metadata
license: other
license_name: flux-dev-non-commercial-and-component-licenses
library_name: imagewam
pipeline_tag: image-to-image
tags:
- robotics
- world-model
- vision-language-action
- action-prediction
- flux2
datasets:
- InternRobotics/InternData-A1
base_model:
- black-forest-labs/FLUX.2-klein-base-4B
- black-forest-labs/FLUX.2-dev
ImageWAM FLUX.2 4B — InternData-A1 EE Pretrained
Model purpose and architecture
This is an ImageWAM FLUX.2 Klein 4B checkpoint pretrained on InternData-A1 with a 16-dimensional end-effector action representation. It is intended as an initialization model for downstream robot-policy fine-tuning.
ImageWAM jointly predicts a future image and a future action chunk from camera observations, a language instruction, and robot proprioception. Its FLUX.2 video expert and ActionDiT action expert interact through a Mixture-of-Transformers (MoT) architecture.
Key parameters
| Item | Value |
|---|---|
| Video expert | FLUX.2 Klein 4B |
| Action expert | ActionDiT |
| Pretraining data | InternRobotics/InternData-A1 |
| Action dimension | 16 |
| Proprioception dimension | 16 |
| Maximum action horizon | 64 |
| Text encoder | Qwen/Qwen3-4B |
| Text feature dimension | 7,680 |
| Precision | bfloat16 |
Included weights
model.safetensors contains:
- the FLUX.2 video expert;
- the ActionDiT action expert;
- the MoT layers;
- the proprioception encoder; and
- the FLUX.2 autoencoder (VAE).
Qwen3 weights, dataset normalization statistics, and optimizer state are not included.
Citation
@misc{zhang2026imagewam,
title={ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?},
author={Yuyang Zhang and Wenyao Zhang and Zekun Qi and He Zhang and Haitao Lin and Jingbo Zhang and Yao Mu and Xiaokang Yang and Wenjun Zeng and Xin Jin},
year={2026},
eprint={2606.19531},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2606.19531}
}