---
title: Text2Earth
canonical_url: "https://www.modelscope.cn/models/lcybuaa1111/Text2Earth"
md_url: "https://www.modelscope.cn/models/lcybuaa1111/Text2Earth.md"
repository: lcybuaa1111/Text2Earth
last_updated: 2025-05-28
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
parameters: 1.3B
tensor_type:
  - F32
  - I64
  - F16
library_name:
  - pytorch
  - safetensors
  - diffusers
frameworks:
  - Pytorch
downloads: 191
stars: 0
---

# Text2Earth

> Text2Earth - lcybuaa1111 在 ModelScope 开源的模型。--- frameworks: Pytorch license: Apache License 2.0 tasks: text-to-image-synthesis

- **Repository**: lcybuaa1111/Text2Earth
- **Tasks**: text-to-image-synthesis
- **Parameters**: 1.3B
- **Downloads**: 191
- **Stars**: 0
- **Last updated**: 2025-05-28

Source: https://www.modelscope.cn/models/lcybuaa1111/Text2Earth

---

---
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**](https://github.com/Chen-Yang-Liu/Text2Earth).

## 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}}
```