Deep-Exemplar-based-Video-Colorization
pytorch-CycleGAN-and-pix2pix
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| Deep-Exemplar-based-Video-Colorization | pytorch-CycleGAN-and-pix2pix | |
|---|---|---|
| 4 | 10 | |
| 317 | 21,998 | |
| - | - | |
| 0.0 | 2.8 | |
| over 1 year ago | 2 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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Deep-Exemplar-based-Video-Colorization
- [Machine Learning] [R] colorisation vidéo basée sur un exemple
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Show HN: I made a new AI colorizer
Thanks! yeah, a few people have used the tool to colorize videos, frame by frame. For example Lord of the flies (1963): https://www.dailymotion.com/video/x8eiho4
Although, I'd recommend colorizing a few key frames and then use https://github.com/zhangmozhe/Deep-Exemplar-based-Video-Colo...
Cool, yeah, my next model will be better for comic books. You can also use the 'Surprise Me' button in the editor and you'll get some decent results.
- 1929 video from Shanghai, upscaled to 4K color using AI
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[P] Colorizing the legacy videos with attention mechanism
We recently released the code for our paper "Deep Exemplar-based Video Colorization". The code along with the Colab demo is available at: https://github.com/zhangmozhe/Deep-Exemplar-based-Video-Colorization. Welcome to have a try.
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
[0] https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
What are some alternatives?
CycleGAN - Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
Few-Shot-Patch-Based-Training - The official implementation of our SIGGRAPH 2020 paper Interactive Video Stylization Using Few-Shot Patch-Based Training
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
mmagic - OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
OASIS - Official implementation of the paper "You Only Need Adversarial Supervision for Semantic Image Synthesis" (ICLR 2021)
Deep-Fakes
TraVeLGAN_with_perceptual_loss - The implementation code of Thesis project which entitled "Photo-to-Emoji Transformation with TraVeLGAN and Perceptual Loss" as a final project in my master study.
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
HyperGAN - Composable GAN framework with api and user interface
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.