stylegan
pix2pix
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stylegan | pix2pix | |
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31 | 13 | |
13,924 | 9,859 | |
0.4% | - | |
0.0 | 0.0 | |
10 days ago | almost 3 years ago | |
Python | Lua | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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.
pix2pix
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Any work on Style transfer using Stable Diffusion based on image-mask pairs similar to Pix2Pix
I have previously worked on retraining Pix2Pix GAN for image-to-image style transfer retrained with image-mask pairs. I expect Stable Diffusion to be better than Pix2Pix, but the problem sounds like something that should have been tackled already. I am familiar with text-based instructions for style transfer using SD (Instruct Pix2Pix), but retraining with image-mask pairs should provide better results. Does anyone know if anything that like exists already? Reference for Pix2Pix https://phillipi.github.io/pix2pix/
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Hello nooby programmer here but i do 3d art and i have few questions
Additionally, there are some open source AI models for texture generation, such as pix2pix: https://phillipi.github.io/pix2pix/.
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Predict Fourier spectrum
Sounds fun. Also sounds like image-to-image translation in 1D? Pix2pix is a famous implementation that uses UNet + adversarial loss: https://github.com/phillipi/pix2pix
- Things I Have Drawn is a site in which the things kids draw are real
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Is it possible to remove a sticker from a photo?
I guess everything is rasterized correctly. While u/Kelaifu is correct: there are no softwares that can guess your face where it's missing, right now there are machine learning techniques and implementations (see: https://phillipi.github.io/pix2pix/) that can go pretty far with guesses.
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Explore the water-logged city of Ys!
[Image generated using Machine Learning algorithm pix2pix trained by me using this dataset: CMP Facade Dataset]
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[D] Geo DeepFakes are not far away
pix2pix can already transform images between satellite and maps (https://phillipi.github.io/pix2pix/). Will there be any difference if I use pix2pix to transform satellite images to maps and back?
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Help with GAN Pix2Pix Code!
# Resize all images in a folder by 1/2 # modified from https://github.com/phillipi/pix2pix/blob/master/scripts/combine_A_and_B.py # resize_A_to_C.py from pdb import set_trace as st import os import numpy as np import cv2 import argparse parser = argparse.ArgumentParser("resize images by 1/2") parser.add_argument( "--fold_A", dest="fold_A", help="input directory for original images", type=str, default="./test_A", ) parser.add_argument( "--fold_C", dest="fold_C", help="output directory", type=str, default="./test_C" ) args = parser.parse_args() for arg in vars(args): print("[%s] = " % arg, getattr(args, arg)) img_list = os.listdir(args.fold_A) if not os.path.isdir(args.fold_C): os.makedirs(args.fold_C) for name_A in img_list: path_A = os.path.join(args.fold_A, name_A) if os.path.isfile(path_A) and not name_A.startswith( "." ): # skip . and .. and folders im_A = cv2.imread(path_A, cv2.IMREAD_COLOR) # Example: scale_down by factor 1/2 both dimensions scale_down = 0.5 im_C = cv2.resize( im_A, None, fx=scale_down, fy=scale_down, interpolation=cv2.INTER_LINEAR ) # store resized image with same name as in folder A path_C = os.path.join(args.fold_C, name_A) cv2.imwrite(path_C, im_C)
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cGAN (img2img) Translation Applied to 3D Scene Post-Editing
This project uses the pix2pix Image translation architecture (https://phillipi.github.io/pix2pix/) for 3D image post-processing. The goal was to test the 3D applications of this type of architecture.
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trained the model based on dark art sketches. got such bizarre forms of life
It seems like this even would make for a cool website, like pix2pix
What are some alternatives?
stylegan2 - StyleGAN2 - Official TensorFlow Implementation
CycleGAN - Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
lucid-sonic-dreams
DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)
naver-webtoon-faces - Generative models on NAVER Webtoon faces
aphantasia - CLIP + FFT/DWT/RGB = text to image/video
awesome-image-translation - A collection of awesome resources image-to-image translation.
ffhq-dataset - Flickr-Faces-HQ Dataset (FFHQ)
art-DCGAN - Modified implementation of DCGAN focused on generative art. Includes pre-trained models for landscapes, nude-portraits, and others.
awesome-pretrained-stylegan2 - A collection of pre-trained StyleGAN 2 models to download
perspective-change - cGAN Based 3D Scene Re-Compositing