import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("EmbodiedCity/Airscape", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Airscape Model Weights
This repository contains the Phase1 & Phase2 weights of the model introduced in the paper:
[AirScape:An Aerial Generative World Model with Motion Controllability].
For more details, please refer to the homepage:
https://embodiedcity.github.io/AirScape/.
Citation
If this work has contributed to your research, welcome to cite it:
@inproceedings{zhao2025airscape,
author = {Zhao, Baining and Tang, Rongze and Jia, Mingyuan and Wang, Ziyou and Man, Fanhang and Zhang, Xin and Shang, Yu and Zhang, Weichen and Wu, Wei and Gao, Chen and Chen, Xinlei and Li, Yong},
title = {AirScape: An Aerial Generative World Model with Motion Controllability},
year = {2025},
isbn = {9798400720352},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3746027.3758180},
doi = {10.1145/3746027.3758180},
booktitle = {Proceedings of the 33rd ACM International Conference on Multimedia},
pages = {12519–12528},
numpages = {10},
keywords = {aerial space, generative world model, motion controllability},
location = {Dublin, Ireland},
series = {MM '25}
}
license: mit
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