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View Code? Open in Web Editor NEW[AAAI 2022] Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization
Home Page: https://arxiv.org/abs/2103.11784
License: Apache License 2.0
[AAAI 2022] Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization
Home Page: https://arxiv.org/abs/2103.11784
License: Apache License 2.0
In your paper you mention changing the decoder of adain to change the stroke size.
what is the difference between the decoders e.g. "decoder_stroke_perceptual_loss_1.pth" ?
In my case, I would like to convert an ultra high resolution (8192x8192), but applying the same "huge" stroke size, as I would if I would resize the picture to 1024x1024
Hi @czczup! Thanks for your awesome work!
Since you've shown that many style transfer methods are compatible with TIN, I wonder if it's possible to use TIN in Aesthetic UST?
Following figure overviews how AesUST work.
They introduced AesSA module which confuses me where to put the TIN module. Could you please help me out?
Best,
Rahul Bhalley
possible to change from adam optimizer to L-BFGS?
not particularly excellent at any of this (code etc.) - so even just a simple, yes or no, will suffice, even as to prevent me from wasting hours attempting to cut and paste stuff that will never work?
thanks again
Hello!
Any chance you can make a google colab version? I am having trouble setting this up.
no problems, fixed. - thank you so very much for this, this is truly excellent, what a package!
I want to use Wang2020 Colaborative for style transfer. The readme says to use test.py and config, style arguments. There is no test.py fail and main.py (which I think you maean) lacks these attributes. Could you please update the instructions in the readme so that one can use the code easily?
Hi @czczup,
Thanks for implementing Multi Model Style Transfer. But it consumes very huge memory and then the program gets killed (for default patch and thumbnail sizes). I'm using --URST
flag for these experiments.
This screenshot is for:
Hi,
Sorry to bother again - lets say; roughly, forgive my expression;
using cysmith neural transfer (LGBFS optimizer), I process 4 x (500 x 500) slices of a lets say 2000 x 2000 pixel image-A, and I process one total picture thumbnail of 500 x 500 pixel of image-A (i suppose, downsampled from the original 2000 x 2000 image-A)
now, using your TIN method;
would I be able to 'stitch' these back together and then get rid of the 'stitch' area problems?
is it possible to string together or isolate some code as to achieve something like this?
thanks again for your time and consideration
(Wang) C:\Users\Bob\URST\Li2017Universal>python test.py --content c:\users\bob\desktop\ds.jpg --style c:\users\bob\desktop\dsstyle.jpg --URST
Traceback (most recent call last):
File "test.py", line 166, in
wct = WCT(args).to(device)
File "C:\Users\Bob\URST\Li2017Universal\util.py", line 16, in init
vgg1 = torchfile.load(args.vgg1)
File "C:\Users\Bob\anaconda3\envs\Wang\lib\site-packages\torchfile.py", line 424, in load
return reader.read_obj()
File "C:\Users\Bob\anaconda3\envs\Wang\lib\site-packages\torchfile.py", line 370, in read_obj
obj._obj = self.read_obj()
File "C:\Users\Bob\anaconda3\envs\Wang\lib\site-packages\torchfile.py", line 385, in read_obj
k = self.read_obj()
File "C:\Users\Bob\anaconda3\envs\Wang\lib\site-packages\torchfile.py", line 386, in read_obj
v = self.read_obj()
File "C:\Users\Bob\anaconda3\envs\Wang\lib\site-packages\torchfile.py", line 370, in read_obj
obj._obj = self.read_obj()
File "C:\Users\Bob\anaconda3\envs\Wang\lib\site-packages\torchfile.py", line 387, in read_obj
obj[k] = v
TypeError: unhashable type: 'list'
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