Les fonctionnalités principales de lightricks/ltx-video sont : Foundation Models, Video Generation, Video Generation Models.
Les alternatives open-source à lightricks/ltx-video incluent : stepfun-ai/step-video-t2v —    . tencent/hunyuanvideo. pku-yuangroup/open-sora-plan — Open-Sora-Plan is a text-to-video framework and distributed video training system. It utilizes a diffusion transformer… skyworkai/skyreels-v2 — SkyReels-V2 is a video generation system that creates, extends, and refines video clips from text descriptions,… hpcaitech/open-sora — Open-Sora is a video generation framework designed to produce cinematic sequences from text prompts and images. It… wan-video/wan2.1 — Wan2.1 is a generative video synthesis framework that provides foundation models for creating high-fidelity video…
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
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
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
