pixel2style2pixel
stylegan3
pixel2style2pixel | stylegan3 | |
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16 | 38 | |
3,107 | 6,176 | |
- | 0.8% | |
0.0 | 1.1 | |
over 1 year ago | 8 months ago | |
Jupyter Notebook | Python | |
MIT License | GNU General Public License v3.0 or later |
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pixel2style2pixel
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The one time it creates legible text
I wouldn't describe it like that. Consider a simpler example. StyleGAN can make plausible looking face that doesn't look like any of the individual faces it was trained on. It's not making a face collage out of this guy's chin pixels and that guy's eyebrows pixels. There's an easy way to test this: give it a photo of yourself or someone you know with something like pixel2style2pixel and it will probably give you back something convincing. But you weren't in the training. What it's actually doing is interpolating between plausible facial features in a space that it's laid for what human being could conceivably look like.
- stylegan3 encoder for image inversion
- desculpa bapo. mas nao fui eu, foi uma IA!!
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Am i the only one who thinks this lil guy looks alot like michele reves?
i think its this one https://github.com/eladrich/pixel2style2pixel
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I used AI to generate real life for honor character faces
What did you use to generate this? Was it https://github.com/eladrich/pixel2style2pixel or something else? Curious
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[R] a Metric for finding the best StyleGAN Latent Encoders
Right now we have encoders like pSp and restyle or encoder4editing, but how can we tell which one performs better than the other?
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[OC] This NPC Does Not Exist: I created an AI to generate NPC portraits
The portaitify tool uses pixel2style2pixel to invert a picture into a 'style vector' then generate the corresponding image with the stylegan 2 generator. Happy to a higher or lower level description if that's of interest!
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Should i start with Windows or Linux environment for ML?
Hi, recently I started playing with ML in python (anaconda in Windows 10), using relevant packages for tensorflow, torch and cuda and running some models. I would like to play with shared projects like the ones in https://paperswithcode.com/, like this one: https://github.com/eladrich/pixel2style2pixel, but many requiere Linux.
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How do I get a GAN to write a dubstep drop?
I did something like this. Many image GAN papers have implementations on GitHub, just pick the model you want. State-of-the-art image translation is probably something like Pixel2Style2Pixel (https://github.com/eladrich/pixel2style2pixel). Note that there are also wave GANs and they have slightly(?) better audio on average. With image models, typically people input mel spectrograms, which discard the phase information (you could also input 2 channel images for the real and complex parts, but I haven't seen any projects that do that). `librosa` has functions for the Fourier transform and its inverse (Griffin Lim algorithm), but if you want high quality reconstructions try using a neural network solution like WaveGlow to do the inverse conversion (if you're training a GAN, you can fine-tune WaveGlow). The biggest bottleneck is data - get as much data as possible. Also check out /r/machinelearning.
stylegan3
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StyleGAN3 NvidiaLabs - The state-of-the-art in Artificial Intelligence applied to Human Face Generation.
StyleGAN3 by Nvidia Open Source Software »
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AI's Triumph: Lifelike Human Faces through GAN Technology
StyleGAN by Nvidia (Open Source) - GitHub » StyleGAN on GitHUB
- StyleGAN by Nvidia: Revolutionizing Generative AI
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What Photoshop Can't Do, DragGAN Can! See How! Paper Explained, Along with Additional Supplementary Video Footage
Not unless you are Nvidia Corporation: https://github.com/NVlabs/stylegan3/blob/main/LICENSE.txt
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How to load a StyleGAN3 PKL into PyTorch?
I found the follow script but unsure of how to utilize it in this context. https://github.com/NVlabs/stylegan3/blob/main/torch_utils/persistence.py
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Can't figure out how to link key pairs with one hot encoded binary tables in a Json file - Stylegan3/pytorch/python
full dataset.py file that I'm changing is here: https://github.com/NVlabs/stylegan3/blob/main/training/dataset.py
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Online Football Manager face generator
The neural network on which the training was carried out - https://github.com/NVlabs/stylegan3
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AI-generated Formidable portraits
I trained a Generative Adversarial Network stylegan2-ada model with NVIDIA’s StyleGAN3 algorithm with a RTX 3090 GPU for a few nights. This is an interpolation video of the generator model’s random-walk datapoints divided into 4 different windows. Initially I web crawled some 2000 images of any Formidable images and cropped them with nagadomi’s lbpcascade_animeface anime face detector, with a setting that I attempted to also include her assets in the image. Previously I have done by transfer-learning from Gwern’s ThisWaifuDoesNotExist, which only included heads of Emilia from Re:Zero, which was quite good. This time I wanted to see if the model can also handle having something more than just a head. Having Formidable’s chest also in the image made some angles perform pretty bad, as there are as many ways of making anatomy as there are artists. Because of this, I removed all swimsuit and party skin images, as making her features was hard enough with her default skin, making the final dataset size some 1500 images. In the end, I’m pretty satisfied with the results, but I could prune the dataset even more and crop the images more homogenously as well as try a bit different hyperparameters (most importantly gamma) and stylegan3-t. However, I want to move into trying out Stable Diffusion model, so I will wrap this project up at least for now and post this. There is a psi hyperparameter used in this video generation, that determines how “creative” the generator might be, i.e. how far from an optimal statistical distribution it can go at any given datapoint (video time in this case). With psi=0 you have almost static video, and with psi=1 wildly varying results of which half aren’t even recognizable, and some are really good. I settled for 0.65, which I think has some nice variety with a reasonable amount of bad morphs.
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Training StyleGAN3 on anime faces.
Refer to the training configurations to choose the correct values for your hardware, config and dataset. Reducing the --batch value typically warrants increasing --gamma and/or lowering the D and G learning rates (--dlr and --glr). Think of --gamma as the “randomness” or “creativity” of the model.
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I sure love chasing an ungodly fast VGT that has a 25 second head start on me
Heck to train StyleGAN, an AI image style transfer model, requires 1-8 GPU’s with a minimum of 12GB of VRAM to train. Using a single Tesla V100, which is about £6-10k, it still can take upwards of one minute to process 1000 256x256 images.
What are some alternatives?
stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation
encoder4editing - Official implementation of "Designing an Encoder for StyleGAN Image Manipulation" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02766
StyleGAN3-CLIP-notebooks - A collection of Jupyter notebooks to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.
stylegan3-editing - Official Implementation of "Third Time's the Charm? Image and Video Editing with StyleGAN3" (AIM ECCVW 2022) https://arxiv.org/abs/2201.13433
stylegan3-encoder - stylegan3 encoder for image inversion
ganspace - Discovering Interpretable GAN Controls [NeurIPS 2020]
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Deep-Learning - In-depth tutorials on deep learning. The first one is about image colorization using GANs (Generative Adversarial Nets).
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
restyle-encoder - Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" (ICCV 2021) https://arxiv.org/abs/2104.02699
stylegan3 - Official PyTorch implementation of StyleGAN3