Update README.md

README.md

@@ -12,9 +12,8 @@ datasets:
 <a href="https://research.adobe.com/person/romain-rouffet/" target="_blank">Romain Rouffet</a></p>
 
 <p align="center"><a href="https://sites.google.com/view/morse2025" target="_blank">CVPR 2025 Workshop MORSE</a> </p>
-<p align="center"><img src=https://huggingface.co/NewtNewt/MESA/blob/main/mesa-header-nz.png></p>
 
-MESA is a novel generative model based on latent denoising diffusion capable of generating 2.5D representations of terrain based on the text prompt conditioning supplied via natural language. The model produces two co-registered modalities of optical and depth maps.
+MESA is a novel generative model based on latent denoising diffusion capable of generating 2.5D representations of terrain based on the text prompt conditioning supplied via natural language. The model produces two co-registered modalities of optical and depth maps. This model is a finetune of [stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) and is builds upon Hugging Face’s [Diffusers](https://github.com/huggingface/diffusers) library.
 
 ## Model Description
 - **Paper:** [MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data](https://arxiv.org/abs/2504.07210)
@@ -35,6 +34,19 @@ cd MESA
 huggingface-cli download NewtNewt/MESA --local-dir ./weights
 ```
 
+
+## Usage
+```python
+from MESA.pipeline_terrain import TerrainDiffusionPipeline
+import MESA.models as models
+
+pipe = TerrainDiffusionPipeline.from_pretrained("./weights", torch_dtype=torch.float16)
+pipe.to("cuda");
+
+image,dem = pipe(prompt, num_inference_steps=50, guidance_scale=7.5)
+```
+
+## Citation
 ```latex
 @inproceedings{mesa2025,
 title={MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data},
@@ -47,6 +59,4 @@ url={https://arxiv.org/abs/2504.07210},}
 
 ## Acknowledgements
 
-This implementation builds upon Hugging Face’s [Diffusers](https://github.com/huggingface/diffusers) library. We also acknowledge [Gradio](https://www.gradio.app/) for providing an easy-to-use interface that allowed us to create the inference demos for our models.
-
 This model is the product of a collaboration between [Φ-lab, European Space Agency (ESA)](https://philab.esa.int/) and the [Adobe Research (Paris, France)](https://research.adobe.com/careers/paris/).