PyWavelets==1.1.1
git+://github.com/fbcotter/pytorch_wavelets.git
neptune-client==0.4.132
kornia==0.4.1
pytorch_fid
coremltools

The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict. The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.

After further analysis, in this project, The version constraint of dependency pytorch-lightning can be changed to >=0.3.6.9,<=0.5.2.1. The version constraint of dependency gdown can be changed to >=3.7.0,<=4.5.1. The version constraint of dependency kornia can be changed to >=0.2.1,<=0.6.2.

The above modification suggestions can reduce the dependency conflicts as much as possible, and introduce the latest version as much as possible without calling Error in the projects.

The invocation of the current project includes all the following methods.

pytorch_lightning.callbacks.ModelCheckpoint
pytorch_lightning.Trainer.fit
pytorch_lightning.Trainer

gdown.cached_download

kornia.augmentation.RandomHorizontalFlip
kornia.augmentation.RandomAffine
kornia.augmentation.RandomErasing

json.dump
UpFirDn2dBackward.apply
torch.no_grad
torch.rsqrt.view
core.models.discriminator.Discriminator
real_pred.F.softplus.mean
SynthesisBlock
self.idwt.size
cfg.type.pl_loggers.getattr
fid_inception_v3
Wrapper
torch.unbind
ToRGB
self.perceptual_loss
distiller
prep_filt_sfb2d
pred.self.inception.view
self.branch3x3dbl_1
self.dwt_to_img.size
R1Regularization
torch.nn.Parameter
t.add_
json.load
torch.nn.functional.max_pool2d
FIDInceptionE_2
self.modulation
j.img_s.cpu
core.models.mobile_synthesis_network.MobileSynthesisNetwork
ConstantInput
gradgrad_input.reshape.reshape
x_pred.sum
style_b.unsqueeze.repeat.unsqueeze
cv2.imwrite
style.view.unsqueeze
self.layers
idwt.DWTInverse
size.size.b.torch.randn.to
k.source_state.size
torch.nn.functional.conv_transpose2d
self.branch7x7dbl_4
self.FIDInceptionC.super.__init__
torch.tensor
modules.DWTInverse
self.loss.reg_d
range
self.branch3x3_1
self.get_demodulation.view
core.models.mapping_network.MappingNetwork
torch.tensor.sum
style_a.unsqueeze.repeat
torch.load
self.mapping_net.apply
FIDInceptionC
random.randint
cv2.waitKey
self.branch5x5_1
t.mul.add_.clamp_
UpFirDn2dBackward.apply.view
pytorch_wavelets.DWTInverse
torch.utils.data.DataLoader
self.get_demodulation
torch.utils.cpp_extension.load.fused_bias_act
gdown.cached_download
ResBlock
pytorch_lightning.callbacks.ModelCheckpoint
core.models.mapping_network.MappingNetwork.state_dict
high.view.view
torch.autograd.grad.size
torch.onnx.export
self.weight.unsqueeze
t.mul.add_.clamp_.permute
pred.squeeze.squeeze.cpu.numpy.size
self.net._modules.items
image.new_empty
distiller.size
self.to_img1
numpy.iscomplexobj
EqualConv2d
self.to_img1.view
img_t.cpu
block.conv2.load_state_dict
self.parameters
enumerate
self.branch3x3dbl_3b
self.synthesis_net.append
Blur
core.loss.perceptual_loss.PerceptualLoss
scipy.linalg.sqrtm
modules.legacy.PixelNorm.append
getattr
self.noise
torch.nn.LeakyReLU
torch.cat
grad_x.size.grad_x.view.norm
core.models.synthesis_network.SynthesisNetwork
torch.nn.ModuleList
self.student
int_to_mode
FIDInceptionE_1
torch.uint8.img.to.numpy
style_a.unsqueeze.repeat.unsqueeze
gradgrad_out.view.view
covmean.np.isfinite.all
format
UpFirDn2d.apply
conv_module
build_logger
sorted
torch.cat.view
core.utils.select_weights.size
torch.nn.BatchNorm2d
upfirdn2d_native
stddev.mean.squeeze
torch.nn.BatchNorm2d.train.to
numpy.isfinite
FusedLeakyReLU
torch.nn.functional.conv2d.size
input.reshape.reshape
block.to_rgb.load_state_dict
core.utils.download_ckpt
ctx.save_for_backward
self.branch7x7dbl_1
style.view.size
self.register_buffer
self.dwt
stddev.repeat.var
self.mapping_net
self.branch7x7dbl_2
cv2.imshow
core.distiller.Distiller.to_onnx
multiprocessing.cpu_count
ValueError
torch.nn.AdaptiveAvgPool2d
self.mapping_net.style_dim.self.wsize.torch.randn.to
weight.transpose.reshape.pow
hasattr
self.generator_step
EqualLinear
StyledConv2d
noise_injection.NoiseInjection
batch.to.to
torch.rsqrt
core.utils.tensor_to_img
torchvision.models.inception_v3.load_state_dict
self.net
bias.view
self.weight_dw.transpose
core.distiller.Distiller.to_coreml
self.student.append
self.branch3x3dbl_2
core.utils.save_cfg
distiller.cpu
torch.save
self.branch3x3_2a
torch.optim.Adam
StyledConv
out.view.view
offset.sigma1.dot
torch.cat.sigmoid
stddev.repeat.mean
modules.MultichannelIamge
img.size
core.utils.apply_trace_model_mode
block_idx.InceptionV3.to.eval
self.final_linear
self.branch7x7_3
self.get_modulation
opts.append
self.loss_weights.items
t.mul
self.branch1x1
torch.zeros
torch.nn.L1Loss
extract_snet
torch.nn.functional.interpolate
torchvision.transforms.ToTensor
pathlib.Path.glob
batch.self.mapping_net.unsqueeze.repeat
self.blocks.append
torch.autograd.grad
var.self.mapping_net.view
img.view.size
self.modulation.bias.data.fill_
self
self.conv.view
stddev.repeat.repeat
style.self.modulation.view
self.l1_loss
torch.nn.functional.conv2d
fake.self.F.softplus.mean
v.size
self.branch5x5_2
self.make_sample
target.state_dict.items
diff.dot
hidden.size.hidden.size.torch.randn.to
self.branch3x3dbl_3
math.log
self.branch3x3dbl_3a
chr
self.FIDInceptionE_1.super.__init__
mnet.layers.load_state_dict
upfirdn2d
range.t.add_.div_
torch.nn.Sequential
self.log
kernel.torch.flip.view
out.permute.permute
self.mapping_net.style_dim.torch.randn.to
RuntimeError
core.models.synthesis_network.SynthesisNetwork.state_dict
torch.utils.model_zoo.load_url
numpy.load
self.to_img1.size
weight.transpose.reshape.transpose
torch.nn.BatchNorm2d.train.to.eval
torch.utils.cpp_extension.load.upfirdn2d
self.mapping_net.style_dim.torch.randn.self.mapping_net.mean
pathlib.Path.endswith
tqdm.tqdm
snet.conv1.load_state_dict
FusedLeakyReLUFunction.apply
MultichannelIamge
torch.nn.functional.leaky_relu
model
torch.uint8.img.to.numpy.to
int
pred.squeeze.squeeze.cpu.numpy.squeeze
torch.nn.functional.adaptive_avg_pool2d
mapping_net_ckpt.MappingNetwork.eval
core.model_zoo.model_zoo
self._log_loss
block.conv1.load_state_dict
block
self.InceptionV3.super.__init__
core.utils.load_weights
coremltools.TensorType
self.loss.loss_d
img.view.view
i.snet.layers.load_state_dict
self.branch7x7_2
ConvLayer
fake_pred.F.softplus.mean
self.layers.append
ScaledLeakyReLU
target.load_state_dict
os.path.exists
grad_x.size.grad_x.view.norm.mean
torch.nn.functional.avg_pool2d
calculate_frechet_distance
numpy.atleast_2d.dot
self.student.apply
block_idx.InceptionV3.to
pytorch_lightning.Trainer.fit
core.utils.select_weights
FIDInceptionA
pred.squeeze.squeeze.cpu
numpy.abs
self.upsample
self.activate
_SFB2D
self.to_img
self.up
self.loss.reg_d.items
pytorch_lightning.Trainer
style.self.modulation.view.size
torch.load.items
self.branch3x3_2b
torch.autograd.grad.view
torch.stack
self.FIDInceptionA.super.__init__
torchvision.models.inception_v3
self.to_rgb1
outp.append
numpy.trace
out_dim.torch.zeros.fill_
synthesis_net_ckpt.SynthesisNetwork.eval
isinstance
numpy.mean
torch.cat.size
grad_output.reshape.reshape
numpy.atleast_1d
w.self.style_inv.self.scale.pow
addict.Dict
piq.KID
self.minibatch_discrimination
weight.pow.sum
make_kernel
m.wsize
numpy.atleast_2d
input.size
style_b.unsqueeze.repeat
gt.self.inception.view
fused.fused_bias_act.sum
self.img_to_dwt
self.synthesis_net
channels.append
real.detach
torch.device
distiller.to.to
torch.utils.cpp_extension.load
Upsample
self.r1_reg
print
arch.models.getattr
numpy.concatenate.append
numpy.max
self.compute_mean_style
ImagePathDataset
torch.nn.functional.linear
style.unsqueeze.repeat
shape.torch.randn.to
numpy.allclose
pywt.Wavelet
self.mapping_net.style_dim.self.wsize.torch.randn.to.to
pytorch_wavelets.DWTForward
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@developer Could please help me check this issue? May I pull a request to fix it? Thank you very much.

Thank you for amazing work. I'm in need of a pytorch lite (.ptl) version of the Pytorch (.pt) model. How may I download your .pt or .ptl model somehow? Do you have a torch.hub.load version we can download?

Hi!

Thank you for an amazing repository. I successfully converted my StyleGAN2-ada rosinality checkpoint, by running the following line: python convert_rosinality_ckpt.py --ckpt {path_to_rosinality_stylegan2_ckpt} --ckpt-mnet output/mnet.ckpt --ckpt-snet output/snet.ckpt --cfg-path output/config.json

I tested the checkpoint with demo.py and it produces images as expected.

I then converted it to ONNX by running python train.py --cfg output/config.json --export-model onnx --export-dir onnx-2 and tried to use the converted checkpoint in MobileStyleGAN web demo (https://github.com/cyrildiagne/mobilestylegan-web-demo). It produces uniform grey images for all seeds. The web demo works fine with the authors' ffhq checkpoint so it seems to be an issue with the converted model.

Do you have any thoughts on what might be causing this?