rafaelvareto
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A simple PyTorch implementation of CartoonGAN
Official tensorflow implementation for CVPR2020 paper “Learning to Cartoonize Using White-box Cartoon Representations”
Implementation and modification of CartoonGAN
Re-Implementation of "Image Inpainting for Irregular Holes using Partial Convolution"
Unofficial pytorch implementation of 'Image Inpainting for Irregular Holes Using Partial Convolutions' [Liu+, ECCV2018]
Model summary in PyTorch similar to `model.summary()` in Keras
Pytorch implementation of the SN-PatchGAN inpainter.
A PyTorch reimplementation of the paper Free-Form Image Inpainting with Gated Convolution (DeepFill v2) (https://arxiv.org/abs/1806.03589)
DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral
Official inference repo for FLUX.1 models
Image inpainting project using PatchGAN on the CelebA dataset. Fills in missing or damaged parts of images for enhanced photo editing, restoration, and object removal.
Official PyTorch Code and Models of "RePaint: Inpainting using Denoising Diffusion Probabilistic Models", CVPR 2022
Stable diffusion for inpainting
A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)
Self-contained, minimalistic implementation of diffusion models with Pytorch.
Implementation of "Denoising Diffusion Probabilistic Models", Ho et al., 2020
We propose the shadow-guided inpainting task to take advantage of the shadow removal and image inpainting.
My notes / works on deep learning from Coursera
In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets …
Notebooks, projects and study material of the 'Sequences, Time Series and Prediction' course by deeplearning.ai
[pytorch] DANet: Dual Attention Network for Scene Segmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Pytorch re-implementation of boundary loss, proposed in "Boundary Loss for Remote Sensing Imagery Semantic Segmentation"
Deep Learning for Seismic Imaging and Interpretation
A Python package for reading and writing SEG Y files.