Search Results for "web-based"
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Showing 26 open source projects for "web-based"
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Pixelle-Video
AI Fully Automated Short Video Engine
Pixelle-Video is an AI-driven system designed for generating and processing video content using modern generative techniques. It focuses on enabling automated video creation workflows where visual content can be synthesized, edited, or enhanced through AI models. The project integrates different components of video processing, such as frame generation, transformation, and sequencing, into a unified pipeline. It is built to support experimentation with generative video models, making it... -
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MoneyPrinterTurbo
Generate short videos with one click using AI LLM
MoneyPrinterTurbo is an AI-driven tool that enables users to generate high-definition short videos with minimal input. By providing a topic or keyword, the system automatically creates video scripts, sources relevant media assets, adds subtitles, and incorporates background music, resulting in a polished video ready for distribution. -
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AutoClip
AI-powered video clipping and highlight generation
...The system supports downloading videos from major platforms (e.g. YouTube, Bilibili), or accepting local uploads, and then applies AI analysis to identify segments worth clipping based on content (e.g. high energy moments, speech, or other heuristics). Once highlights are identified, AutoClip can automatically cut those segments and optionally assemble them into a compilation, thus greatly reducing manual video editing effort. It uses a modern web application stack with a front end (React + TypeScript) for user interaction and a back end that handles downloading, processing, clipping, and queue management, allowing real-time progress feedback and easy deployment, e.g. via Docker. -
4
Remotion
Make videos programmatically with React
...Because it integrates with the React ecosystem, Remotion fits naturally into modern front-end stacks and tooling, and can produce dynamic content like personalized videos, dashboards, and data-driven animations with the same code used to build interactive web apps. The framework supports exporting to standard video formats, audio synchronization, frame callbacks, and powerful tooling for previewing and debugging, so teams can iterate quickly and reliably. -
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Wan2.1
Wan2.1: Open and Advanced Large-Scale Video Generative Model
Wan2.1 is a foundational open-source large-scale video generative model developed by the Wan team, providing high-quality video generation from text and images. It employs advanced diffusion-based architectures to produce coherent, temporally consistent videos with realistic motion and visual fidelity. Wan2.1 focuses on efficient video synthesis while maintaining rich semantic and aesthetic detail, enabling applications in content creation, entertainment, and research. The model supports text-to-video and image-to-video generation tasks with flexible resolution options suitable for various GPU hardware configurations. ... -
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LTX-2.3
Official Python inference and LoRA trainer package
...Unlike most earlier video generation systems that only produced silent clips, LTX-2 combines video and audio generation in a unified architecture capable of producing coherent audiovisual scenes. The model uses a diffusion-transformer-based architecture designed to generate high-fidelity visual frames while simultaneously producing corresponding audio elements such as speech, music, ambient sound, or effects. This unified approach allows creators to generate complete multimedia sequences where motion, timing, and sound are aligned automatically. LTX-2 is designed for both research and production workflows and can generate high-resolution video clips with precise control over structure, motion, and camera behavior. -
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Wan2.2
Wan2.2: Open and Advanced Large-Scale Video Generative Model
...The model is trained on significantly larger datasets than its predecessor, greatly enhancing motion complexity, semantic understanding, and aesthetic diversity. Wan2.2 also open-sources a 5-billion parameter high-compression VAE-based hybrid text-image-to-video (TI2V) model that supports 720P video generation at 24fps on consumer-grade GPUs like the RTX 4090. It supports multiple video generation tasks including text-to-video. -
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ComfyUI-LTXVideo
LTX-Video Support for ComfyUI
ComfyUI-LTXVideo is a bridge between ComfyUI’s node-based generative workflow environment and the LTX-Video multimedia processing framework, enabling creators to orchestrate complex video tasks within a visual graph paradigm. Instead of writing code to apply effects, transitions, edits, and data flows, users can assemble nodes that represent video inputs, transformations, and outputs, letting them prototype and automate video production pipelines visually. -
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CogVideo
Text and image to video generation: CogVideoX and CogVideo
CogVideo is an open-source family of advanced video generation models that can create videos from text, images, or existing video inputs. Built on large-scale Transformer and diffusion architectures, it enables multimodal generation across text-to-video, image-to-video, and video continuation tasks. The latest CogVideoX models offer higher resolution outputs, longer video durations, and improved controllability through prompt engineering. The project includes tools for inference,... -
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AI YouTube Shorts Generator
A python tool that uses GPT-4, FFmpeg, and OpenCV
AI-YouTube-Shorts-Generator is a Python-based tool that automates the creation of short-form vertical video clips (“shorts”) from longer source videos — ideal for adapting content for platforms like YouTube Shorts, Instagram Reels, or TikTok. It analyzes input video (whether a local file or a YouTube URL), transcribes audio (with optional GPU-accelerated speech-to-text), uses an AI model to identify the most compelling or engaging segments, and then crops/resizes the video and applies subtitle overlays, producing a polished short video without manual editing. ... -
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Medeo Video Generator
AI-powered video generation skill for OpenClaw
Medeo Video Generator is an AI-driven project designed to enable advanced video processing and generation capabilities within agent-based or automation systems. It provides a “skill” module that can be integrated into AI agents, allowing them to create, edit, and manipulate video content programmatically. The project focuses on bridging the gap between language-based AI systems and multimedia outputs by enabling models to produce structured video content as part of their workflows. ... -
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Sora.FM
Sora AI Video Generator by Sora.FM
Sora.FM is positioned as a tool in the AI-generated video domain — likely aiming to let users produce video content via AI-driven workflows rather than classic manual editing. The project belongs to the growing class of “AI video generator / AI-assisted content creation” tools: it may use model-based generation, template-based editing, or combine video assets with generative models to automate parts of video creation or editing. For creators wanting to explore AI-based content generation — for example automated video clips, short-form media, or other generated video content — sorafm offers a starting point. As with many open-source generators in this space, the tradeoff lies in balancing ease-of-use and the limitations of generative output, but the fact that it’s publicly available means users can experiment, iterate, or fork to adapt pipelines: maybe customizing model prompts, video templates, or post-processing. -
13
video2robot
End-to-end pipeline converting generative videos
...This workflow allows users to generate robot motion files that specify joint angles, root positions, and orientations that can be deployed on supported robot platforms (e.g., Unitree models). Video2robot includes scripts for each stage of the pipeline (generation, extraction, conversion, visualization) and can run as a CLI or through a basic web UI. -
14
HunyuanVideo-I2V
A Customizable Image-to-Video Model based on HunyuanVideo
HunyuanVideo-I2V is a customizable image-to-video generation framework from Tencent Hunyuan, built on their HunyuanVideo foundation. It extends video generation so that given a static reference image plus an optional prompt, it generates a video sequence that preserves the reference image’s identity (especially in the first frame) and allows stylized effects via LoRA adapters. The repository includes pretrained weights, inference and sampling scripts, training code for LoRA effects, and... -
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HunyuanCustom
Multimodal-Driven Architecture for Customized Video Generation
...It supports conditioning via image, audio, video, and text, and can perform subject replacement in videos, generate avatars speaking given audio, or combine multiple subject images. The architecture builds on HunyuanVideo, with added modules for identity reinforcement and modality-specific condition injection. Text-image fusion module based on LLaVA for improved multimodal understanding. Applicable to single- and multi-subject scenarios, video editing/replacement, singing avatars etc. -
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yt-x
Browse youtube from your terminal
yt-x is a lightweight tool designed to enhance the YouTube viewing and interaction experience by providing additional functionality beyond the standard platform interface. It typically works by injecting scripts or modifying how YouTube pages behave, enabling users to customize playback, interface elements, or interaction features. The project focuses on improving usability and control, allowing users to tailor their viewing experience according to personal preferences. It may include... -
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NodeTool
Visual AI Workflow Builder
NodeTool is an open‑source, visual AI workflow builder that lets you connect nodes for text, images, audio, video, data, and automation—then run them locally or on the cloud. Build multi‑step agents, RAG systems, and creative media pipelines without coding, inspect execution in real time, and deploy anywhere: home server, private VPC, RunPod, or Cloud Run. With a local‑first design, NodeTool keeps models and data under your control while still supporting providers like OpenAI, Anthropic,... -
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HunyuanVideo-I2V
A Customizable Image-to-Video Model based on HunyuanVideo
HunyuanVideo-I2V is a customizable image-to-video generation framework developed by Tencent, extending the capabilities of HunyuanVideo. It allows for high-quality video creation from still images, using PyTorch and providing pre-trained model weights, inference code, and customizable training options. The system includes a LoRA training code for adding special effects and enhancing video realism, aiming to offer versatile and scalable solutions for generating videos from static image inputs. -
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VideoCrafter2
Overcoming Data Limitations for High-Quality Video Diffusion Models
VideoCrafter is an open-source video generation and editing toolbox designed to create high-quality video content. It features models for both text-to-video and image-to-video generation. The system is optimized for generating videos from textual descriptions or still images, leveraging advanced diffusion models. VideoCrafter2, an upgraded version, improves on its predecessor by enhancing motion dynamics and concept combinations, especially in low-data scenarios. Users can explore a wide... -
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Recurrent Interface Network (RIN)
Implementation of Recurrent Interface Network (RIN)
...They also combine this with the self-conditioning technique from the Bit Diffusion paper, specifically for the latents. The last ingredient seems to be a new noise function based around the sigmoid, which the author claims is better than cosine scheduler for larger images. The big surprise is that the generations can reach this level of fidelity. Will need to verify this on my own machine. Additionally, we will try adding an extra linear attention on the main branch as well as self-conditioning in the pixel space. ... -
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text-to-video-synthesis-colab
Text To Video Synthesis Colab
text-to-video-synthesis-colab is a collection of Google Colab notebooks that enable users to generate videos from text prompts using state-of-the-art AI models. It integrates multiple open-source models and pipelines, allowing users to experiment with different approaches to text-to-video generation. The notebooks are preconfigured for cloud execution, making them accessible without requiring local GPU setups. Users can generate, upscale, and refine videos directly from natural language... -
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Aphantasia
CLIP + FFT/DWT/RGB = text to image/video
...Direct RGB pixels optimization (very stable) depth-based 3D look (courtesy of deKxi, based on AdaBins), complex queries: text and/or image as main prompts, separate text prompts for style and to subtract (avoid) topics. Starting/resuming process from saved parameters or from an image. -
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Amiga Memories
A walk along memory lane
...The images and sound produced by Amiga Memories are 100% automatically generated. The generator itself is implemented in Squirrel, the 3D rendering is done on GameStart 3D. An Amiga Memories video is mostly based on a narrative. The purpose of the script is to define the spoken and written content. The spoken text will be read by a voice synthesizer (Text To Speech or TTS), the written text is simply drawn on the image as subtitles. Here, in addition to the spoken & written narration, the script controls the camera movements as well as the LED activity of the computer. ... -
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NÜWA - Pytorch
Implementation of NÜWA, attention network for text to video synthesis
Implementation of NÜWA, state of the art attention network for text-to-video synthesis, in Pytorch. It also contains an extension into video and audio generation, using a dual decoder approach. It seems as though a diffusion-based method has taken the new throne for SOTA. However, I will continue on with NUWA, extending it to use multi-headed codes + hierarchical causal transformer. I think that direction is untapped for improving on this line of work. In the paper, they also present a way to condition the video generation based on segmentation mask(s). You can easily do this as well, given you train a VQGanVAE on the sketches beforehand. ... -
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Text2Video
Software tool that converts text to video for more engaging experience
...So I imagined, "What if there was a tool that turns text into something more engaging such as a video, wouldn't it improve my learning experience?" I created a prototype web application that takes text as an input and generates a video as an output. I plan to further work on the project targeting young college students who are aged between 18 to 23 because they tend to prefer learning through videos over books based on the survey I found. The technologies I used for the project are HTML, CSS, Javascript, Node.js, CCapture.js, ffmpegserver.js, Amazon Polly, Python, Flask, gevent, spaCy, and Pixabay API.
