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---
license: apache-2.0
tags:
- text-to-image
---
# Text2Earth Model Card
This model card focuses on the model associated with the [**Text2Earth model**](https://github.com/Chen-Yang-Liu/Text2Earth).
Paper is [[**here**](https://arxiv.org/pdf/2501.00895)]
## Examples
Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Text2Earth in a simple and efficient manner.
```bash
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):
```python
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}}
```
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