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Back to nvlabs/longlive

Open-source alternatives to LongLive

30 open-source projects similar to nvlabs/longlive, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best LongLive alternative.

  • thudm/cogvideoTHUDM avatar

    THUDM/CogVideo

    12,792View on GitHub↗

    CogVideo is a generative video framework that uses diffusion models and transformer-based architectures to synthesize high-resolution video clips. It functions as both a text-to-video and image-to-video generator, converting textual descriptions or static images into temporal visual sequences. The system integrates large language model capabilities to expand short user prompts into detailed descriptions for better visual alignment. It supports the animation of static images through latent seeding and provides the ability to extend the length of existing video sequences. The project includes

    Python
    View on GitHub↗12,792
  • sandai-org/magi-1SandAI-org avatar

    SandAI-org/MAGI-1

    3,711View on GitHub↗

    MAGI-1 is an autoregressive video generation model designed to synthesize high-resolution video sequences from text prompts and image references. It functions as a generative system for text-to-video, image-to-video, and video-to-video transformations. The model utilizes an autoregressive architecture that treats spatio-temporal patches as a sequence of discrete tokens to maintain temporal motion. It employs a variational autoencoder to compress the spatial and temporal dimensions of video data and uses distillation-based step scaling to allow for inference budget control. The system integra

    Pythonautoregressivediffusion-modelsvideo-generation
    View on GitHub↗3,711
  • pku-yuangroup/open-sora-planPKU-YuanGroup avatar

    PKU-YuanGroup/Open-Sora-Plan

    12,163View on GitHub↗

    Open-Sora-Plan is a text-to-video framework and distributed video training system. It utilizes a diffusion transformer architecture and large language model components to transform written descriptions or image prompts into high-quality video sequences. The system features a distributed infrastructure designed for large-scale video training and inference. It employs sequence parallelism to split high-resolution or long-duration video samples across multiple GPUs and uses a sparse attention mechanism to increase processing speed. The project includes capabilities for both text-to-video and im

    Python
    View on GitHub↗12,163

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  • tencent/hunyuanvideoT

    Tencent/HunyuanVideo

    0View on GitHub↗
    View on GitHub↗0
  • wan-video/wan2.2Wan-Video avatar

    Wan-Video/Wan2.2

    14,283View on GitHub↗

    Wan2.2 is a generative video artificial intelligence system designed to synthesize visual media by interpreting natural language instructions. It functions as a text-to-video diffusion model that transforms written concepts into coherent motion sequences through deep learning and latent space manipulation. The system utilizes a transformer-based architecture to process video data as a series of tokens, allowing it to capture complex spatial and temporal relationships. By employing a temporal attention mechanism, the model maintains visual consistency across frames, while its latent space appr

    Pythonaigcvideo-generation
    View on GitHub↗14,283
  • stepfun-ai/step-video-t2vstepfun-ai avatar

    stepfun-ai/Step-Video-T2V

    3,186View on GitHub↗

       

    Python
    View on GitHub↗3,186
  • lightricks/ltx-videoLightricks avatar

    Lightricks/LTX-Video

    9,324View on GitHub↗
    Pythondiffusion-modelsditimage-to-video
    View on GitHub↗9,324
  • lllyasviel/framepacklllyasviel avatar

    lllyasviel/FramePack

    17,028View on GitHub↗

    FramePack is a neural video synthesis engine and generation framework designed to produce long, temporally consistent video sequences. It functions as a diffusion model optimizer, providing a suite of techniques to manage the computational demands of high-parameter video models while maintaining visual stability during extended generation tasks. The system distinguishes itself through a hierarchical approach to frame prediction, which plans distant anchor frames before filling in intermediate content to prevent cumulative temporal drift. By utilizing constant-length context compression and to

    Python
    View on GitHub↗17,028
  • skyworkai/skyreels-v2SkyworkAI avatar

    SkyworkAI/SkyReels-V2

    6,356View on GitHub↗

    SkyReels-V2 is a video generation system that creates, extends, and refines video clips from text descriptions, images, or both. It operates as a diffusion-based video generation model that can produce videos of any duration by denoising frames sequentially, with each new frame conditioned on the ones that came before it. The system supports generating videos from scratch using text prompts, starting from a single image and producing subsequent frames, or constraining both the first and last frames to match user-provided images. What distinguishes SkyReels-V2 is its combination of infinite-le

    Python
    View on GitHub↗6,356
  • tencent-hunyuan/hunyuanvideo-1.5Tencent-Hunyuan avatar

    Tencent-Hunyuan/HunyuanVideo-1.5

    4,440View on GitHub↗

    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

    Pythonimage-to-videotext-to-videovideo-generation
    View on GitHub↗4,440
  • yaofang-liu/pusa-vidgenY

    Yaofang-Liu/Pusa-VidGen

    0View on GitHub↗
    View on GitHub↗0
  • hpcaitech/open-sorahpcaitech avatar

    hpcaitech/Open-Sora

    29,101View on GitHub↗

    Open-Sora is a video generation framework designed to produce cinematic sequences from text prompts and images. It functions as a generative system that transforms written descriptions or reference images into video content featuring realistic textures and lighting. The project includes a dedicated prompt engineering tool that uses large language models to expand simple user inputs into detailed descriptions. It also features a motion controller for adjusting movement intensity in generated sequences and evaluating motion levels in existing video files. The framework incorporates text-to-vid

    Python
    View on GitHub↗29,101
  • wan-video/wan2.1Wan-Video avatar

    Wan-Video/Wan2.1

    15,350View on GitHub↗

    Wan2.1 is a generative video synthesis framework that provides foundation models for creating high-fidelity video sequences and static images from descriptive text prompts. The system utilizes a unified architecture trained on both static and dynamic datasets, allowing it to function as a comprehensive tool for visual media creation. The framework distinguishes itself through a transformer-based temporal modeling approach that ensures structural coherence and consistent motion across video frames. It supports multi-resolution latent scaling, enabling the generation of content in various aspec

    Pythonaigcvideogeneration
    View on GitHub↗15,350
  • stepfun-ai/step1x-editstepfun-ai avatar

    stepfun-ai/Step1X-Edit

    2,231View on GitHub↗

    A SOTA open-source image editing model, which aims to provide comparable performance against the closed-source models like GPT-4o and Gemini 2 Flash.

    Pythonimage-editingreasoningvisual-reasoning
    View on GitHub↗2,231
  • jd-opensource/joyai-echoJ

    jd-opensource/JoyAI-Echo

    0View on GitHub↗
    View on GitHub↗0
  • lightricks/ltx-2Lightricks avatar

    Lightricks/LTX-2

    3,971View on GitHub↗
    Pythongenerative-ailtxltx-2
    View on GitHub↗3,971
  • bytedance/berniniB

    bytedance/Bernini

    0View on GitHub↗
    View on GitHub↗0
  • ezioby/dittoE

    EzioBy/Ditto

    0View on GitHub↗
    View on GitHub↗0
  • river-zhang/iceditRiver-Zhang avatar

    River-Zhang/ICEdit

    2,079View on GitHub↗
    Pythondiffusiondiffusion-modelsdiffusion-transformer
    View on GitHub↗2,079
  • danijar/dreamerv3danijar avatar

    danijar/dreamerv3

    3,461View on GitHub↗

    A reimplementation of DreamerV3paper, a scalable and general reinforcement learning algorithm that masters a wide range of applications with fixed hyperparameters.

    Python
    View on GitHub↗3,461
  • danijar/dreamerv2danijar avatar

    danijar/dreamerv2

    1,049View on GitHub↗

    Status: Stable release

    Python
    View on GitHub↗1,049
  • bghira/simpletunerbghira avatar

    bghira/SimpleTuner

    2,862View on GitHub↗

    A general fine-tuning kit geared toward image/video/audio diffusion models.

    Pythondiffusersdiffusion-modelsfine-tuning
    View on GitHub↗2,862
  • h-embodvis/vega-3dH-EmbodVis avatar

    H-EmbodVis/VEGA-3D

    418View on GitHub↗

    Xianjin Wu 1 , Dingkang Liang 1† , Tianrui Feng 1 , Kui Xia 2 , Yumeng Zhang 2 , Xiaofan Li 2 , Xiao Tan 2 , Xiang Bai 1 1 Huazhong University of Science and Technology, 2 Baidu Inc., China, † Project Lead

    Python
    View on GitHub↗418
  • danijar/directorD

    danijar/director

    0View on GitHub↗

    Deep Hierarchical Planning from Pixels

    View on GitHub↗0
  • h-embodvis/hydraH-EmbodVis avatar

    H-EmbodVis/HyDRA

    257View on GitHub↗

    Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models

    Python
    View on GitHub↗257
  • hao-ai-lab/fastvideohao-ai-lab avatar

    hao-ai-lab/FastVideo

    3,743View on GitHub↗

    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

    Pythondiffusersdiffusion-modelsdistillation
    View on GitHub↗3,743
  • hoyyyaard/3dflowactionHoyyyaard avatar

    Hoyyyaard/3DFlowAction

    56View on GitHub↗

    This repository contains PyTorch implementation for 3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model

    Python
    View on GitHub↗56
  • gboduljak/vfmfgboduljak avatar

    gboduljak/vfmf

    44View on GitHub↗

    Gabrijel Boduljak | Yushi Lan | Christian Rupprecht | Andrea Vedaldi

    Jupyter Notebook
    View on GitHub↗44
  • huang-yh/owlH

    huang-yh/Owl

    0View on GitHub↗

    $\dagger$ Project leader

    View on GitHub↗0
  • danijar/daydreamerdanijar avatar

    danijar/daydreamer

    445View on GitHub↗

    Official implementation of the DayDreamerpaper algorithm in TensorFlow 2.

    Jupyter Notebook
    View on GitHub↗445