LIFT by Shengxiang Ji: Open-Weight Model

Model Card

By Shengxiang Ji, published under apache-2.0, revision 38d444bd5dd0.

We introduce LIFT, a unified image-to-video generation framework that complements camera control with Layout-In-FuTure control, enabling users to specify what should appear in a future view and where it should appear. Our models are built on Wan2.1-Fun-V1.1-1.3B-Control-Camera.

Read Shengxiang Ji's full model card

LIFT: Layout-In-Future Video Generation under Large Viewpoint Change via On-Policy Self-Distillation

Given a first frame, users can navigate from the first-frame view along a desired camera path and specify layouts using bounding boxes with local text prompts in the final frame. Then, LIFT generates the intended shot that transitions from the input image to the user-defined last-frame layout following the prescribed camera trajectory.

We introduce LIFT, a unified image-to-video generation framework that complements camera control with Layout-In-FuTure control, enabling users to specify what should appear in a future view and where it should appear.

Model Checkpoints

Our models are built on Wan2.1-Fun-V1.1-1.3B-Control-Camera.

Model	Description
`LIFT/transformer/`	Last-frame layout student trained by dual-mode on-policy self-distillation from the dense-layout teacher.
`LIFT_dense_layout_teacher/transformer/`	Dense-layout teacher fine-tuned with dense per-frame layout

Download

pip install -U "huggingface_hub[cli]"
# Last-frame layout student (dual-mode OPSD)
hf download Overdog/LIFT --include "LIFT/*" --local-dir models
# Dense-layout teacher
hf download Overdog/LIFT --include "LIFT_dense_layout_teacher/*" --local-dir models

Identity and Version

Repository: Overdog/LIFT
Publisher: Shengxiang Ji
Task: Image to video
Modality: Other
Library: diffusers
Parameters: Not stated by the source
Languages: Not stated by the source
Revision: 38d444bd5dd01707e0dc4a894681e293547a95ce
First published: 2026-09-29
Last updated: 2026-09-29

Files and Weights

7 files, 6.5 GB in total. The weights are 2 files totalling 6.5 GB in safetensors.

Weights2 files · 6.5 GB

Configuration2 files · 1.3 KB

Documentation1 file · 2.4 KB

Other1 file · 360.0 KB

Repository1 file · 1.6 KB

Every file

File	Type	Size	SHA-256
LIFT/transformer/diffusion_pytorch_model.safetensors	Weights	3.2 GB	c00810f75284
LIFT_dense_layout_teacher/transformer/diffusion_pytorch_model.safetensors	Weights	3.2 GB	7c8eaffd2d9d
LIFT/transformer/config.json	Configuration	635 B	—
LIFT_dense_layout_teacher/transformer/config.json	Configuration	635 B	—
README.md	Documentation	2.4 KB	—
assets/teaser.png	Other	360.0 KB	18a45b3b2cfe
.gitattributes	Repository	1.6 KB	—

License and Download

License: apache-2.0
Access: Open weights, no gate
Download size: 6.5 GB

Download from Shengxiang Ji

Released by Shengxiang Ji through its official repository on Hugging Face. Read the license.

Memory Requirements

Precision	Weights in memory
As published	6.5 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About LIFT

Can I use LIFT commercially?

Yes. LIFT is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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