stylegan
aphantasia
Our great sponsors
stylegan | aphantasia | |
---|---|---|
31 | 21 | |
13,933 | 768 | |
0.5% | - | |
0.0 | 3.9 | |
14 days ago | 6 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
stylegan
-
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.
-
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
-
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
-
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
-
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.
-
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?
-
[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.
aphantasia
- An AI written, AI illustrated, human performed audio drama: Asteroid Annie and the Mushiblooms, Part 1 (Uncanny Robot Podcast)
- An audio drama written with NovelAI: Asteroid Annie and the Mushiblooms, Part 1
-
DeadSeanKennedy - Black Sheep Supreme [Breakbeat Techno House Electro Indie] [2022]
A new music video I made off of my latest release "Junglehaus" I used the Aphantasia library from eps696 (https://github.com/eps696/aphantasia) by feeding it the lyrics from the song and then editing together the best generations.
-
test
(Added Mar. 1, 2021) Aphantasia.ipynb - Colaboratory by eps696. Uses FFT (Fast Fourier Transform) from Lucent/Lucid to generate images. GitHub. Twitter reference. Example #1. Example #2.
- Batch render different prompts
-
Saw u/R_is_Ris post and inspired me to post my own. I call it Glow Forest for obvious reasons
Made with Illustrip by Vadim Epstein (https://github.com/eps696/aphantasia) and FL Studio for the background ambience
- Feeding in Politics: It Did Not Go As Planned
-
AI - A love story // AI-generated video about the future of AI // prompt -> GPT-J-6B -> Aphantasia
GPT-J - from the wizards at Eleuther.ai, via HuggingFace. - https://huggingface.co/EleutherAI/gpt-j-6B Aphantasia - from vadim epstein (eps696) - https://github.com/eps696/aphantasia
- Mario's Power-up (created with Aphantasia)
-
I heard a bird sing in the dark of December. A magical thing.
Over the weekend i've been toying around with the amazing Aphantasia, using quotes about the months of the year as prompts, this is definitely my favorite of the whole set.
What are some alternatives?
pix2pix - Image-to-image translation with conditional adversarial nets
DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)
stylegan2 - StyleGAN2 - Official TensorFlow Implementation
big-sleep - A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Technique was originally created by https://twitter.com/advadnoun
lucid-sonic-dreams
Colab-BigGANxCLIP
Queryable - Run OpenAI's CLIP model on iOS to search photos.
ffhq-dataset - Flickr-Faces-HQ Dataset (FFHQ)
StyleCLIP - Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral)
awesome-pretrained-stylegan2 - A collection of pre-trained StyleGAN 2 models to download
sentencepiece - Unsupervised text tokenizer for Neural Network-based text generation.