a-PyTorch-Tutorial-to-Super-Resolution
pytorch-CycleGAN-and-pix2pix
| a-PyTorch-Tutorial-to-Super-Resolution | pytorch-CycleGAN-and-pix2pix | |
|---|---|---|
| 2 | 10 | |
| 542 | 22,029 | |
| - | - | |
| 2.7 | 2.5 | |
| about 1 year ago | 1 day ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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a-PyTorch-Tutorial-to-Super-Resolution
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What subjects in Python do you need to know to tackle Artificial Intelligence?
Image upscaling might be a good goal to work towards first, and there are probably lots of good tutorials on it. You can follow a tutorial and Google anything from the tutorial that you don't understand. Here's one I found that looks helpful at first glance, and it includes working code: https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution
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I have dozens of blurred images cropped from a custom YOLOv3 detection - What GAN can I use to reconstruct a single superior image?
Hi try to give a look at image super resolution task https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution . This is the first result from google. Of course the quality of the results could depend on your data (domain shift). In that case grab high quality images from internet, lower their quality and do fine tuning of the selected model
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?
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
Fast-SRGAN - A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
Image-Super-Resolution-via-Iterative-Refinement - Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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.
Deep-Fakes
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
iSeeBetter - iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
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.