stylegan2-ada-pytorch
pixel2style2pixel
stylegan2-ada-pytorch | pixel2style2pixel | |
---|---|---|
30 | 16 | |
3,917 | 3,107 | |
0.9% | - | |
2.3 | 0.0 | |
4 months ago | over 1 year ago | |
Python | Jupyter Notebook | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
stylegan2-ada-pytorch
-
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
-
[R] StyleGAN2-ADA on Power 9?!
I am talking about the original Nvidia implementation here: https://github.com/NVlabs/stylegan2-ada-pytorch
-
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...
-
[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).
-
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.
-
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
-
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)
-
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
-
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.
pixel2style2pixel
-
The one time it creates legible text
I wouldn't describe it like that. Consider a simpler example. StyleGAN can make plausible looking face that doesn't look like any of the individual faces it was trained on. It's not making a face collage out of this guy's chin pixels and that guy's eyebrows pixels. There's an easy way to test this: give it a photo of yourself or someone you know with something like pixel2style2pixel and it will probably give you back something convincing. But you weren't in the training. What it's actually doing is interpolating between plausible facial features in a space that it's laid for what human being could conceivably look like.
- stylegan3 encoder for image inversion
- desculpa bapo. mas nao fui eu, foi uma IA!!
-
Am i the only one who thinks this lil guy looks alot like michele reves?
i think its this one https://github.com/eladrich/pixel2style2pixel
-
I used AI to generate real life for honor character faces
What did you use to generate this? Was it https://github.com/eladrich/pixel2style2pixel or something else? Curious
-
[R] a Metric for finding the best StyleGAN Latent Encoders
Right now we have encoders like pSp and restyle or encoder4editing, but how can we tell which one performs better than the other?
-
[OC] This NPC Does Not Exist: I created an AI to generate NPC portraits
The portaitify tool uses pixel2style2pixel to invert a picture into a 'style vector' then generate the corresponding image with the stylegan 2 generator. Happy to a higher or lower level description if that's of interest!
-
Should i start with Windows or Linux environment for ML?
Hi, recently I started playing with ML in python (anaconda in Windows 10), using relevant packages for tensorflow, torch and cuda and running some models. I would like to play with shared projects like the ones in https://paperswithcode.com/, like this one: https://github.com/eladrich/pixel2style2pixel, but many requiere Linux.
-
How do I get a GAN to write a dubstep drop?
I did something like this. Many image GAN papers have implementations on GitHub, just pick the model you want. State-of-the-art image translation is probably something like Pixel2Style2Pixel (https://github.com/eladrich/pixel2style2pixel). Note that there are also wave GANs and they have slightly(?) better audio on average. With image models, typically people input mel spectrograms, which discard the phase information (you could also input 2 channel images for the real and complex parts, but I haven't seen any projects that do that). `librosa` has functions for the Fourier transform and its inverse (Griffin Lim algorithm), but if you want high quality reconstructions try using a neural network solution like WaveGlow to do the inverse conversion (if you're training a GAN, you can fine-tune WaveGlow). The biggest bottleneck is data - get as much data as possible. Also check out /r/machinelearning.
What are some alternatives?
stylegan3 - Official PyTorch implementation of StyleGAN3
BigGAN-PyTorch - The author's officially unofficial PyTorch BigGAN implementation.
encoder4editing - Official implementation of "Designing an Encoder for StyleGAN Image Manipulation" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02766
StyleFlow - StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continuous Normalizing Flows (ACM TOG 2021)
stylegan3-editing - Official Implementation of "Third Time's the Charm? Image and Video Editing with StyleGAN3" (AIM ECCVW 2022) https://arxiv.org/abs/2201.13433
lucid-sonic-dreams
ganspace - Discovering Interpretable GAN Controls [NeurIPS 2020]
data-efficient-gans - [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
Deep-Learning - In-depth tutorials on deep learning. The first one is about image colorization using GANs (Generative Adversarial Nets).
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