---
title: "ModelScopeT2V"
description: "ModelScopeT2V from Alibaba (DAMO Academy / ModelScope): text-to-video; Runner parity pending."
modelId: "modelscope-t2v"
pageSource: authored
---
ModelScopeT2V is Alibaba DAMO Academy's original open text-to-video diffusion model — the 1.7B model that kicked off much of the open T2V ecosystem (ZeroScope in this catalog is a fine-tune of it). The WorldFoundry route is complete and reports missing checkpoints at runtime. It is kept mainly as a historical baseline.
The recorded paper is [arXiv 2308.06571](https://arxiv.org/abs/2308.06571).
Project page: [modelscope.cn](https://modelscope.cn/). Code: [modelscope/modelscope](https://github.com/modelscope/modelscope).
Architecturally it is a **latent video U-Net**: temporal attention is inserted into a text-to-image diffusion backbone so a prompt becomes a short, low-resolution clip. That is why later DiT families (Wan, CogVideoX, HunyuanVideo) superseded it on quality and temporal consistency. The Hugging Face card is `ali-vilab/modelscope-damo-text-to-video-synthesis`. Recorded license is **CC-BY-NC-4.0** — the manifest says not to treat it as unrestricted open source.
## What it is for
`modelscope-t2v` is the recorded prompt-to-video route — not image editing and not a policy. Start here when the job is a clip from text.
A U-Net latent video diffusion model with temporal attention layers inserted into a text-to-image backbone (per the upstream publication). This repository records the prompt-only input contract and generated_video artifact. It is published by Alibaba (DAMO Academy / ModelScope).
Catalog tasks include text-to-video. Documented modalities: text in, video out.
Recorded evaluation entry points include [FETV](/docs/evaluation/benchmark-hub/fetv) (Fine-grained T2V evaluation suited to legacy baselines).
Reproduce a 2023-era open T2V baseline: required `prompt` in, `modelscope-t2v.mp4` out through `ModelScopeT2VPipeline`. The wired controls are text prompt, **fps**, and **seed**. Use it to compare against later entries or to explain ZeroScope's parent. For research-quality or production T2V, use the DiT cards. [FETV](/docs/evaluation/benchmark-hub/fetv) is the fine-grained suite this generation of models is usually discussed on.
## Runtime contract
Optional `prompt`, `negative_prompt`, `frames`, `fps`, and `height`; artifacts `modelscope-t2v.mp4`.
CLI task profiles: `interactive-video`. WorldFoundry binds `ModelScopeT2VPipeline`.
Family status: **Runner parity pending**.
The recorded license is non-commercial per HF cardData — the manifest says explicitly not to treat it as unrestricted open source. Use it for research comparisons only; for production-quality T2V use the DiT-based entries.
Checkpoint license is non-commercial per HF cardData; do not treat as unrestricted open source.
## Running in WorldFoundry
Launch `worldfoundry-eval run modelscope-t2v --pipeline.task-profile interactive-video` to sample the clip. The command builder above switches `--pipeline.task-profile` among recorded tasks and still emits `worldfoundry-eval run`.
It runs in the unified environment `worldfoundry-unified-cu128` (Python 3.11, CUDA 12.8).
Recorded weight repositories include `ali-vilab/modelscope-damo-text-to-video-synthesis` and `ali-vilab/modelscope-damo-text-to-video-synthesis`. Prepare the environment and check local assets with `bash scripts/inference/prepare_model_infer.sh modelscope-t2v`. Add `--download` to fetch missing public checkpoints.
Recorded licenses include cc-by-nc-4.0; status: **Runner parity pending**.