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base_model:
- Wan-AI/Wan2.1-T2V-1.3B
language:
- en
license: apache-2.0
pipeline_tag: text-to-video
---
# DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory
DecMem is a decoupled memory architecture designed for consistent, long-horizon world generation. It employs **Sparse Global Memory** for efficient fine-grained access to global history and **Anchored Local Memory** for stable and high-quality extrapolation. This approach enables minute-level controllable long video generation with high fidelity and consistency.
[**Project Page**](https://jeffreyyzh.github.io/DecMem-Page/) | [**Paper**](https://arxiv.org/abs/2605.31336) | [**Code**](https://github.com/KlingAIResearch/DecMem)
## Checkpoints
Download the Wan2.1 backbone (VAE + tokenizer weights used by the pipeline):
```bash
huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B \
--local-dir-use-symlinks False \
--local-dir wan_models/Wan2.1-T2V-1.3B
```
Download DecMem trained checkpoints:
```bash
huggingface-cli download KlingTeam/DecMem --local-dir checkpoints
```
Checkpoint layout expected by training / inference scripts:
```
checkpoints/
└── decmem.pt # released weights
```
## Quick start
We provide example video-pose pairs for quick inference. The inference is performed in a block-by-block causal denoising manner with KV cache.
To run the inference, follow the installation instructions in the [official repository](https://github.com/KlingAIResearch/DecMem) and run:
```bash
bash scripts/infer_example.sh
```
## Citation
If you find our work helpful, please cite our paper:
```bibtex
@misc{yang2026decmemminutelongconsistentworld,
title={DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory},
author={Zhenhao Yang and Xiaoshi Wu and Zhengyao Lv and Xiaoyu Shi and Xintao Wang and Pengfei Wan and Kun Gai and Kwan-Yee K. Wong},
year={2026},
eprint={2605.31336},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2605.31336},
}
``` |