data-efficient-gans
ESRGAN
data-efficient-gans | ESRGAN | |
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9 | 21 | |
1,258 | 5,707 | |
0.2% | - | |
0.0 | 0.0 | |
6 months ago | over 1 year ago | |
Python | Python | |
BSD 2-clause "Simplified" License | Apache License 2.0 |
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data-efficient-gans
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[D] Has anyone tried GAN "tricks" on VAEs?
Code for https://arxiv.org/abs/2006.10738 found: https://github.com/mit-han-lab/data-efficient-gans
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What StyleGan model to use for a custom dataset of small size?
I would like to make a tiny project with GANs using some high quality pictures of a single individual. I am planning to get around 500 of these and then x-flip them, however I am not sure what model I should consider for the training. I have used StyleGan2 ADA for another project which ended quite well, but I had around 14k pictures, here now the training size is much smaller and I was therefore thinking about using DiffAugment which has seemingly promising results with just 100 images.
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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] Differentiable augmentation for GANs - Implementation and explanation
Paper: https://arxiv.org/abs/2006.10738
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Deepspeed x Stylegan?
There are some repos which I've looked at to add deepspeed to such as DiffAugment-stylegan2-pytorch, lucidrains/stylegan2-pytorch and eps696/stylegan2 (which is in tensorflow so it would need to be translated to pytorch as deepspeed only works with pytorch right now).
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Model takes seconds to train per epoch with 1 accuracy
Here is the paper using GANs with few data points https://arxiv.org/abs/2006.10738
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Looking for resources regarding GANs trained on my own stuff.
Hey, for image gans, you can use smooth data aumentation https://github.com/mit-han-lab/data-efficient-gans in case you have a reasonable sized dataset.
ESRGAN
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Upscayl - Free and Open Source AI Image Upscaler for Linux, Mac and Windows
This seems to be based on ESRGAN which is supposed to be higher quality than waifu2x.
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How can I change the parameters of ESRGAN
I downloaded https://github.com/xinntao/ESRGAN , I run it using command line, no gui.
- Finetuning a x2 Real-ESRGAN model?
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The Golden Gator re-imagined by AI (Read comment before watching on stream)
Nerd info: Uses 3 different algorithms together: https://github.com/CompVis/stable-diffusion For generating source https://github.com/TencentARC/GFPGAN For repairing faces, mouths, eyes https://github.com/xinntao/ESRGAN For upscaling Paint.Net for manual retouching, cleanup and adjustments.
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[D] Has anyone tried GAN "tricks" on VAEs?
Code for https://arxiv.org/abs/1809.00219 found: https://github.com/xinntao/ESRGAN
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How to convert a low resolution, pixelated image into a high resolution picture 💫
Even if it's not a simple black and white image, you can get damn impressive results when upscaling images with GAN-based algorithms, like ESRGAN, together with a suitable pretrained model. Both are free of charge. The software can be tricky to use compared to Photoshop, though.
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Ambriel Motion Graphics Experiment
I use ESRGAN: https://github.com/xinntao/ESRGAN
- Some architectural stuff I’ve been working on.
- children playing in the rain at night illuminated by lanterns, reflective puddles, fine detail, by Leonid Afremov
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Paper: "A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminators", Wei et al 2021. "Main idea: Introduce attention U-net into the field of blind real world image super resolution. We aims to provide a super resolution method with sharper result and less distortion."
There are two Generator architectures that are in the code, however these are actually just sitting there unused, likely from work that did not pan out because if you look at their inference code it's not used at all. Instead they directly import the plain old vanilla RRDB architecture from BasicSR yet again another Xinntao repository. Seeing the theme here.
What are some alternatives?
stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
stable-diffusion-docker - Run the official Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint.
realsr-ncnn-vulkan - RealSR super resolution implemented with ncnn library
Fast-SRGAN - A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
Waifu2x-Extension-GUI - Video, Image and GIF upscale/enlarge(Super-Resolution) and Video frame interpolation. Achieved with Waifu2x, Real-ESRGAN, Real-CUGAN, RTX Video Super Resolution VSR, SRMD, RealSR, Anime4K, RIFE, IFRNet, CAIN, DAIN, and ACNet.
SDEdit - PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
video2x - A lossless video/GIF/image upscaler achieved with waifu2x, Anime4K, SRMD and RealSR. Started in Hack the Valley II, 2018.
gansformer - Generative Adversarial Transformers
waifu2x-ncnn-vulkan - waifu2x converter ncnn version, runs fast on intel / amd / nvidia / apple-silicon GPU with vulkan
generative_inpainting - DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration