lucid-sonic-dreams
stylegan
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lucid-sonic-dreams | stylegan | |
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14 | 31 | |
772 | 13,933 | |
- | 0.5% | |
0.0 | 0.0 | |
over 2 years ago | 14 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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lucid-sonic-dreams
- Deep Music Visualizer + Stable WarpFusion
- Lucid Sonic Dreams
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I wrote an opensource software that shows audio reactive visual effects generated by StyleGAN2 (links to github and more information under video description)
If by "made" you mean stole from https://github.com/mikaelalafriz/lucid-sonic-dreams without attribution an packaged it into an executable without attribution while claiming its "opensource" (its not), then yes
- I wrote some code that can play audio reactive visual loops generated by StyleGAN2 in real time.
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Hei jeg er autistisk og har brukt 10 år av livet mitt på å skape en kolleksjon på 3 album og 33 sanger. Disse er alle helt ny sanger aldri av noen. Sangene ble laget i 2012 som skaper unike stiler basert på min personlighet den tiden. To av album coverene har jeg tegnet selv, gjett hvilke :P
Takk! Brukte dette biblioteket med egne presets. Må advare om at du trenger kraftig GPU https://github.com/mikaelalafriz/lucid-sonic-dreams
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Lucid Sonic Dreams GAN (Abstract Photo Set)
Here's the Github link to the lib.
- Edge Feedback on Face Morph GAN.
- Beach Baby as seen by an AI trained on Impressionist Paintings
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Stylegan2-ada x lucid sonic dreams x animal eyes
This video was created with following repository’s stylegan2-ada-pytorch, lucid-sonic-dreams and spleeter
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Fiz um vídeo de música generativa com Barões da Pisadinha lo-fi usando um pacote que sincroniza a música com a saída de uma rede neural generativa adversarial
O pacote usado chama Lucid Sonic Dreams: https://github.com/mikaelalafriz/lucid-sonic-dreams
stylegan
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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.
What are some alternatives?
jukebox - Code for the paper "Jukebox: A Generative Model for Music"
pix2pix - Image-to-image translation with conditional adversarial nets
stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation
stylegan2 - StyleGAN2 - Official TensorFlow Implementation
DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)
spleeter - Deezer source separation library including pretrained models.
aphantasia - CLIP + FFT/DWT/RGB = text to image/video
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
ffhq-dataset - Flickr-Faces-HQ Dataset (FFHQ)
v_machine - Visual Loop Machine that plays MTD (Multiple Temporal Dimension) videos based on audio input.
awesome-pretrained-stylegan2 - A collection of pre-trained StyleGAN 2 models to download