Create README.md

README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+  - robotics
+  - imitation-learning
+  - world-model
+  - image-editing
+  - flux
+  - imagewam
+  - pretraining
+datasets:
+  - InternRobotics/InternData-A1
+---
+
+# ImageWAM-FLUX.2-4B-InternData-A1-EE
+
+This repository contains the **ImageWAM FLUX.2 4B checkpoint pretrained from InternData-A1** from **ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?**
+
+ImageWAM is a family of world action models built on image-editing foundation models. This checkpoint is intended for evaluation and research use with the accompanying ImageWAM codebase.
+
+## Model Details
+
+- **Model family:** ImageWAM
+- **Image-editing backbone:** FLUX.2 [klein] base
+- **Variant:** FLUX.2 klein-base-4B
+- **Benchmark:** None, pretrained model.
+- **Training code:** [yuyangalin/ImageWAM](https://github.com/yuyangalin/ImageWAM)
+- **Base model weights:** Users must separately prepare the FLUX.2 klein-base-4B weights and FLUX.2 autoencoder as described in the ImageWAM README.
+
+## Files
+
+Expected file layout:
+
+```text
+.
+├── model.pt
+├── dataset_stats.json
+└── config.yaml
+```
+
+- `model.pt`: ImageWAM checkpoint used by the evaluation scripts.
+- `dataset_stats.json`: normalization statistics required for policy evaluation.
+- `config.yaml`: original training configuration for provenance and reproducibility.
+
+## Usage
+
+Install and prepare the ImageWAM repository following the project README. Then download this model repository:
+
+```bash
+mkdir -p checkpoints/imagewam_release/interndata_pretrained/flux2_klein_4b
+
+huggingface-cli download yuyangalin/ImageWAM-FLUX.2-4B-InternData-A1-EE \
+  --repo-type model \
+  --local-dir checkpoints/imagewam_release/interndata_pretrained/flux2_klein_4b
+```
+
+## Intended Use
+
+This checkpoint is intended for:
+
+- Training downstream models for benchmarks and real-world settings from this pretrained checkpoint.
+- Research on robot policy learning, world action models, and image-editing-based action generation.
+
+This checkpoint is not intended for safety-critical or real-world robot deployment without additional validation.
+
+## Limitations
+
+- The checkpoint assumes the same model variant and configuration used during training. See `config.yaml`.
+- Users must separately prepare the matching FLUX.2 4B base model and autoencoder weights.
+
+## Citation
+
+If you use this checkpoint, please cite the ImageWAM paper:
+
+```bibtex
+@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}, 
+}
+```
+
+## Acknowledgements
+
+ImageWAM builds on several open-source projects and model families, including FLUX.2, FastWAM, LIBERO, LIBERO-plus, and RoboTwin. Please also follow the licenses and citation requirements of the corresponding upstream projects.
+