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).
ReMind post-trains a causal video generator to preserve and evolve hidden
world state through camera motion, occlusion, and illumination changes.
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:
1git clone https://github.com/Applied-Intuition-Open-Source/ReMind.git
2cd ReMind
3python -m venv .venv
4source .venv/bin/activate
5pip install -U pip
6pip install -r requirements.txt
7pip install -e .
Download the official Wan 2.2 TI2V 5B model in its original repository
layout:
1hf download Wan-AI/Wan2.2-TI2V-5B \
2 --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:
1python inference.py \
2 --preset examples/presets/01_latte_occluder_recovery.yaml \
3 --config configs/model_5b.yaml \
4 --model-folder checkpoints/Wan2.2-TI2V-5B \
5 --base-checkpoint checkpoints/ReMind-5B/ReMind-5B.safetensors \
6 --ema-checkpoint checkpoints/ReMind-5B/ReMind-5b-dmd-ema.safetensors \
7 --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
1@article{xu2026teaching,
2 title={Teaching Video Generators to Remember: Eliciting Dynamic Memory for Out-of-Sight State Evolution},
3 author={Xu, Tianshuo and Xie, Yichen and Meng, Depu and Peng, Chensheng and Herau, Quentin and Jiang, Bo and Hu, Yihan and Zhan, Wei},
4 journal={arXiv preprint arXiv:2605.25333},
5 year={2026}
6}
