stylegan2-ada-pytorch
stylegan3
stylegan2-ada-pytorch | stylegan3 | |
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30 | 38 | |
3,917 | 6,176 | |
0.9% | 0.8% | |
2.3 | 1.1 | |
4 months ago | 8 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | 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.
stylegan3
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StyleGAN3 NvidiaLabs - The state-of-the-art in Artificial Intelligence applied to Human Face Generation.
StyleGAN3 by Nvidia Open Source Software »
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AI's Triumph: Lifelike Human Faces through GAN Technology
StyleGAN by Nvidia (Open Source) - GitHub » StyleGAN on GitHUB
- StyleGAN by Nvidia: Revolutionizing Generative AI
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What Photoshop Can't Do, DragGAN Can! See How! Paper Explained, Along with Additional Supplementary Video Footage
Not unless you are Nvidia Corporation: https://github.com/NVlabs/stylegan3/blob/main/LICENSE.txt
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How to load a StyleGAN3 PKL into PyTorch?
I found the follow script but unsure of how to utilize it in this context. https://github.com/NVlabs/stylegan3/blob/main/torch_utils/persistence.py
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Can't figure out how to link key pairs with one hot encoded binary tables in a Json file - Stylegan3/pytorch/python
full dataset.py file that I'm changing is here: https://github.com/NVlabs/stylegan3/blob/main/training/dataset.py
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Online Football Manager face generator
The neural network on which the training was carried out - https://github.com/NVlabs/stylegan3
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AI-generated Formidable portraits
I trained a Generative Adversarial Network stylegan2-ada model with NVIDIA’s StyleGAN3 algorithm with a RTX 3090 GPU for a few nights. This is an interpolation video of the generator model’s random-walk datapoints divided into 4 different windows. Initially I web crawled some 2000 images of any Formidable images and cropped them with nagadomi’s lbpcascade_animeface anime face detector, with a setting that I attempted to also include her assets in the image. Previously I have done by transfer-learning from Gwern’s ThisWaifuDoesNotExist, which only included heads of Emilia from Re:Zero, which was quite good. This time I wanted to see if the model can also handle having something more than just a head. Having Formidable’s chest also in the image made some angles perform pretty bad, as there are as many ways of making anatomy as there are artists. Because of this, I removed all swimsuit and party skin images, as making her features was hard enough with her default skin, making the final dataset size some 1500 images. In the end, I’m pretty satisfied with the results, but I could prune the dataset even more and crop the images more homogenously as well as try a bit different hyperparameters (most importantly gamma) and stylegan3-t. However, I want to move into trying out Stable Diffusion model, so I will wrap this project up at least for now and post this. There is a psi hyperparameter used in this video generation, that determines how “creative” the generator might be, i.e. how far from an optimal statistical distribution it can go at any given datapoint (video time in this case). With psi=0 you have almost static video, and with psi=1 wildly varying results of which half aren’t even recognizable, and some are really good. I settled for 0.65, which I think has some nice variety with a reasonable amount of bad morphs.
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Training StyleGAN3 on anime faces.
Refer to the training configurations to choose the correct values for your hardware, config and dataset. Reducing the --batch value typically warrants increasing --gamma and/or lowering the D and G learning rates (--dlr and --glr). Think of --gamma as the “randomness” or “creativity” of the model.
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I sure love chasing an ungodly fast VGT that has a 25 second head start on me
Heck to train StyleGAN, an AI image style transfer model, requires 1-8 GPU’s with a minimum of 12GB of VRAM to train. Using a single Tesla V100, which is about £6-10k, it still can take upwards of one minute to process 1000 256x256 images.
What are some alternatives?
pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
StyleGAN3-CLIP-notebooks - A collection of Jupyter notebooks to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.
BigGAN-PyTorch - The author's officially unofficial PyTorch BigGAN implementation.
StyleFlow - StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continuous Normalizing Flows (ACM TOG 2021)
stylegan3-encoder - stylegan3 encoder for image inversion
lucid-sonic-dreams
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
data-efficient-gans - [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
spleeter - Deezer source separation library including pretrained models.
stylegan3 - Official PyTorch implementation of StyleGAN3