DeepSIM
pytorch-CycleGAN-and-pix2pix
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| DeepSIM | pytorch-CycleGAN-and-pix2pix | |
|---|---|---|
| 3 | 10 | |
| 418 | 21,952 | |
| - | - | |
| 1.8 | 2.8 | |
| over 2 years ago | 1 day ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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DeepSIM
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[P] I made FaceShop! Instance segmentation + CGAN for editing faces (badly)
Pix2PixHD (from DeepSIM)
Uses a mix of instance segmentation (BiSeNet) and conditional GAN, and is heavily inspired by the Pix2PixHD and DeepSIM papers. Will have more details when I wake up!
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Israeli Researchers Unveil DeepSIM, a Neural Generative Model for Conditional Image Manipulation Based on a Single Image
5 Min Read | Paper | Project | Code
pytorch-CycleGAN-and-pix2pix
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List of AI-Models
Click to Learn more...
- 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
What are some alternatives?
anycost-gan - [CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
SDEdit - PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
Deep-Fakes
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
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
Text2LIVE - Official Pytorch Implementation for "Text2LIVE: Text-Driven Layered Image and Video Editing" (ECCV 2022 Oral)
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.