data-efficient-gans
generative_inpainting
| data-efficient-gans | generative_inpainting | |
|---|---|---|
| 9 | 2 | |
| 1,258 | 3,155 | |
| 0.2% | - | |
| 0.0 | 0.0 | |
| 6 months ago | over 2 years ago | |
| Python | Python | |
| BSD 2-clause "Simplified" License | GNU General Public License v3.0 or later |
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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.
generative_inpainting
- after instantiating a graph, data or node needs to pass before it loads the parameters?
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how to make older tensorflow work properly with eager execution?
tried to run https://github.com/JiahuiYu/generative_inpainting but code was coded in tensorflow 1.x
What are some alternatives?
stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
stable-diffusion-docker - Run the official Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint.
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
Fast-SRGAN - A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
Put-In-Context - Putting Visual Object Recognition in Context
SDEdit - PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
gansformer - Generative Adversarial Transformers
cartoonize - A demo webapp to convert images and videos into cartoon!
YOLO_Object_Detection - This is the code for "YOLO Object Detection" by Siraj Raval on Youtube
DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2
ESRGAN - ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.