| # DeMemWM |
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| This repo is forked from [Boyuan Chen](https://boyuan.space/)'s research template [repo](https://github.com/buoyancy99/research-template). By its MIT license, you must keep this sentence in `README.md` and the `LICENSE` file to credit the author. |
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| DeMemWM is a Memory-DiT video prediction project built on the local research template. The primary algorithm entry point is `DeMemWMMinecraft`, registered through the Hydra algorithm config `dememwm_memory_dit`. |
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| ## Quick Start |
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| ```bash |
| python -m venv .venv |
| source .venv/bin/activate |
| pip install -r requirements.txt |
| python -m pytest tests |
| ``` |
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| Run a local offline experiment after setting the dataset path in `configurations/dataset/video_minecraft.yaml`: |
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| ```bash |
| python main.py +name=dememwm_debug algorithm=dememwm_memory_dit wandb.mode=offline |
| ``` |
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| Use `resume_ckpt_path=/path/to/checkpoint.ckpt` for deterministic checkpoint resume, or keep `auto_resume=true` to resume from `output_dir/checkpoints` when available. |
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| ## Layout |
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| - `algorithms/worldmem/dememwm/`: DeMemWM memory construction, retrieval, scheduling, diagnostics, and injection code. |
| - `algorithms/worldmem/dememwm_memory_dit.py`: primary DeMemWM algorithm class. |
| - `configurations/algorithm/dememwm_memory_dit.yaml`: consumed DeMemWM training and evaluation contract. |
| - `scripts/`: Slurm and inspection scripts using the DeMemWM naming. |
| - `tests/`: static and unit coverage for DeMemWM config, retrieval, compression, schedules, and training behavior. |
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| ## Reproducibility Notes |
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| - Keep `wandb.mode=offline` for local reproducible runs that do not depend on network access. |
| - Set `seed=<int>` on the command line to seed Lightning and dataloader workers. |
| - Runtime artifacts such as `outputs/`, `slurm_logs/`, Python caches, checkpoints, and local datasets are ignored by git. |
| - The default Hydra training config selects `algorithm: dememwm_memory_dit`. |
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