yangu94

facechain

FaceChain is a deep-learning toolchain for generating your Digital-Twin.

stable-diffusion

A latent text-to-image diffusion model

Concept-centric-Personalization

The official code of "Concept-centric Personalization with Large-scale Diffusion Priors".

kohya_ss

Stable-Diffusion

Stable Diffusion, SDXL, LoRA Training, DreamBooth Training, Automatic1111 Web UI, DeepFake, Deep Fakes, TTS, Animation, Text To Video, Tutorials, Guides, Lectures, Courses, ComfyUI, Google Colab, RunPod, NoteBooks, ControlNet, TTS, Voice Cloning, AI, AI News, ML, ML News, News, Tech, Tech News, Kohya LoRA, Kandinsky 2, DeepFloyd IF, Midjourney

sd-webui-additional-networks

DiffBIR

Official codes of DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

Awesome-AIGC-Tutorials

Curated tutorials and resources for Large Language Models, AI Painting, and more.

sd-face-editor

Face Editor for Stable Diffusion

stable-diffusion-webui

Stable Diffusion web UI

multi-task-defocus-deblurring-dual-pixel-nimat

Reference github repository for the paper "Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning". We propose a single-image deblurring network that incorporates the two sub-aperture views into a multitask framework. Specifically, we show that jointly learning to predict the two DP views from a single blurry input image improves the network’s ability to learn to deblur the image. Our experiments show this multi-task strategy achieves +1dB PSNR improvement over state-of-the-art defocus deblurring methods. In addition, our multi-task framework allows accurate DP-view synthesis (e.g., ~ 39dB PSNR) from the single input image. These high-quality DP views can be used for other DP-based applications, such as reflection removal. As part of this effort, we have captured a new dataset of 7,059 high-quality images to support our training for the DP-view synthesis task.

UABC

Universal ABeration Correction Network

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

recurrent-defocus-deblurring-synth-dual-pixel

Reference github repository for the paper "Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data". We propose a procedure to generate realistic DP data synthetically. Our synthesis approach mimics the optical image formation found on DP sensors and can be applied to virtual scenes rendered with standard computer software. Leveraging these realistic synthetic DP images, we introduce a new recurrent convolutional network (RCN) architecture that can improve defocus deblurring results and is suitable for use with single-frame and multi-frame data captured by DP sensors.

event-camera-visualization

It's a very basic visualization of event camera outputs.

MIRNet

[ECCV 2020] Learning Enriched Features for Real Image Restoration and Enhancement. SOTA results for image denoising, super-resolution, and image enhancement.

Human-Aware-Motion-Deblurring

This repo is the implementation of the paper Human-Aware Motion Deblurring (https://arxiv.org/abs/2001.06816)

CoCosNet

DRN

Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution

SPSR

Pytorch implementation of Structure-Preserving Super Resolution with Gradient Guidance (CVPR 2020 & TPAMI 2021)

progressive_growing_of_gans

Progressive Growing of GANs for Improved Quality, Stability, and Variation

Face-Super-Resolution

Face super resolution based on ESRGAN

DCGAN

In this repository, I reproduce the DCGAN paper using PyTorch

dcgan_vae_pytorch

dcgan combined with vae in pytorch!

luarocks

LuaRocks is the package manager for the Lua programming language.

SiGAN

SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination

ECCV16-CBN

Demo code + Caffe prototxt training file, for Shizhan Zhu et al.'s ECCV-16 work Deep Cascaded Bi-Network for Face Hallucination

Face-and-Image-super-resolution

IMDN

Lightweight Image Super-Resolution with Information Multi-distillation Network (ACM MM 2019, Winner Award of ICCVW AIM 2019 Constrained SR Track1&Track2)

knowledge-distillation-papers

knowledge distillation papers