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Stylegan Alternatives
Similar projects and alternatives to stylegan
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stylegan2-pytorch
Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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progressive_growing_of_gans
Progressive Growing of GANs for Improved Quality, Stability, and Variation
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better stylegan alternative or higher similarity.
stylegan reviews and mentions
Posts with mentions or reviews of stylegan.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-09.
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An AI artist isn't an artist
Been following generative AI since 2017 when nvidia released their first GAN paper & the results always fascinated me. Trained my own models with their repo then experimented with other open source projects. went thru the pain of assembling my own data set, tweaking code parameters to achieve what i'm looking for, had to deal with all kinds of hardware/software issues. I know it's not easy. (screenshot of a motorbike GAN model i was training in 2018 https://imgur.com/a/SIULFhR, was taken after 5 hours of training on a gtx 1080) or this, cinema camera output from another locally trained model. So yeah i have a couple ideas of how generative AI works. yup things were that bad few years ago, that technology has come a long way. Using & setting up something like stable diffusion with automatic1111 webui isn't really a complex process. Though generating AI art locally is always gonna feel more rewarding than using a cloud based service.
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Clearview AI scraped 30 billion images from Facebook and gave them to cops: it puts everyone into a 'perpetual police line-up'
Their algorithm is public, you could do it yourself if you have the proper hardware: https://github.com/NVlabs/stylegan
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StyleGAN-T Nvidia, 30x Faster than SD?
Umm, StyleGAN was the first decent image generation model, and it was producing great images from random seeds 5 years ago. Now, that's with the obvious caveat that each model was trained to produce one specific type of image and it helped immensely if the training images were all aligned the same. Diffusion models are certainly the trendy current architecture for image generation, but AFAIK there's no fundamental theoretical limitation to the output quality of any architecture except the general rule that more parameters is better.
- The Concept Art Association updates their AI-restricting gofundme campaign, revealing their lack of AI understanding & nefarious plans! [detailed breakdown]
- This was taken outdoors with no special lighting
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What the F**k
Jokes aside, ML moves extremely fast and our field is quickly advancing. The honest truth is that no researcher can even keep up other than their extremely niche corner. I'll show you an example. Here's what state of the art image generation looked like in 2014, 2018, and here is today (which now is highly controllable using text prompts instead of data prompts).
- Garfield
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Teaching AI to Generate New Pokemon
The fundamental technology we will use in this work is a generative adversarial network. Specifically, the Style GAN variant.
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A100 vs A6000 vs 3090 for computer vision and FP32/FP64
Based on my findings, we don't really need FP64 unless it's for certain medical applications. But The Best GPUs for Deep Learning in 2020 — An In-depth Analysis is suggesting A100 outperforms A6000 ~50% in DL. Also the Stylegan project GitHub - NVlabs/stylegan: StyleGAN - Official TensorFlow Implementation uses NVIDIA DGX-1 with 8 Tesla V100 16G(Fp32=15TFLOPS) to train dataset of high-res 1024*1024 images, I'm getting a bit uncertain if my specific tasks would require FP64 since my dataset is also high-res images. If not, can I assume A6000*5(total 120G) could provide similar results for StyleGan?
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[D] Which gpu should I choose?
Yes that's what I thought. But StyleGan https://github.com/NVlabs/stylegan uses NVIDIA DGX-1 with 8 Tesla V100 16G GPUs(FP32=15) to do the training, not sure if it's related to its high-res training images or something else.
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A note from our sponsor - InfluxDB
www.influxdata.com | 20 May 2024
Stats
Basic stylegan repo stats
31
13,962
0.0
about 1 month ago
NVlabs/stylegan is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of stylegan is Python.
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