---
title: "VideoCrafter2 T2V"
description: "VideoCrafter2 T2V from Tencent AI Lab: text-to-video and video-generation; Runner parity pending."
modelId: "videocrafter2-t2v"
pageSource: authored
---

VideoCrafter2 T2V is the improved text-to-video model of the VideoCrafter family, trained to overcome low-quality video data by combining it with high-quality image data. It is Apache-2.0 on its HF card, making it the license-cleanest member of the family. The in-tree infer-only runtime exposes local checkpoint, fps, frame count, and seed controls.

<ModelPaperFigures modelId="videocrafter2-t2v" locale="en" />

The recorded paper is [arXiv 2401.09047](https://arxiv.org/abs/2401.09047).

Project page: [ailab-cvc.github.io/videocrafter2](https://ailab-cvc.github.io/videocrafter2/). Code: [VideoCrafter/VideoCrafter](https://github.com/VideoCrafter/VideoCrafter).

<ModelCommandBuilder modelId="videocrafter2-t2v" locale="en" />

## What it is for

`videocrafter2-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 whose training decouples motion (from video) and appearance (from images), per the upstream publication. Checkpoint provenance is tracked on the family entry. It is published by Tencent AI Lab.

Catalog tasks include text-to-video and video-generation. Catalog aliases include `videocrafter2_t2v`. Documented modalities: text in, video out.

Recorded evaluation entry points include [EvalCrafter](/docs/evaluation/benchmark-hub/evalcrafter) (Companion benchmark of the VideoCrafter effort), [VBench](/docs/evaluation/benchmark-hub/vbench) (Standard T2V suite).

Not [VideoCrafter](/docs/guides/supported-models/videocrafter) and [VideoCrafter1 T2V](/docs/guides/supported-models/videocrafter1-t2v). Score on [EvalCrafter](/docs/evaluation/benchmark-hub/evalcrafter) and [VBench](/docs/evaluation/benchmark-hub/vbench).

## Runtime contract

Optional `prompt`, `frames`, `fps`, `height`, and `width`; artifacts `generated_video` and `videocrafter2-t2v.mp4`.

CLI task profiles: `interactive-video`. WorldFoundry binds `VideoCrafter2T2VPipeline`.

Family status: **Runner parity pending**.

Runner parity is pending. This is the recommended VideoCrafter variant: better quality than V1 and a clean Apache-2.0 license.

Checkpoint provenance is tracked on the top-level videocrafter catalog entry to avoid duplicate HF repo aliases.

## Running in WorldFoundry

The catalog launch is `worldfoundry-eval run videocrafter2-t2v --pipeline.task-profile interactive-video`. 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 `VideoCrafter/VideoCrafter2`. Prepare the environment and check local assets with `bash scripts/inference/prepare_model_infer.sh videocrafter2-t2v`. Add `--download` to fetch missing public checkpoints.

Status: **Runner parity pending**.

<ModelRelatedRecipes modelId="videocrafter2-t2v" locale="en" />