pytorch-CycleGAN-and-pix2pix
precision-recall-distributions
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| pytorch-CycleGAN-and-pix2pix | precision-recall-distributions | |
|---|---|---|
| 10 | 1 | |
| 21,998 | 95 | |
| - | - | |
| 2.8 | 0.0 | |
| 1 day ago | over 1 year ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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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
precision-recall-distributions
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[R] What are some up-to-date references to the evaluation of model fit across various types of generative models (GANs, flows, VAEs, diffusion, etc.)?
Code for https://arxiv.org/abs/1806.00035 found: https://github.com/msmsajjadi/precision-recall-distributions
What are some alternatives?
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
the-gan-zoo - A list of all named GANs!
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
generative-evaluation-prdc - Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
vae-anomaly-detector - Experiments on unsupervised anomaly detection using variational autoencoder. The variational autoencoder is implemented in Pytorch.
Deep-Fakes
disentangling-vae - Experiments for understanding disentanglement in VAE latent representations
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
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.
Anime-face-generation-DCGAN-webapp - A port of my Anime face generation using Pytorch into a Webapp
DeepMosaics - Automatically remove the mosaics in images and videos, or add mosaics to them.