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HuiYanEarth-SAR

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HuiYanEarth-SAR(2026)

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## HuiYanEarth-SAR
  
Yongxiang Liu, Jie Zhou, Bowen Peng, Yafei Song, Tianpeng Liu, Li Liu*

👑**HuiYanEarth-SAR is the first foundation model for high-fidelity and low-cost global SAR imagery generation based on AlphaEarth and integrated scattering mechanisms.**

✨**We hope she can inspire more friends to delve into this fascinating field.**

## 🛠️ Updates (YY/MM/DD)
- (2026/04) Code is released.

## 😃 Human Visual Evaluation
To comprehensively evaluate the visual authenticity, geographical plausibility, and physical consistency of the generated images from a human perception perspective, we designed and conducted a hierarchical double-blind visual assessment experiment.

[Welcome to enjoy it](https://www.credamo.com/u/oBgNz4JWXPN/)!

## 📽️ Getting Started
### Stable Diffusion v1-5 Model Card

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion).

The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) 
checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).

You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). Thanks them VERY MUCH!!!

### Diffusers
```py
from diffusers import StableDiffusionPipeline
import torch

model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]  
    
image.save("astronaut_rides_horse.png")
```
For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion)

### Original GitHub Repository

1. Download the weights 
   - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference
   - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning

## 🎞️ Visualization

## 📖 License

license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: true
extra_gated_prompt: |-
  This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
  The CreativeML OpenRAIL License specifies:

1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 
  2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
  3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
   Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license

## 🎈**Acknowledgements**

**Thanks to all collaborators and the lovely participants who joined in the experiments, as well as the selfless open-source contributors.**
 
**My gratitude also goes to you who are reading this paper right now.**
 
 **It is because of you that this work and SAR image interpretation have been made better.**

Please light up the STAR⭐⭐⭐⭐⭐  to encourage more opensource on SAR image interpretations!🥰🥳🥂

HuiYanEarth-SAR

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