pytorch-fid
stylegan2-ada-pytorch
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pytorch-fid | stylegan2-ada-pytorch | |
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5 | 30 | |
2,976 | 3,882 | |
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5.9 | 2.3 | |
10 days ago | 3 months ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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pytorch-fid
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[D] A better way to compute the Fréchet Inception Distance (FID)
The Fréchet Inception Distance (FID) is a widespread metric to assess the quality of the distribution of a image generative model (GAN, Stable Diffusion, etc.). The metric is not trivial to implement as one needs to compute the trace of the square root of a matrix. In all PyTorch repositories I have seen that implement the FID (https://github.com/mseitzer/pytorch-fid, https://github.com/GaParmar/clean-fid, https://github.com/toshas/torch-fidelity, ...), the authors rely on SciPy's sqrtm to compute the square root of the matrix, which is unstable and slow.
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[D] Are there any good FID and KID metrics implementations existing that are compatible with pytorch?
https://github.com/GaParmar/clean-fid/ is my goto. https://github.com/mseitzer/pytorch-fid isn't bad either.
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[P] Frechet Inception Distance
https://github.com/mseitzer/pytorch-fid for example this here. The code is quite clean and clear
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...
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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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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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This Bot Crime Did Not Occur
I used a modified version of this repo, and there's also the official NVIDIA implementation, though neither have official notebooks. You can Google 'StyleGAN2 ADA Colab' and find a few starting points that way, but wait a few hours and I can clean up my notebook and post it here!
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[P] Suggest a Conditional GAN for a project?
Consider this repo: https://github.com/NVlabs/stylegan2-ada-pytorch. It is quite well documented and has conditions built-in. I have worked with this code recently and it is easy to make your own modifications, so if you don’t shy away from doing some minor work yourself, I imagine you could make quantitative conditions work with a few changes to the input of the mapping network.
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[OC] This NPC Does Not Exist: I created an AI to generate NPC portraits
Both tools rely on a stylegan2 encoder which was finetuned using a set of drawn portraits I've been collecting for some time.
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[D] What is the smallest dataset you styleGAN2 trained?
Authors of the StyleGAN2-ada already try many things in this paper, I suggest you check it out: https://arxiv.org/abs/2006.06676. You can just check sections 4.2 and 4.3.
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Stylegan2-ada x lucid sonic dreams x animal eyes
This video was created with following repository’s stylegan2-ada-pytorch, lucid-sonic-dreams and spleeter
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So here is what an AI thinks Naruto would look like in real life
I do in the description of the video! But to make your life easier: StyleGAN2-Ada source code: https://github.com/NVlabs/stylegan2-ada-pytorch Pixel2Style2Pixel source code: https://github.com/eladrich/pixel2style2pixel
What are some alternatives?
stylegan3 - Official PyTorch implementation of StyleGAN3
pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
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)
clean-fid - PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]
lucid-sonic-dreams
data-efficient-gans - [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
spleeter - Deezer source separation library including pretrained models.
TryOnGAN - TryOnGAN: Unofficial Implementation
ContraD - Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)
torch-fidelity - High-fidelity performance metrics for generative models in PyTorch
mugl