Instructions to use YilaiLiu-HKU/SlotMem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Wan2.2
How to use YilaiLiu-HKU/SlotMem with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
SlotMem: Character-Addressable Internal Memory for Narrative Long Video Generation
Official checkpoints for SlotMem, a character-addressable internal memory framework for multi-character narrative long video generation. SlotMem represents recurring characters with compact role-wise memory slots, updates them during autoregressive generation, and injects them only into localized character-relevant tokens.
- Repository: YilaiLiu-HKU/SlotMem
- Paper: coming soon
Checkpoints
This repository provides the SlotMem LoRA checkpoints for the two-stage model:
ckpt/
stage1/
stage1_low.pt
stage1_high.pt
stage2/
stage2_low.pt
stage2_high.pt
Due to limited training data scaling, we strongly recommend training SlotMem checkpoints on your own target data before running final inference.
Download all checkpoints:
huggingface-cli download YilaiLiu-HKU/SlotMem --local-dir . --include "ckpt/*"
Usage
Download the SlotMem checkpoints into the code repository:
huggingface-cli download YilaiLiu-HKU/SlotMem --local-dir . --include "ckpt/*"
For training, data curation, benchmarking, and sample data format, please refer to the GitHub repository.
Citation
The BibTeX entry will be updated when the paper is public.
Model tree for YilaiLiu-HKU/SlotMem
Base model
Wan-AI/Wan2.2-I2V-A14B