frameworks:

• Pytorch license: Apache License 2.0 tasks:
• text-to-image-synthesis

Text2Earth Model Card

This model card focuses on the model associated with the Text2Earth model.

Examples

Using the 🤗's Diffusers library to run Text2Earth in a simple and efficient manner.

pip install diffusers transformers accelerate scipy safetensors

Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to EulerDiscreteScheduler):

import torch
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler

model_id = "lcybuaa/Text2Earth"
# Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to DPMSolverMultistepScheduler):
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, scheduler=scheduler, 
                                         custom_pipeline="pipeline_text2earth_diffusion", safety_checker=None)
pipe = pipe.to("cuda")
prompt = "Seven green circular farmlands are neatly arranged on the ground"
image = pipe(prompt,
             height=256,
             width=256,
             num_inference_steps=50, 
             guidance_scale=4.0).images[0]

image.save("circular.png")

Citation

If you find this paper useful in your research, please consider citing:

@ARTICLE{10988859,
  author={Liu, Chenyang and Chen, Keyan and Zhao, Rui and Zou, Zhengxia and Shi, Zhenwei},
  journal={IEEE Geoscience and Remote Sensing Magazine}, 
  title={Text2Earth: Unlocking text-driven remote sensing image generation with a global-scale dataset and a foundation model}, 
  year={2025},
  volume={},
  number={},
  pages={2-23},
  doi={10.1109/MGRS.2025.3560455}}