A full-stack framework and tutorial for newcomers, rather than a specific model.

minWM is our contribution to the world-model community: a full-stack open-source framework that walks you end-to-end through turning a bidirectional T2V foundation model into an action-conditioned video world model — with example data, runnable scripts, Claude skills capturing our hands-on experience, and onboarding knowledge for newcomers. We hope more researchers and developers join us in growing the community together.

• 2026-05-29 🚀 We release the technical report.
• 2026-05-17 🚀 We release minWM — the first full-stack open-source world model framework.

• 🎬 Demo
• 🔥 News
• ✨ Why minWM?
  • 1. Full-Stack Framework
  • 2. Multi-Backbone Support
  • 3. Multi-Condition Injection
  • 4. Claude Skills — Modify the Framework with an LLM Assistant
  • 5. Onboarding Knowledge — for Newcomers to World Models
• 🛠️ Installation
• 🧱 Model Checkpoints
• 🚀 Quick Start
• ⚙️ Data & Training & Reproduction
• 📚 Citation
• Contact
• 🙏 Acknowledgements

The complete data → training → inference pipeline is open-sourced; every stage exposes input/output checkpoints so you can stop, swap, or fork anywhere.

1.1 Data. We walk you through how to construct training-ready datasets paired with camera poses, and the full data processing pipeline that turns them into latents.

1.2 Training. Including FSDP + sequence parallelism, single-/multi-node training, and the full distillation pipeline from a bidirectional diffusion model to a 4-step AR student:

Phase 1                            Phase 2 — Distillation to Causal Few-Step
─────────────────────              ────────────────────────────────────────────
Bidirectional SFT      ──▶   Stage 1   Teacher Forcing AR Diffusion
                             Stage 2a  Causal ODE  (proposed in [Causal Forcing](https://arxiv.org/abs/2602.02214))
                             Stage 2b  Causal CD   (proposed in [Causal Forcing++](https://arxiv.org/abs/2605.15141))
                             Stage 3   Asymmetric DMD with Self Rollout
                                                ▼
                                         4-step real-time

1.3 Inference.

• ✅ 4-step DMD inference for HY Action2V / HY TI2V / Wan Action2V, multi-GPU sequence parallelism, camera-trajectory control via pose strings ("a*4,w*8,s*7") or JSON files
• 🚧 Inference acceleration [TBD]

minWM supports two paths to arriving at an interactive world model.

The HunyuanVideo 1.5 and Wan 2.1 lines walk through the full 4-stage pipeline — starting from a bidirectional T2V foundation model and ending at a 4-step autoregressive world model.

Both lines share the same trainer / loss / dataset abstractions, so adding a third backbone is structurally a wrapper-and-config exercise.

The forthcoming worldplay-finetune entry will let you start from an already-trained video world model and adapt it to new conditions, scenes, or resolutions — without rerunning the 4-stage pipeline from scratch.

We aim to support both multiple condition types and multiple injection methods, mixable along either axis.

• ✅ Camera pose
• 🚧 Human pose [TBD]

• ✅ ProPE
• 🚧 Latent concat [TBD]
• 🚧 Cross-attention [TBD]

We are packaging our project experience across the CF / CF++ pipeline as Claude skills, so that an LLM assistant can help users debug failures and integrate new models without reverse-engineering the whole repo.

• 🐛 debug-world-model — collected failure modes from the training pipeline (loss NaN, frame-to-frame jitter, camera drift, memory attenuation, distillation collapse, …). Claude diagnoses likely root causes from your symptoms instead of guessing.
• 🔌 integrate-new-backbone — step-by-step recipe for plugging a new video DiT into minWM, grounded in the HunyuanVideo and Wan reference integrations — e.g. "look at how HY does teacher forcing here, do the same for your model there".

• onboarding-world-model

A third Claude skill aimed at researchers entering the world-model space for the first time. Two parts:

• 🎓 Foundations — the minimal background to follow the pipeline: Teacher Forcing for AR diffusion training and Causal Forcing & Causal Forcing++ for AR diffusion distillation.
• 🪤 Pitfalls — the non-obvious mistakes we hit while building minWM, distilled so you don't repeat them.

Intended audience: graduate students, independent researchers, and junior labs that want to enter the world-model space without spending three months reverse-engineering existing repos.

conda create -n minwm python=3.10 -y 
conda activate minwm
pip install -r requirements.txt
pip install flash-attn --no-build-isolation
export PYTHONPATH="$PWD/HY15:$PWD/Wan21:$PWD/shared:$PYTHONPATH"

The fastest path: install → download three DMD checkpoints → run three demo commands. Full reproduction (all 4 training stages × 3 model lines) is in § Data & Training & Reproduction.

# Wan base (T2V-1.3B)
hf download Wan-AI/Wan2.1-T2V-1.3B --local-dir ./ckpts/Wan2.1-T2V-1.3B 

# Code hardcodes the load path; create a symlink.
mkdir -p Wan21/wan_models
ln -s "$(realpath ./ckpts/Wan2.1-T2V-1.3B)" Wan21/wan_models/Wan2.1-T2V-1.3B


# HY base + text/vision encoders (required by HY pipelines)
hf download tencent/HunyuanVideo-1.5 --local-dir ./ckpts/HunyuanVideo-1.5 \
    --include "vae/*"  "scheduler/*" "transformer/480p_i2v/*"
hf download Qwen/Qwen2.5-VL-7B-Instruct --local-dir ./ckpts/HunyuanVideo-1.5/text_encoder/llm
hf download google/byt5-small           --local-dir ./ckpts/HunyuanVideo-1.5/text_encoder/byt5-small
modelscope download --model AI-ModelScope/Glyph-SDXL-v2 \
    --local_dir ./ckpts/HunyuanVideo-1.5/text_encoder/Glyph-SDXL-v2
hf download black-forest-labs/FLUX.1-Redux-dev \
    --local-dir ./ckpts/HunyuanVideo-1.5/vision_encoder/siglip --token <your_hf_token>


# 4-step DMD checkpoints
## Wan Action2V (DMD, 4-step)
hf download MIN-Lab/minWM --local-dir ./ckpts \
    --include "Wan21/Action2V/dmd/*"

## HY Action2V (DMD, 4-step, worldplay teacher) 
hf download MIN-Lab/minWM --local-dir ./ckpts \
    --include "HY15/Action2V/dmd/*"

# HY Action2V (DMD, 4-step, our bidirectional teacher) 
# hf download MIN-Lab/minWM --local-dir ./ckpts \
#     --include "HY15/Action2V/dmd_ourbi/*"

## HY TI2V (DMD, 4-step)
hf download MIN-Lab/minWM --local-dir ./ckpts \
    --include "HY15/TI2V/dmd/*"

# 2.1  Wan Action2V (4-step DMD, camera control)
OUTPUT_FOLDER=./outputs/quickstart_wan_action2v \
TRAJECTORY_PATH="Wan21/prompts/trajectories.txt" \
    bash Wan21/scripts/inference/run_infer_causal_camera.sh

# 2.2  HY Action2V (4-step DMD, camera control)
TRANSFORMER_DIR=./ckpts/HY15/Action2V/dmd \
OUTPUT_DIR=./outputs/quickstart_hy_action2v \
    bash HY15/scripts/inference/run_infer_causal_camera.sh

# 2.3  HY TI2V (4-step DMD)
TRANSFORMER_DIR=./ckpts/HY15/TI2V/dmd \
OUTPUT_DIR=./outputs/quickstart_hy_ti2v \
    bash HY15/scripts/inference/run_infer_causal.sh

Camera control. For HY Action2V, trajectories are read per-sample from assets/example.json under the "trajectory" field. Format: w/s/a/d keys with *N repeats; comma-separated segments — e.g. "a*4,w*8,s*7".

Three model lines × two phases × four stages, each documented as (1) Model download → (2) Data preparation → (3) Training script → (4) Validation. Full reproduction guides are split by backbone:

• 📗 training_wan.md
  • Wan Action2V (Wan 2.1 backbone)
• 📘 training_hunyuan.md — HY Action2V (HY1.5-8B backbone)
  • HY TI2V (HY1.5-8B backbone)

If minWM helps your research, please cite:

# ICML 2026
@article{zhu2026causal,
  title={Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation},
  author={Zhu, Hongzhou and Zhao, Min and He, Guande and Su, Hang and Li, Chongxuan and Zhu, Jun},
  journal={arXiv preprint arXiv:2602.02214},
  year={2026}
}

# Technical Report
@article{zhao2026causal,
  title={Causal Forcing++: Scalable Few-Step Autoregressive Diffusion Distillation for Real-Time Interactive Video Generation},
  author={Zhao, Min and Zhu, Hongzhou and Zheng, Kaiwen and Zhou, Zihan and Yan, Bokai and Li, Xinyuan and Yang, Xiao and Li, Chongxuan and Zhu, Jun},
  journal={arXiv preprint arXiv:2605.15141},
  year={2026}
}

# Technical Report
@article{zhao2026minwm,
  title={minWM: A Full-Stack Open-Source Framework for Real-Time Interactive Video World Models},
  author={Zhao, Min and Zhu, Hongzhou and Yan, Bokai and Zhou, Zihan and Chen, Yimin and Sun, Wenqiang and Zheng, Kaiwen and He, Guande and Yang, Xiao and Li, Chongxuan and others},
  journal={arXiv preprint arXiv:2605.30263},
  year={2026}
}

For questions, suggestions, or collaboration, please open a GitHub issue or contact: gracezhao1997@gmail.com.

minWM stands on the shoulders of giants. We thank the authors and maintainers of HunyuanVideo 1.5, HY-WorldPlay, Wan 2.1, Causal-Forcing, and FastVideo for their open-source contributions, which made this framework possible.