lucid-sonic-dreams
stylegan2
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lucid-sonic-dreams | stylegan2 | |
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14 | 40 | |
772 | 10,753 | |
- | 0.2% | |
0.0 | 0.0 | |
over 2 years ago | about 1 year 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
stylegan2
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Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
I don't know. If you're really curious, you can just try it: https://github.com/NVlabs/stylegan2
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Used thispersondoesnotexist.com, then expanded it with DALL-E
StyleGAN2 (Dec 2019) - Karras et al. and Nvidia
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Show HN: Food Does Not Exist
> The denoising part of a denoising autoencoder refers to the noise applied to its input
Agree, it converts a noisy image to a denoised image. But the odd thing is, when you put a noisy image into a StyleGAN2 encoder, you get latents which the decoder will turn into a de-noised image. So in practical use, you can take a trained StyleGAN2 encoder/decoder pair and use it as if it was a denoiser.
> These differences lead to learned distributions in the latent space that are entirely different
I also agree there. The training for a denoising auto-encoder and for a GAN network is different, leading to different distributions which are sampled for generating the images. But the architecture is still very similar, meaning the limits of what can be learned should be the same.
> Beyond that the comparison just doesn't work, yes there are two networks but the discriminator doesn't play the role of the AE's encoder at all
Yes, the discriminator in a GAN won't work like an encoder. But if you look at how StyleGAN 1/2 are used in practice, people combine it with a so-called "projection", which is effectively an encoder to convert images to latents. So people use a pipeline of "image to latent encoder" + "latent to image decoder".
That whole pipeline is very similar to an auto-encoder. For example, here's an NVIDIA paper about how they round-trip from image to latent to image with StyleGAN: https://arxiv.org/abs/1912.04958 My interpretation of what they did in that paper is that they effectively trained a StyleGAN-like model with the image L2 loss typically used for training a denoising auto-encoder.
- "Why yes I totally believe the 'Xinjiang Police Files', they got photos of REAL (100% not AI generated) detainees!"
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How did they code Viola AI (face to cartoon)
These problems are usually done with CNN Encoder-Decoder frameworks. Usually GAN (Generative Adversarial Networks see StyleGan2).
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AI morphs many faces together to all sing Scatman
This is the result of two different models. The first looks like a latent space interpolation of StyleGan2 and the mouth movements are without a doubt from wav2lip.
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What A.I. tool is this?
OP: if you want to run this at higher resolution, you should probably look at running it yourself, using something like this: https://github.com/NVlabs/stylegan2
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Imagined ML model deployment on normal machine, is it possible?
StyleGAN2 (Dec 2019) - Karras et al. and Nvidia
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I'm implementing StyleGAN2 with Keras. I was worried it wasn't working, but after some 300K training steps it's finally starting to converge. (+ plot of what the first (4x4) part looks like)
A few of you might've seen an earlier post of mine about this project (Or the repost that got more upvotes 🙃), and I've improved the code and network since then after more thoroughly reading and understanding the official StyleGAN2 implementation.
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Is it just me or has Google Colab Pro become a lot more restrictive lately?
So I've been a Pro+ subscriber since around November which I mainly use to train GANs. I have multiple Google accounts, let's call them Account 1, 2, and 3. Accounts 1 and 2 are normal Google accounts and Account 3 is an account I got from my university after I graduated which has unlimited storage.
What are some alternatives?
jukebox - Code for the paper "Jukebox: A Generative Model for Music"
Wav2Lip - This repository contains the codes of "A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild", published at ACM Multimedia 2020. For HD commercial model, please try out Sync Labs
stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation
stylegan - StyleGAN - Official TensorFlow Implementation
pix2pix - Image-to-image translation with conditional adversarial nets
spleeter - Deezer source separation library including pretrained models.
stylegan2-ada - StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
v_machine - Visual Loop Machine that plays MTD (Multiple Temporal Dimension) videos based on audio input.
lightweight-gan - Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two