We present SolarWM, a fully open foundation for building interactive video
world models from data preparation through scalable training and long-horizon
inference.
- Open, reconfigurable data infrastructure. SolarWM converts 1.43 million
canonical clips from 14 datasets into a unified, frame-aligned contract for
observations, metric camera geometry, captions, quality metadata, selection,
and provenance. Source processing is decoupled from training-mixture design.
- A scalable, backbone-native model family. One framework supports four
5B–33B models across Wan2.2, LTX-2.5, and MiniMax-H3 while preserving each
backbone's native representation and objective.
- A simple three-stage training recipe. Bidirectional adaptation,
teacher-forced autoregressive initialization, and distribution matching
distillation form a shared route across heterogeneous video backbones,
without specialized ODE or consistency-distillation initialization.
- Long-horizon interaction from short training clips. After training only
on 5-second sequences, the resulting causal models support real-time
interaction with rollouts spanning minutes to hours, without long-sequence
fine-tuning or attention-sink mechanisms.
News
- September 3, 2026 — We open-source the training and inference code, the
complete dataset, the data pipeline, model weights for all SolarWM-5B training
stages, and bidirectional weights for SolarWM-14B, SolarWM-LTX, and SolarWM-H3.
Training progression
The staged workflow is Stage0.5 FM → Stage1 TF-AnyFlow → Stage2 DMD via
SGF, turning a bidirectional video model into a camera-controlled few-step
autoregressive model.
- Stage0.5 learns full-clip bidirectional flow matching and establishes the
base video, text, and camera-conditioned representation.
- Stage1 combines teacher forcing with the AnyFlow loss in one training
stage. Clean history conditions noisy target chunks while the model learns
both denoising and finite-step flow maps. This removes the need for a separate
ODE or consistency-distillation initialization before Stage2/DMD.
- Stage2 performs DMD via self-gradient forcing (SGF), training the causal
student on its own autoregressive rollout with a frozen teacher and a
trainable critic.
| Backend |
Stage0.5 (Bid-Cam) |
Stage1 (TF-AnyFlow) |
Stage2 (SGF) |
Runtime interfaces |
| Wan2.2-5B |
✓ |
✓ |
✓ |
train, infer, preencode |
| Wan2.2-14B |
✓ |
Coming soon |
Coming soon |
train, infer, preencode |
| LTX-2.5 |
✓ |
Coming soon |
Coming soon |
train, infer, preencode |
| MiniMax-H3 |
✓ |
Coming soon |
Coming soon |
train, infer, preencode |
Install
Wan, LTX, and MiniMax-H3 require
separate runtime environments. Activate the
environment for the selected backbone, then install the shared SolarWM source:
python -m pip install -e .
solarwm environment probe
Model weights are available from the
SolarWM model collection. See the
Wan2.2 TI2V-5B guide for Stage0.5, Stage1,
and Stage2 commands. Data access options are described below.
Data
The public
SolarWM-Data release
contains release controls, licenses, recipe and test indexes, small format
examples, and the SolarWM-Data-Annotation/ package. It does not include
the full releases-v1/raw-wds/ or releases-v1/latent-wds/ payloads.
For a released recipe that uses preencoded data, download its matching latent
generation. That is sufficient for training and does not require raw-WDS.
Raw-WDS is needed only when you want the full processed video corpus, online
encoding, your own latent generation, or another workflow whose index points
to raw data.
- Use preencoded latents. Each latent generation is published in a
separate repository. See the latent-WDS release list
for available downloads and generations that are still being uploaded.
- Rebuild raw-WDS from annotations.
SolarWM-Data-Annotation/ is an
annotation-only release with no videos. It contains the released camera
trajectories, captions, metadata, source identities, and reconstruction
tools. Follow its README to download the original videos from their source
publishers and process them into the expected raw-wds/ layout.
- Request prepared raw-WDS. Submit the
Dataset Access Form.
Approved applicants receive download instructions by email.
See the dataset access guide for download commands and
the payload required by each training, validation, and inference example.
Unified commands
# Validate and render the exact resolved configuration.
solarwm config resolve \
--config configs/examples/wan22_ti2v_5b/train_stage0p5_fm_153f.yaml \
--set model.base_path=/path/to/SolarWM-models/SolarWM-5B-base \
--set checkpoint.path=/path/to/SolarWM-models/SolarWM-5B-bid-stage0p5-81f/model.pt \
--set data.index_root=/path/to/SolarWM-Data/releases-v1 \
--set data.transport.root=/path/to/SolarWM-Data/releases-v1 \
--set runtime.validate_every=0 \
--set runtime.output_dir=/path/to/output
# Train the released preencoded recipe without raw-WDS.
torchrun --standalone --nproc-per-node=8 \
-m solarwm train \
--config configs/examples/wan22_ti2v_5b/train_stage0p5_fm_153f.yaml \
--set distributed.world_size=8 \
--set train.global_batch_size=8 \
--set model.base_path=/path/to/SolarWM-models/SolarWM-5B-base \
--set checkpoint.path=/path/to/SolarWM-models/SolarWM-5B-bid-stage0p5-81f/model.pt \
--set data.index_root=/path/to/SolarWM-Data/releases-v1 \
--set data.transport.root=/path/to/SolarWM-Data/releases-v1 \
--set runtime.validate_every=0 \
--set runtime.output_dir=/path/to/output
# Run Wan2.2-5B Stage2 inference for the longest camera-backed horizon.
torchrun --standalone --nproc-per-node=1 -m solarwm infer \
--config configs/examples/wan22_ti2v_5b/infer_stage2_sgf_camera_length.yaml \
--set model.base_path=/path/to/SolarWM-models/SolarWM-5B-base \
--set checkpoint.path=/path/to/SolarWM-models/SolarWM-5B-sgf-stage2-81f \
--set data.index_root=/path/to/SolarWM-Data/releases-v1 \
--set data.transport.root=/path/to/SolarWM-Data/releases-v1 \
--set inference.run_id=my-camera-run \
--set runtime.output_dir=/path/to/output
# Preencode raw-WDS for the Wan2.2-5B 153f recipe.
torchrun --standalone --nproc-per-node=8 \
-m solarwm preencode \
--config configs/examples/wan22_ti2v_5b/preencode_153f.yaml \
--set model.base_path=/path/to/SolarWM-models/SolarWM-5B-base \
--set data.index_root=/path/to/wan153f-fixed-window-index \
--set data.transport.root=/path/to/SolarWM-Data/releases-v1 \
--set preencode.output_root=/path/to/latent-wds/wan22-ti2v5b-153f-480p-v1 \
--set preencode.logical_output_root=/path/to/recipes/wan22-ti2v5b-153f-480p-v1 \
--set runtime.output_dir=/path/to/output
The quickstart walks through a complete Wan2.2-5B setup.
Copyable commands for every released route are in the backend guides.
Every launch writes resolved-config.json and launch-manifest.json before
model allocation. Config overrides are explicit and included in the resolved
configuration identity.
Local and bucket data
Index rows always contain POSIX shard keys relative to the release directory:
{"sample_id":"...","shard":"raw-wds/abot/shards/kept-high-000001.tar"}
Only the runtime root changes:
# Locally mounted storage
data:
index_root: /path/to/SolarWM-Data/releases-v1
transport:
kind: local
root: /path/to/SolarWM-Data/releases-v1
# Object-store streaming uses the same release-relative rows. Supply the
# release root from the distribution channel or deployment environment.
data:
index_root: /path/to/SolarWM-Data/releases-v1
transport:
kind: gcs
root: ${SOLAR_RELEASE_ROOT}
cache_dir: /path/to/solar-cache
cache_max_gib: 256
Documentation
Acknowledgements
We gratefully thank the teams behind
Wan2.2,
LTX-2.5, and
MiniMax-H3 for releasing the code
and pretrained models that make the SolarWM backbone family possible. We also
thank the authors of all datasets and open-source projects listed in
NOTICE.
Citation
If you use SolarWM-Data, the data engine, or the released models in your research,
please cite our paper.
Paper: https://arxiv.org/abs/2609.02886
@misc{huang2026solarwmopendatascalable,
title={SolarWM: Open Data and Scalable Training for Long-Horizon Video World Models},
author={Junchao Huang and Guian Fang and Shengju Qian and Xianghao Kong and Zhuoran Zhao and Wei Huang and Yihua Du and Zixin Zhang and Justin Cui and Yuchao Gu and Yukang Chen and Xinting Hu and Tianyu He and Shaoshuai Shi and Zhuotao Tian and Xin Wang and Mike Zheng Shou and Li Jiang},
year={2026},
eprint={2609.02886},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2609.02886},
}
License and attribution
SolarWM is licensed under Apache-2.0. See LICENSE and
NOTICE. Model weights and backbone packages may carry their own
licenses; review the license and model card in the corresponding release
package before use or redistribution. In particular, LTX-2.5 derivatives are
subject to the LTX-2.x Community License, and the MiniMax H3 Community License
contains territory restrictions. Those packages are not relicensed under the
SolarWM code license.
SolarWM 是一个完全开放的构建交互式视频世界模型的基础框架，涵盖数据准备、可扩展的多主干训练和长时程推理。