stylegan2-ada-pytorch
stylegan2-pytorch
stylegan2-ada-pytorch | stylegan2-pytorch | |
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30 | 1,989 | |
3,917 | 3,646 | |
0.9% | - | |
2.3 | 2.7 | |
4 months ago | 8 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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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.
stylegan2-pytorch
- Wikipedia No Longer Considers CNET "Generally Reliable" Source After AI Scandal
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Discord Clone Using Next.js and Tailwind - Part 3: Channel List
export default function ChannelListBottomBar(): JSX.Element { const { client } = useChatContext(); const [micActive, setMicActive] = useState(false); const [audioActive, setAudioActive] = useState(false); return (
{client.user?.image && (div> )}setMicActive((currentValue) => !currentValue)} > button> setAudioActive((currentValue) => !currentValue)} > button> button> div> ); }{client.user?.name} span> {client.user?.online ? 'Online' : 'Offline'} span> p> button>
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Realism Engine SDXL v2.0 just released
I wonder if we will ever get a realism model which can produce normal faces like https://thispersondoesnotexist.com instead of like super symmetrical faces of models
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Spongebob!!!
this has been in circulation since a while AI images took off, and they certainly weren't convincing before they did. You know the old "try to name one thing in this image" macro? Pretty sure that was AI generated, there was also thispersondoesnotexist.com which was always pretty good but of course it is
- 🤣🤣🤣
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Many AI images have are photorealistic, but have a strangely empty and ''soulless'' expression. Something is wrong, but it's hard to say what
Not the workflow for these images, but if you easily want to spice up your gens with a bit more natural look, try using images from thispersondoesnotexist.com with IPAdapter face model.
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‘Nudify’ Apps That Use AI to ‘Undress’ Women in Photos Are Soaring in Popularity
...then they just use the app on generated images of not real people (potentially based on specific inputs to remind you of a real person).
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Quan Chi is from Massachusetts
Also we can generate very realistic faces with AI, https://thispersondoesnotexist.com/ (old example), fully 3D faces are doable at this point. So in another 5-10 years a low profile model like him wouldn't even be hired for this, they would just generate a digital face model. Unionizing will only speed up studios adoption of digital replacements.
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Sketchy Youtube comments talking about Hostinger
It looks like they all use a profile picture made with https://thispersondoesnotexist.com/
- Lorem picsum but for avatars?
What are some alternatives?
stylegan3 - Official PyTorch implementation of StyleGAN3
DeepFaceLab - DeepFaceLab is the leading software for creating deepfakes.
pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
awesome-pretrained-stylegan2 - A collection of pre-trained StyleGAN 2 models to download
BigGAN-PyTorch - The author's officially unofficial PyTorch BigGAN implementation.
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
StyleFlow - StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continuous Normalizing Flows (ACM TOG 2021)
stylegan2-ada - StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation
lucid-sonic-dreams
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
data-efficient-gans - [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
dalle-mini - DALL·E Mini - Generate images from a text prompt