Picsart-AI-Research/StreamingT2V

Health v2 · maintenance only

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1630 stars · 157 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

StreamingT2V is a research codebase (CVPR 2025) implementing an autoregressive technique that turns short text-to-video diffusion models like SVD and ModelScope into long, temporally consistent video generators. It supports generating videos of 200 to 1200+ frames with smooth transitions, rich motion, and text/image alignment.

Use cases

• generate long videos from a text prompt
• extend a short text-to-video model to minutes-long output
• generate video from an image with consistent motion
• produce temporally consistent AI video without hard cuts
• run diffusion-based video generation on a single GPU
• experiment with autoregressive video diffusion research

When to choose

• you need long (8s to 2min+) AI-generated videos with smooth transitions
• you want to build on SVD or ModelScope as a base video model
• you have a CUDA GPU with at least 24 GB VRAM and can run Python research code
• you're reproducing or extending CVPR 2025 video generation research

When to avoid

• you need a production-ready product with a polished UI or API
• you only need short 16-24 frame clips, where standard T2V models suffice
• you lack a high-VRAM GPU (default config needs 60 GB)
• you require a permissive or clearly defined license - the repo has none
• you need real-time or low-latency video generation

Facets

library · maturity active

video-processing machine-learning deep-learning llm-inference artificial-intelligence deep-learning image-processing python text-to-video image-to-video diffusion-models long-video-generation autoregressive stable-video-diffusion cvpr-2025 research-code huggingface video linux gpu

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