ALAE
lightweight-gan
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| ALAE | lightweight-gan | |
|---|---|---|
| 1 | 10 | |
| 3,492 | 1,599 | |
| - | - | |
| 0.0 | 3.2 | |
| about 3 years ago | over 1 year ago | |
| Python | Python | |
| - | MIT License |
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ALAE
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[D] How do I make a model which takes a bedroom image as input give an output of different design of bedroom related to input image?
source link: https://github.com/podgorskiy/ALAE
lightweight-gan
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[D] My embarrassing trouble with inverting a GAN generator. Do GAN questions still get answered? ;-)
GAN details: I trained using the code from https://github.com/lucidrains/lightweight-gan, image size is 256, attn-res-layers is [32,64], disc_output_size is 5 and I trained with AMP.
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I trained AI to generate OMORI image
Long short story, i trained AI called 'Lightweight' GAN with some image from the game (Steam version). The result isn't satisfying (either too similar with original image or distorted), but i decided to share it since it took 3-4 days.
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Imagined ML model deployment on normal machine, is it possible?
Code for training your own [original] [simple] [light]
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[P] - These Nebulae Do Not Exist
Trained lightweight-gan on public domain nebulae images. The blogpost covers how it was assembled end-to-end, from data scraping, to model training, and finally deploying it as a web app using free cloud services only
- Questi politici italiani non esistono
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It begins! [Sixth World Music Comp Update]
https://github.com/lucidrains/stylegan2-pytorch is the simplest implementation I know of, and https://github.com/lucidrains/lightweight-gan is similar but designed for relatively lower performance machines.
- Bow Generation Using Lightweight GAN (In Progress)
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GAN-generated Minecraft skins
I also regret using https://github.com/lucidrains/lightweight-gan over the standard StyleGAN2. The training time wasn't an issue for 128x128 images and I should have gone for the higher quality model.
- GAN-generated "Now That's What I Call Music!" CDs
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GAN-generated "Now That's What I Call Music!" covers
It's hard to tell if the medicore results are due to the small dataset (~305 semi-aligned images, yes there's that many of these) or my own inexperience in GAN generation (i used https://github.com/lucidrains/lightweight-gan and the only changes i made from the defaults were to decrease the batch size and add color augmentation)
What are some alternatives?
jukebox - Code for the paper "Jukebox: A Generative Model for Music"
anycost-gan - [CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch
HyperGAN - Composable GAN framework with api and user interface
dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).
stylegan2 - StyleGAN2 - Official TensorFlow Implementation
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
pi-GAN-pytorch - Implementation of π-GAN, for 3d-aware image synthesis, in Pytorch
denoising-diffusion-pytorch - Implementation of Denoising Diffusion Probabilistic Model in Pytorch
gigagan-pytorch - Implementation of GigaGAN, new SOTA GAN out of Adobe. Culmination of nearly a decade of research into GANs
checkface - Putting a face to a hash
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement