Echo-Team/Echo-Memory
2
Echo-Memory — Wan 2.1 1.3B memory checkpoints
Validated 30,000-step fine-tunes for Echo-Memory, built on Wan-AI/Wan2.1-T2V-1.3B.
Paper · Project page · Code · **Interactive demo** · Dataset
Try the model in the browser (ZeroGPU, no local GPU): [hugging-apps/echo-memory](https://huggingface.co/spaces/hugging-apps/echo-memory)
Available checkpoints
MoC, geometry-grounded spatial memory, legacy SSM, and VideoSSM weights are not part of this validated release.
Download
huggingface-cli download Echo-Team/Echo-Memory \
block_wise_ssm_causal_v2/epoch-0.safetensors \
framepack_len_r8/epoch-0.safetensors \
--local-dir ./ckptsKeep the row folder in the local path. Echo-Memory auto-detects the profile and restores the trained context order, FramePack strategy, and camera-RT continuation.
Multi-chunk inference
export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B
export CONTEXT_IMAGE=/path/to/first_frame.png
export ACTION_PATH=/path/to/81_frame_camera_action.json
CKPT=./ckpts/block_wise_ssm_causal_v2/epoch-0.safetensors NUM_CHUNKS=4 \
bash inference/memory_baselines_basic/run_infer_block_wise_ssm_causal_v2.sh
CKPT=./ckpts/framepack_len_r8/epoch-0.safetensors NUM_CHUNKS=4 \
bash inference/memory_baselines_basic/run_infer_framepack_len_r8.shCitation
@article{king2026echomemory,
title={Echo-Memory: A Controlled Study of Memory in Action World Models},
author={King, Wayne and Xue, Zeyue and Bian, Yuxuan and Huang, Jie and Li, Haoran and Li, Yaowei and Su, Yaofeng and Li, Yuming and Wang, Haoyu and Zhang, Shiyi and Zhang, Songchun and Niu, Yuwei and Xu, Sihan and Zhuang, Junhao and Huang, Haoyang and Duan, Nan},
journal={arXiv preprint arXiv:2606.09803},
year={2026}
}