stylegan2-ada-pytorch
StyleFlow
stylegan2-ada-pytorch | StyleFlow | |
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30 | 6 | |
3,917 | 2,389 | |
0.9% | - | |
2.3 | 0.0 | |
4 months ago | about 1 year ago | |
Python | Python | |
GNU General Public License v3.0 or later | - |
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stylegan2-ada-pytorch
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Samsung expected to report 80% profit plunge as losses mount at chip business
> there is really nothing that "normal" AI requires that is bound to CUDA. pyTorch and Tensorflow are backend agnostic (ideally...).
There are a lot of optimizations that CUDA has that are nowhere near supported in other software or even hardware. Custom cuda kernels also aren't as rare as one might think, they will often just be hidden unless you're looking at libraries. Our more well known example is going to be StyleGAN[0] but it isn't uncommon to see elsewhere, even in research code. Swin even has a cuda kernel[1]. Or find torch here[1] (which github reports that 4% of the code is cuda (and 42% C++ and 2% C)). These things are everywhere. I don't think pytorch and tensorflow could ever be agnostic, there will always be a difference just because you have to spend resources differently (developing kernels is time resource). We can draw evidence by looking at Intel MKL, which is still better than open source libraries and has been so for a long time.
I really do want AMD to compete in this space. I'd even love a third player like Intel. We really do need competition here, but it would be naive to think that there's going to be a quick catchup here. AMD has a lot of work to do and posting a few bounties and starting a company (idk, called "micro grad"?) isn't going to solve the problem anytime soon.
And fwiw, I'm willing to bet that most AI companies would rather run in house servers than from cloud service providers. The truth is that right now just publishing is extremely correlated to compute infrastructure (doesn't need to be but with all the noise we've just said "fuck the poor" because rejecting is easy) and anyone building products has costly infrastructure.
[0] https://github.com/NVlabs/stylegan2-ada-pytorch/blob/d72cc7d...
[1] https://github.com/microsoft/Swin-Transformer/blob/2cb103f2d...
[2] https://github.com/pytorch/pytorch/tree/main/aten/src
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[R] StyleGAN2-ADA on Power 9?!
I am talking about the original Nvidia implementation here: https://github.com/NVlabs/stylegan2-ada-pytorch
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This X Does Not Exist
I think you should be able to find a latent vector that returns a cat that is part of the original training data (or at least very close to it). Most of the outputs will not be real cats at all though. However, it's pretty simple to try and find the latent vector that reproduces a given image, e.g. https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/pr...
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[P] Frechet Inception Distance
One irritating flaw with FID is that scores are massively biased by the number of samples, that is, the fewer samples you use, the larger the score. So to make comparisons fair it's absolutely crucial to use the same number of samples. From what I've seen on standard benchmarks it's pretty common now to compute Inception features for every single data point, but only for 50k samples from generative models (for reference off the top of my head StyleGAN2-ADA does this, see Appendix A).
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generating images
You can follow the development of stylegan from NVIDIA: https://github.com/NVlabs/stylegan2-ada-pytorch They have formed datasets containing human faces, maybe you can use human faces with expressions as classes and train conditional GAN with your own classes.
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What is the best GAN architecture for image data augmentation?
Given the lack of data StyleGan 2 by Nvidia, which was specifically created to handle small datasets could be an option - https://github.com/NVlabs/stylegan2-ada-pytorch
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City Does Not Exist
First, you have to collect a few thousand images of the same thing (maybe more or less depending on how complex your thing is or how good the results should be). Then, you train a generative adversarial neural network on those images to generate new images. https://github.com/NVlabs/stylegan2-ada-pytorch works quite well. https://github.com/NVlabs/stylegan3 is supposedly even better, but I did not try it yet.
- Modern Propaganda (this person does not exist)
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From 53% to 95% acc - Real vs Fake Faces Classification | Fine-tuning EfficientNet (Github in comment)
What NVIDIA does when computing Perceptual Path Length is to center crop the faces before computing the metric. Here you can find the code to get an idea https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/metrics/perceptual_path_length.py
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StyleGAN2 ADA Pytorch ends after tick 0 with no errors.
I\m trying to train StyleGAN2 ADA Pytorch https://github.com/NVlabs/stylegan2-ada-pytorch on my own dataset.
StyleFlow
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StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows
Github: https://github.com/RameenAbdal/StyleFlow
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Laptop Nvidia GPU suddenly started overheating, but only in some applications. Possible causes?
I also recently installed NVIDIA's CUDA toolkit and cuDNN as I tried to get a tool from a scientific paper on neural networks to work (StyleFlow (GitHub)). That succeeded and after launching the tool for the first time and using it for ~30s, my laptop suddenly turned itself off. I didn't think much of it at the time since the code was tested on desktop PCs running Linux with more potent GPUs, not my Windows 10 mobile-GPU laptop. Around this point, I also upgraded my NVIDIA driver to 496.13 (I can't remember if that was before or after using StyleFlow).
- [P] Suggest a Conditional GAN for a project?
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What the heck do I do if I took some really great photos, but their face just.. does not work at all.
Learn to use some magic? https://rameenabdal.github.io/StyleFlow/
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The exponential improvement of "StyleFlow" over "StyleGAN2". Aging and other modifications off the chart. This new computing derived AI is proprietary and was released exclusively to "Two Minute Papers"
This isn't proprietary anymore. It was opensourced like a month ago.
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Super new addition to GANs
Their repository includes a readme with instructions to clone and deploy the Docker image which contains the application.
What are some alternatives?
stylegan3 - Official PyTorch implementation of StyleGAN3
DashcamCleaner - Censor identifiable information in videos, in particular dashcam recordings in Germany.
pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
SAM - Official Implementation for "Only a Matter of Style: Age Transformation Using a Style-Based Regression Model" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02754
BigGAN-PyTorch - The author's officially unofficial PyTorch BigGAN implementation.
ALAE - [CVPR2020] Adversarial Latent Autoencoders
lucid-sonic-dreams
StyleGAN_PyTorch - The implementation of StyleGAN on PyTorch 1.0.1
data-efficient-gans - [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
sofgan - [TOG 2022] SofGAN: A Portrait Image Generator with Dynamic Styling
spleeter - Deezer source separation library including pretrained models.
restyle-encoder - Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" (ICCV 2021) https://arxiv.org/abs/2104.02699