pytorch-fid
DE-GAN
| pytorch-fid | DE-GAN | |
|---|---|---|
| 5 | 1 | |
| 3,065 | 151 | |
| - | - | |
| 5.9 | 0.0 | |
| about 1 month ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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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
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SSIM (Structural Similarity Index Metric )
Didn't get your problem statement but you can also consider FFID score to measure the similarity between two images. Check out: https://github.com/mseitzer/pytorch-fid
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[R] Maximum likelihood estimation can fail due to "Manifold Overfitting"
Author here, thanks for the comment! The example in 3.1 is not meant as a realistic example, it is meant as an intuitive explanation of how the manifold overfitting issue can arise. As for FID scores, we intend on releasing our code soon, but we used the standard PyTorch implementation (https://github.com/mseitzer/pytorch-fid).
DE-GAN
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Is there an AI that can un-grain text?
Looks like you could try DE-GAN https://github.com/dali92002/DE-GAN
What are some alternatives?
clean-fid - PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]
Ghost-DeblurGAN - This is a lightweight GAN developed for real-time deblurring. The model has a super tiny size and a rapid inference time. The motivation is to boost marker detection in robotic applications, however, you may use it for other applications definitely.
TryOnGAN - TryOnGAN: Unofficial Implementation
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch
torch-fidelity - High-fidelity performance metrics for generative models in PyTorch
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
mugl
ALAE - [CVPR2020] Adversarial Latent Autoencoders
stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation
TextZoom - A super-resolution dataset of paired LR-HR scene text images