Official codebase for "Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation"

MagicTryOn is a video virtual try-on framework based on a large-scale video diffusion Transformer.

A Collection of Google Colab Notebooks for various projects

基于MoneyPrinterTurbo，AI生成分镜大纲与视频（动态，不是念ppt），接入万相通义wan2.1 ai文生视频、图生视频功能，灵活把控视频生成。Based on MoneyPrinterTurbo, AI generates image outline and video (dynamic, not ppt), and integrates wan2.1 text-to-video and image-to-video functions to flexibly control video generation.

Reinforcement Learning Framework for Visual Generation

official code for "magicworld: towards long-horizon stability for interactive video world exploration"

[ICCV 2025] FiVE-Bench: A Fine-grained Video Editing Benchmark for Evaluating Emerging Diffusion and Rectified Flow Models

[ICLR 2026] This is the official PyTorch implementation of "QVGen: Pushing the Limit of Quantized Video Generative Models".

A one-click, fully automated script for installing Wan2GP.

Bringing video generation to a next level by automation and simplification of routine processes.

Benchmarking training throughput of contemporary diffusion models on AMD's GPU hardware.

ComfyUI_Wan22Blockswap tries to uzse advanced VRAM optimization techniques for WAN 2.1/2.2 models.

A pure Golang binding library for stable-diffusion.cpp based on github.com/ebitengine/purego, no cgo dependency required, supporting cross-platform operation.

Official repository for Tio Magic Animation Toolkit

🎥 Generate high-quality interactive videos in real-time using Causal Forcing, a method that elevates autoregressive diffusion distillation.

🎨 Build and train reinforcement learning models for advanced visual content generation with a scalable, modular framework.