Instructions to use zhongzero/outdreamer_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zhongzero/outdreamer_model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zhongzero/outdreamer_model", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| license: bsd-2-clause | |
| base_model: | |
| - LanguageBind/Open-Sora-Plan-v1.2.0 | |
| library_name: diffusers | |
| tags: | |
| - OutDreamer | |
| - video-outpainting | |
| - diffusion-transformer | |
| - DiT | |
| # OutDreamer checkpoint for video outpainting | |
| This repository provides the OutDreamer checkpoint for **OutDreamer: Video Outpainting with a Diffusion Transformer**. | |
| OutDreamer is a DiT-based video outpainting framework designed to extend video content beyond the original frame boundaries while maintaining spatial and temporal consistency. The model introduces an efficient video control branch, a conditional outpainting branch, mask-driven self-attention, latent alignment loss, and a cross-video-clip refiner for long video outpainting. | |
| The method and its results are detailed in the arXiv paper: [OutDreamer: Video Outpainting with a Diffusion Transformer](https://arxiv.org/abs/2506.22298). | |
| ## How to Use | |
| **Important:** This checkpoint is intended to be used with the OutDreamer codebase and is not a standalone Hugging Face pipeline. | |
| For project details, please refer to the OutDreamer GitHub repository: [zhongzero/OutDreamer](https://github.com/zhongzero/OutDreamer) | |
| For setup and inference scripts compatible with this checkpoint, please refer to the reproduction repository: [zhongzero/OutDreamer-unofficial](https://github.com/zhongzero/OutDreamer-unofficial) | |
| ## Citation | |
| If you find this work helpful for your research, please cite: | |
| ```BibTeX | |
| @article{zhong2026outdreamer, | |
| title={Outdreamer: Video outpainting with a diffusion transformer}, | |
| author={Zhong, Linhao and Li, Fan and Huang, Yi and Liu, Jianzhuang and Pei, Renjing and Song, Fenglong}, | |
| journal={IEEE Transactions on Image Processing}, | |
| year={2026}, | |
| publisher={IEEE} | |
| } | |
| ``` | |