Stable Diffusion web UI
- deep-learning
- diffusion
- image-generation
- image2image
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Stable Diffusion web UI
Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4.1, MiniMax-H3, Gemma 4, FLUX and more.
Translated⚡️HivisionIDPhotos: a lightweight and efficient AI ID photos tools. A lightweight AI algorithm for making ID photos.
Image augmentation for machine learning experiments.
🐍 Geometric Computer Vision Library for Spatial AI
Translate manga/image 一键翻译各类图片内文字 https://cotrans.touhou.ai/ https://greasyfork.org/en/scripts/437569-cotrans-manga-image-translator-regular-edition
A self-hosted open source photo management service.
A Python framework for GPU-accelerated simulation, robotics, and machine learning.
SUPIR aims at developing Practical Algorithms for Photo-Realistic Image Restoration In the Wild. Our new online demo is also released at suppixel.ai.
TranslatedYet another computer-aided comic/manga translation tool powered by deeplearning. Supports one-click machine translation and simple image/text editing.
A PyTorch Library for Accelerating 3D Deep Learning Research
Keras model to generate HTML code from hand-drawn website mockups. Implements an image captioning architecture to drawn source images.
Image augmentation library in Python for machine learning.
Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.
Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR
QualityScaler - image/video AI upscaler app
Implementation of papers in 100 lines of code.
:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification
HistoClean is a tool for the preprocessing and augmentation of images used in deep learning models. This easy to use application brings together the most popular image processing packages from across the python universe, meaning no more looking at documentation! HistoClean provides real time feedback to augmentations and preprocessing options. This allows users to evaluate their steps before implementation.