README.md

---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: image-to-video
library_name: wan2.2
model_name: "ReMind 5B — Teaching Video Generators to Remember: Eliciting Dynamic Memory for Out-of-Sight State Evolution"
base_model: Wan-AI/Wan2.2-TI2V-5B
base_model_relation: finetune
tags:
- remind
- remind-5b
- video-generation
- image-to-video
- world-model
- video-world-model
- causal-video-generation
- dynamic-memory
- out-of-sight-state-evolution
- teaching-video-generators-to-remember
- wan2.2
- lora
- arxiv:2605.25333
---

# ReMind 5B — Teaching Video Generators to Remember

Official ReMind 5B checkpoints for **Teaching Video Generators to Remember:
Eliciting Dynamic Memory for Out-of-Sight State Evolution**
([arXiv:2605.25333](https://arxiv.org/abs/2605.25333)).

ReMind post-trains a causal video generator to preserve and evolve hidden
world state through camera motion, occlusion, and illumination changes.

[Project page](https://remind-applied.github.io/) ·
[Paper](https://arxiv.org/abs/2605.25333) ·
[Code](https://github.com/Applied-Intuition-Open-Source/ReMind) ·
[Dataset](https://huggingface.co/datasets/AppliedIntuitionResearch/ReMind1M) ·
[ReMind Collection](https://huggingface.co/collections/AppliedIntuitionResearch/remind-teaching-video-generators-to-remember-6a6cd1ff7e9a063abb7cf5eb)

## Release status

| Model | Status |
|---|---|
| ReMind 5B | Available in this repository |
| ReMind 1.3B | Coming soon |

This repository currently releases only the validated ReMind 5B weights. It
does not contain ReMind 1.3B weights.

## Files

| File | Size | Purpose |
|---|---:|---|
| `ReMind-5B.safetensors` | 10.37 GiB | Complete ReMind-5B generator |
| `ReMind-5b-dmd-ema.safetensors` | 1.38 GiB | Rank-128 SF-DMD EMA student LoRA |

The DMD EMA file is not a standalone model. Load the official
`Wan-AI/Wan2.2-TI2V-5B` model, overlay the ReMind-5B generator, cast it to
BF16, and then merge the ReMind DMD EMA LoRA.

Only inference weights are included. Optimizer state, gradients, critics, raw
student adapters, schedulers, and training-state checkpoints are excluded.

## Setup

Clone and install the public ReMind code:

```bash
git clone https://github.com/Applied-Intuition-Open-Source/ReMind.git
cd ReMind
python -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install -r requirements.txt
pip install -e .
```

Download the official Wan 2.2 TI2V 5B model in its original repository
layout:

```bash
hf download Wan-AI/Wan2.2-TI2V-5B \
  --local-dir checkpoints/Wan2.2-TI2V-5B
```

Place the two ReMind files together, for example under
`checkpoints/ReMind-5B/`, and run one of the seven bundled presets:

```bash
python inference.py \
  --preset examples/presets/01_latte_occluder_recovery.yaml \
  --config configs/model_5b.yaml \
  --model-folder checkpoints/Wan2.2-TI2V-5B \
  --base-checkpoint checkpoints/ReMind-5B/ReMind-5B.safetensors \
  --ema-checkpoint checkpoints/ReMind-5B/ReMind-5b-dmd-ema.safetensors \
  --output outputs/latte_occluder_recovery.mp4
```

The public inference recipe uses 81 frames at 832×480 and 16 fps, seven
three-latent-frame chunks, and the shifted four-step schedule
`[1000, 937, 833, 625]`. Camera examples use the bundled pair-fixed GT camera
trajectories.

## Checkpoint lineage and validation

- ReMind-5B generator: validated pair-fixed 480p release checkpoint.
- DMD EMA adapter: last complete SF-DMD checkpoint 2500.
- The ReMind-5B checkpoint loads with 100% key coverage.
- All eleven public inference presets pass the released validation suite.

Integrity hashes are provided in `SHA256SUMS`.

## Intended use

ReMind is a research artifact for studying dynamic memory, controlled camera
motion, reversible visibility disturbances, and clean image-to-video
generation. The bundled examples demonstrate representative inputs and
controls; they are not a benchmark or a guarantee of behavior.

## Limitations

- Long or complex motion can accumulate autoregressive errors.
- Camera control is approximate and may not follow every requested path.
- Recovery depends on scene content, event timing, seed, and model size.
- Outputs can contain physical, geometric, identity, lighting, and temporal
  artifacts.
- The model should not be used for high-stakes decisions or to misrepresent
  generated media as real.

## License

The two ReMind weight files are licensed under Creative Commons
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). See `LICENSE` for
the full legal code.

The upstream Wan model is not included and remains governed by its own terms.
The public ReMind code and bundled third-party components are governed by the
licenses and notices in the code repository.

## Citation

```bibtex
@article{xu2026teaching,
  title={Teaching Video Generators to Remember: Eliciting Dynamic Memory for Out-of-Sight State Evolution},
  author={Xu, Tianshuo and Xie, Yichen and Meng, Depu and Peng, Chensheng and Herau, Quentin and Jiang, Bo and Hu, Yihan and Zhan, Wei},
  journal={arXiv preprint arXiv:2605.25333},
  year={2026}
}
```