---
title: "UniAnimate-DiT"
description: "UniAnimate-DiT from Alibaba (Tongyi Lab): human-image-animation and image-to-video; Runner parity pending."
modelId: "unianimate-dit"
pageSource: generated
---

UniAnimate-DiT is Alibaba ali-vilab's human image animation model built on Wan2.1-I2V: it animates a person photo to follow a driving pose sequence. The in-tree route is wired with local checkpoint assets and exposes max_frames, resolution, step count, and seed controls.

<ModelPaperFigures modelId="unianimate-dit" locale="en" />

The recorded paper is [arXiv 2504.11289](https://arxiv.org/abs/2504.11289).

Code: [ali-vilab/unianimate-dit](https://github.com/ali-vilab/unianimate-dit).

<ModelCommandBuilder modelId="unianimate-dit" locale="en" />

## What it is for

Use `unianimate-dit` for the family's joint T2V/I2V inference path. The catalog records both task profiles on this page.

Built on the Wan2.1 image-to-video diffusion transformer with pose-sequence conditioning for human animation (checkpoint: ZheWang123/UniAnimate-DiT). Deeper conditioning internals are not recorded in this repository. It is published by Alibaba (Tongyi Lab).

Catalog tasks include human-image-animation, image-to-video, and video-generation. Documented modalities: image in, video out.

## Runtime contract

Optional `prompt`, `negative_prompt`, `input_path`, `frames`, and `fps`; artifacts `unianimate-dit.mp4`.

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

Family status: **Runner parity pending**.

Recorded `interactive-video` defaults: height=832, width=480, seed=0.

Prepare a clean subject photo and a driving pose sequence for best results.

## Running in WorldFoundry

The catalog launch is `worldfoundry-eval run unianimate-dit --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 `ZheWang123/UniAnimate-DiT`. Prepare the environment and check local assets with `bash scripts/inference/prepare_model_infer.sh unianimate-dit`. Add `--download` to fetch missing public checkpoints.

Status: **Runner parity pending**.

<ModelRelatedRecipes modelId="unianimate-dit" locale="en" />