Fast-SRGAN
data-efficient-gans
| Fast-SRGAN | data-efficient-gans | |
|---|---|---|
| - | 9 | |
| 642 | 1,260 | |
| - | 0.3% | |
| 0.0 | 0.0 | |
| 2 months ago | 6 months ago | |
| Python | Python | |
| MIT License | BSD 2-clause "Simplified" License |
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Fast-SRGAN
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Tracking mentions began in Dec 2020.
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.
What are some alternatives?
a-PyTorch-Tutorial-to-Super-Resolution - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution
stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation
awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments
stable-diffusion-docker - Run the official Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint.
iSeeBetter - iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
SDEdit - PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
super-resolution - Tensorflow 2.x based implementation of EDSR, WDSR and SRGAN for single image super-resolution
gansformer - Generative Adversarial Transformers
CleanTF2plus - Clean TF2's sequel
generative_inpainting - DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral
EDSR_Tensorflow - TensorFlow implementation of 'Enhanced Deep Residual Networks for Single Image Super-Resolution'.
YOLO_Object_Detection - This is the code for "YOLO Object Detection" by Siraj Raval on Youtube