Projects sharing features with ProPainter

HunyuanVideo-1.5 is a video generation foundation model and text-to-video diffusion framework. It utilizes a latent video diffusion model and a spatio-temporal transformer architecture to generate high-definition video sequences from text descriptions and images. The project enables cinematic camera control for directing pans and tilts and provides image-to-video animation capabilities. It supports visual style adaptation through low-rank adaptation tuning and uses a language model for prompt refinement to improve visual alignment. The model covers high-resolution video upscaling via a super

Tune-A-Video is a text-to-video diffusion framework designed to convert pretrained text-to-image diffusion models into video generators. It utilizes a spatio-temporal attention mechanism and single text-video pair training to enable the synthesis of moving sequences from text prompts. The project provides tools for one-shot video personalization, allowing a model to be tuned on a single reference video to preserve specific characters or artistic styles across new generations. It also functions as a video editor that modifies subjects, backgrounds, and styles through noise-sampling prompt guid

CogVideo is a video generation framework and large language model architecture designed for synthesizing high-resolution video clips from natural language descriptions and images. It functions as a text-to-video and image-to-video generator, while also providing a model for video captioning to analyze visual content into descriptive text summaries. The system supports animating static images into motion sequences and transforming series of images into video based on prompts. It includes capabilities for extending the length of generated video clips to create longer sequences of motion. The f

This project is a deep learning image restoration tool designed to remove scratches, fading, and noise from aged photographs and film. It utilizes generative adversarial networks for image translation, alongside specialized networks for face enhancement and video colorization. The system distinguishes itself through a combination of latent-space domain mapping and progressive face enhancement to recover blurred or missing high-frequency facial details. For video content, it employs a colorization framework that uses optical flow and temporal guidance to propagate color from selected keyframes

This is a real-time object detection framework built on the YOLOv3 architecture, implemented in PyTorch. It provides a complete pipeline for identifying and localizing objects in images and video using a single neural network pass, combining a Darknet-53 backbone with multi-scale feature pyramids and anchor-based bounding box prediction. The framework extends beyond basic detection to include instance segmentation, human pose estimation, and multi-object tracking across video frames. It offers a model export toolkit that converts trained models through ONNX to CoreML, TensorFlow Lite, and Ten

This project is a local AI inpainting tool designed to erase hard-coded subtitles and watermarks from videos and images. It functions as a content-aware media restorer that uses deep learning to reconstruct missing pixels and preserve the original resolution of the source files. The software is distinguished by its local execution model, running inference on host hardware to process media without relying on external cloud APIs. It employs content-aware model selection, allowing the use of different generative algorithms based on media types, such as animation or live action, to optimize visua

BasicSR is a PyTorch-based image restoration toolbox and framework designed for training and deploying deep learning models to upscale, denoise, and deblur images and videos. It serves as a comprehensive system for image super-resolution and video quality restoration, providing the necessary infrastructure to recover fine visual details and increase pixel density. The project distinguishes itself through specialized toolkits for facial image enhancement and high-fidelity face synthesis, as well as a dedicated video quality restoration suite that utilizes deformable convolutions and generative

This project is a deep learning implementation of the RetinaNet architecture for detecting and classifying objects within images. Built as a Keras object detection framework and a TensorFlow computer vision tool, it provides a complete neural network implementation based on the RetinaNet paper. The framework includes specialized components such as a Feature Pyramid Network and a focal loss function to handle object detection. It features a configurable backbone architecture and anchor-based bounding boxes to predict object locations across varying scales and aspect ratios. The toolset covers

Deformable-DETR is an object detection system for computer vision that uses a transformer-based encoder-decoder architecture. It identifies and locates objects within images by representing potential targets as a set of learnable queries. The project employs sampling-based attention to restrict attention to a small set of points around a reference, reducing computational complexity and speeding up convergence. It further utilizes multi-scale feature fusion to detect objects of varying sizes within a single frame. The system includes capabilities for training models across multiple GPU cluste

Inpaint-Anything is a diffusion-based image editor and inpainting tool designed to remove or replace objects in images, videos, and 3D scenes. It functions as a text-guided manipulator that uses natural language descriptions and mask-based filling to modify visual content. The system provides specialized capabilities for multi-view 3D scene editing and video object removal. It tracks selected objects across multiple frames or perspectives to synthesize consistent backgrounds and maintain spatial coherence after an element is removed. The tool covers a range of image manipulation tasks, inclu

PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

PlugNPlay-Modules is a collection of reusable PyTorch computer vision modules and deep learning architectural components. It provides a library of standardized building blocks for constructing neural networks, focusing on attention mechanisms, signal processing layers, and feature fusion modules. The project is distinguished by its extensive variety of attention primitives, covering spatial, channel, and temporal weighting, as well as specialized variants like deformable, frequency-enhanced, and linear-complexity attention. It also implements advanced signal processing tools within the neural

This project is a diffusion-based AI art generator and animation framework used to create digital images and motion graphics from text prompts. It functions as a system for producing stylized videos and AI art through iterative diffusion sampling and neural network models. The framework distinguishes itself through specialized tools for 3D depth animation, using depth-map transformations to create spatial movement. It also includes neural style transfer capabilities to apply specific artistic looks, such as watercolor or pixel art, and utilizes optical flow frame blending to reduce flickering

This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and localizing multiple objects within images and video. The project provides a neural network architecture designed for single-shot object detection, which predicts bounding boxes and class labels in one pass. The implementation includes a real-time object detector capable of processing live video streams to track and label objects across sequential frames. It also features a complete computer vision training pipeline for preparing image datasets and training model weights. The fra

ComfyUI is a modular generative AI workflow orchestrator and node-based GUI for designing and executing complex diffusion model pipelines. It functions as both a visual interface for building generative logic graphs and a programmable backend API that exposes diffusion model operations for external integration. The system distinguishes itself through a graph-based execution model that supports differential workflow execution, re-running only modified nodes to reduce computation. It features dynamic model offloading to manage memory between system RAM and GPU VRAM and utilizes metadata-embedde

DeepLabCut is a deep learning toolkit for markerless 2D and 3D animal pose estimation. It functions as a motion tracking system that identifies anatomical keypoints on animals in video sequences without the need for physical markers. The framework utilizes transfer learning and a library of pre-trained weights to accelerate the training of networks for different species. It supports multi-individual identity tracking to maintain unique identities across video sequences and offers real-time pose detection for live video feeds. The system covers a broad range of computer vision capabilities, i

This project is a PyTorch implementation of the YOLOv3 object detection architecture. It functions as a real-time object detector and computer vision framework designed to identify and locate multiple objects within images using bounding boxes and class labels. The system allows for both the use of pretrained weights for immediate image analysis and the training of custom models using datasets with bounding box annotations. It provides a programmatic interface to integrate detection capabilities directly into other software applications. The framework includes tools for model evaluation to m

Detectron is a PyTorch object detection framework and computer vision research platform. It provides implementations of neural network architectures for locating and identifying objects in images, including Mask R-CNN for generating instance segmentation masks and RetinaNet for one-stage detection. The platform supports computer vision prototyping and object detection research through the deployment of pre-trained baseline models. This allows for the rapid implementation and evaluation of visual recognition systems. Its capabilities cover image object localization and instance segmentation w

HigherHRNet is a deep learning framework designed for bottom-up human pose estimation. It functions as a computer vision keypoint detector that identifies and tracks human body joints by utilizing high-resolution feature pyramids and scale-aware representation learning. The project distinguishes itself through a bottom-up approach, which identifies individual body parts across an entire image before clustering them into distinct human skeletons. This methodology is supported by multi-resolution feature fusion, which maintains high-resolution representations throughout the network by repeatedl

AnimateAnyone is an appearance-preserving video synthesizer designed for character animation from a single static image. It functions as a diffusion image-to-video generator that transforms a source image into a high-fidelity video sequence while maintaining consistent character identity, clothing, and visual details across all frames. The system enables video-driven character reenactment by transferring motions, facial expressions, and body movements from a reference video onto a static character. It employs pose-guided video generation to control movement via skeleton keypoints and pose sig

This project is a neural network extension for Stable Diffusion that provides spatial control and geometric consistency for text-to-image generation. It functions as an image structure controller and conditioning tool, enabling the use of external inputs to guide the layout and geometry of generated imagery. The framework is distinguished by its ability to transform input images into structural guides through various preprocessors. These include the extraction of depth maps, normal maps, and human pose landmarks, as well as the detection of Canny edges, anime lineart, and straight architectur

This project provides a deep learning framework for synthesizing video content from text prompts. It functions as a generative video artificial intelligence model that utilizes latent diffusion sampling to iteratively refine noise into coherent visual sequences. The architecture is built on a modular design that separates spatial and temporal processing, allowing the system to handle both static images and video sequences within a unified training pipeline. By employing spatiotemporal convolutional layers and temporal attention mechanisms, the model maintains visual consistency and fluid moti

RT-DETR is a real-time object detection model based on the detection transformer architecture. It is implemented as a computer vision model for both the PyTorch and PaddlePaddle deep learning platforms, designed to identify and locate multiple objects in images and video streams. The model eliminates the need for anchor generation and non-maximum suppression by utilizing a transformer-based approach. It focuses on high-performance detection, balancing precision and low latency for live environment deployment. The system employs a hybrid encoder and multi-scale feature fusion to extract globa

Table Transformer is a deep learning framework designed for document layout analysis and the automated extraction of tabular data from unstructured images and documents. It utilizes a transformer-based architecture to perform object detection, identifying table boundaries and internal grid structures to convert visual information into machine-readable formats. The project distinguishes itself by employing a global optimization strategy for bipartite matching, which eliminates the need for traditional non-maximum suppression during the detection process. By leveraging multi-scale feature extra

PaddleGAN is a generative AI framework and deep learning computer vision library built on the PaddlePaddle framework. It serves as a toolkit for image and video synthesis, providing a collection of generative adversarial network implementations for creating synthetic visual content. The library focuses on advanced synthesis capabilities, including the generation of talking heads through lip motion synchronization and the creation of synthetic videos via motion transfer from driving sequences. It provides tools for domain-to-domain translation, allowing for image style transfer and the transfo

libfacedetection is a C++ face detection library and computer vision tool. It utilizes a neural network face detector to identify human faces in images and return bounding box coordinates. The library is designed for low latency and high throughput processing, enabling real-time face detection in image and video streams. It supports automated image analysis for identifying coordinates of human faces across large batches of photos and high-performance video processing.

YOLOv7 is a PyTorch vision library and real-time inference engine designed for object detection, human pose estimation, and instance segmentation. It provides a framework for detecting and locating multiple objects within images or video streams using neural networks. The system includes tools for custom model training and fine-tuning, allowing pre-trained weights to be adapted to specialized datasets via transfer learning. It also supports model weight export and format conversion to facilitate deployment on production servers and embedded edge devices.