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2 Repos

Awesome GitHub RepositoriesWorld Simulation Generators

Generates images, videos, synchronized sound, and action-conditioned rollouts from text, image, video, or action inputs for world simulation and synthetic data.

Distinct from Video Generation: Distinct from Video Generation: focuses on generating world simulations with synchronized audio and action-conditioned rollouts for physical AI, not general video synthesis.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · World Simulation Generators. Refine with filters or upvote what's useful.

Awesome World Simulation Generators GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • nvidia/cosmosAvatar von NVIDIA

    NVIDIA/cosmos

    10,494Auf GitHub ansehen↗

    Cosmos is an open platform of world models, datasets, and tools for building physical AI systems such as robots and autonomous vehicles. It provides video generation and video understanding models that can generate synthetic videos and world simulations from text, image, video, or action inputs, and analyze videos to produce captions, event timestamps, spatial bounding boxes, and next-action predictions. The platform includes a world simulation generator that produces images, videos, synchronized audio, and action-conditioned rollouts for synthetic data, alongside a visual content analyzer th

    Generates synthetic videos and world simulations from text, image, video, or action inputs for training and testing.

    Jupyter Notebook
    Auf GitHub ansehen↗10,494
  • hao-ai-lab/fastvideoAvatar von hao-ai-lab

    hao-ai-lab/FastVideo

    3,743Auf GitHub ansehen↗

    FastVideo is a comprehensive system for accelerated video generation, serving as a video generation inference engine, a video diffusion training framework, and a modular pipeline orchestrator. It provides a distributed transformer optimizer and a distillation toolkit designed to reduce denoising steps and model complexity to increase frame rates. The project distinguishes itself through specialized acceleration techniques, including joint distillation and sparse attention training. It implements low-step video generation and weight quantization to FP8 or FP4 precision to increase throughput a

    Creates interactive image-to-video game environments that respond to keyboard and mouse control inputs.

    Pythondiffusersdiffusion-modelsdistillation
    Auf GitHub ansehen↗3,743
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