data-efficient-gans
gansformer
| data-efficient-gans | gansformer | |
|---|---|---|
| 9 | 7 | |
| 1,258 | 1,302 | |
| 0.2% | - | |
| 0.0 | 1.8 | |
| 6 months ago | almost 2 years ago | |
| Python | Python | |
| BSD 2-clause "Simplified" License | MIT License |
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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.
gansformer
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[D] GANs + Transformer = SOTA compositional generator? Compositional Transformers for Scene Generation explained (5-minute summary by Casual GAN Papers)
Code for https://arxiv.org/abs/2111.08960 found: https://github.com/dorarad/gansformer
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Generative Adversarial Transformers [R]
As for whether the Ys are shared across layers, check the code.
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[Project] These players does not exist
I tested the gansformer (https://github.com/dorarad/gansformer) to generate football player faces. Here are some selected results (actually some of the images are real players):
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GANsformers: Scene Generation with Generative Adversarial Transformers š„
References: Paperāŗ: https://arxiv.org/pdf/2103.01209.pdf Codeāŗ: https://github.com/dorarad/gansformer Complete referenceāŗ: Drew A. Hudson and C. Lawrence Zitnick, Generative Adversarial Transformers, (2021), Published on Arxiv.
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[R] Generative Adversarial Transformers (2103.01209)
https://github.com/dorarad/gansformer/blob/148f72964219f8ead2621204bc5cfa89200b6879/training/network.py#L461
What are some alternatives?
stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation
pytorch-generative - Easy generative modeling in PyTorch.
stable-diffusion-docker - Run the official Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint.
SteganoGAN - SteganoGAN is a tool for creating steganographic images using adversarial training.
Fast-SRGAN - A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - [ECCV 2022] Compositional Generation using Diffusion Models
SDEdit - PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
long-range-arena - Long Range Arena for Benchmarking Efficient Transformers
generative_inpainting - DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral
gnn-lspe - Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
cartoonize - A demo webapp to convert images and videos into cartoon!
icl-ceil - [ICML 2023] Code for our paper āCompositional Exemplars for In-context Learningā.