OASIS
pytorch-CycleGAN-and-pix2pix
| OASIS | pytorch-CycleGAN-and-pix2pix | |
|---|---|---|
| 1 | 10 | |
| 317 | 22,029 | |
| 2.5% | - | |
| 10.0 | 2.5 | |
| over 1 year ago | 3 days ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | GNU General Public License v3.0 or later |
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OASIS
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StyleGAN-NADA: Blind Training and Other Wonders
Conclusion So this is how StyleGAN-NADA, a CLIP-guided zero-shot method for Non-Adversarial Domain Adaptation of image generators, works. Although the StyleGAN-NADA is focused on StyleGAN, it can be applied to other generative architectures such as OASIS and many others.
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?
Deep-Exemplar-based-Video-Colorization - The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
Keras-GAN - Keras implementations of Generative Adversarial Networks.
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
AdamP - AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights (ICLR 2021)
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Deep-Fakes
clean-fid - PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
anycost-gan - [CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
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.