ArtGAN
pytorch-CycleGAN-and-pix2pix
| ArtGAN | pytorch-CycleGAN-and-pix2pix | |
|---|---|---|
| 1 | 10 | |
| 401 | 22,029 | |
| - | - | |
| 3.6 | 2.5 | |
| 8 months ago | 1 day ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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ArtGAN
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I'm looking for hoards of modern digital art to train an AI to generate art
This could help: https://github.com/cs-chan/ArtGAN https://archive.org/details/academictorrents_1d154cde2fab9ec8039becd03d9bb877614d351b Reach out to e.g. the National Gallery of Art providing proof of your student status (like enrollment cert and a note from your prof); they might be able to provide you with a bulk download option for their paintings. Reach out to the art department and the library of your uni - they might be able to help.
pytorch-CycleGAN-and-pix2pix
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List of AI-Models
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- I want an A.I. to learn my art style so I can keep making art in my art style despite not having the time to do it.
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I'm looking for an AI Art generator from images
pix2pix (https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) - This is a PyTorch implementation of the pix2pix algorithm for image-to-image translation. Given a set of images, the model can learn to generate a new image from a different domain that is similar to the input image.
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Seamless textures with SD and PBR maps with a pix2pix cGAN
Using junyanz/pytorch-CycleGAN-and-pix2pix as a basis for pix2pix, I applied the same blending method to fix seams. It essentially takes an input image and generates an output. The results depend on the paired training data. In this case, each map (height, roughness, etc.) is a separate checkpoint and had to be trained on paired training data with the diffuse as the input and the respective map as the output.
- IA art
- Segmentation and clasification with UNET
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Trying to understand PatchGAN discriminator
Code for https://arxiv.org/abs/1611.07004 found: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
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I made a 3d topographic map based on my recent civ6 game
pix2pix algorithm is used for translating Civ6Maps to heightmaps. Synthesized terrain was rendered in blender.
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This Wojak Does Not Exist
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
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Training a neural net to generate Wojaks
I'm working on creating a face-to-wojak model using PyTorch CycleGan/Pix2Pix [0] and found some of my outputs to be outrageous yet somehow relatable. People are into it so thought I'd share on HN
[0] https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
What are some alternatives?
ebsynth - Fast Example-based Image Synthesis and Style Transfer
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
StyleSwin - [CVPR 2022] StyleSwin: Transformer-based GAN for High-resolution Image Generation
Deep-Fakes
Deep-Exemplar-based-Video-Colorization - The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
MobileStyleGAN.pytorch - An official implementation of MobileStyleGAN in PyTorch
PaddleGAN - PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.