Update README.md

README.md

@@ -1,276 +1,276 @@
----
-license: creativeml-openrail-m
-language:
-  - en
-tags:
-  - stable-diffusion
-  - image-inpainting
-  - remote-sensing
-  - satellite
-  - vhr
-  - diffusion
-  - geospatial
-library_name: diffusers
-pipeline_tag: image-to-image
----
-
-# 🛰️ KAO-DIFFSAT-VHR-v1  
-**Diffusion-based Satellite Image Inpainting (VHR) — Baseline Release**
-
-🚀 Project website: https://kaopanboonyuen.github.io/KAO/
-
----
-
-## 🛰️ Visual Overview
-
-<p align="center">
-  <img src="img/re_show_01.png" width="100%">
-  <img src="img/re_show_02.png" width="100%">
-</p>
-
-<p align="center">
-  <b>High-fidelity satellite image reconstruction across diverse scenes (urban, agricultural, coastal).</b>
-</p>
-
----
-
-## 🌍 Overview
-
-**KAO-DIFFSAT-VHR-v1** is a satellite-domain adaptation of diffusion-based inpainting, designed for **Very High Resolution (VHR) remote sensing imagery**.
-
-This release serves as a **baseline model** in our IEEE TGRS work:
-
-> *KAO: Kernel-Adaptive Optimization in Diffusion for Satellite Image*
-
-> 🎉 **Accepted to IEEE Transactions on Geoscience and Remote Sensing (TGRS)**  
-> 🌏 Presentation at **AOGS 2026, Japan**
-
-⚠️ **Important:**  
-This model is **NOT the KAO method itself**.  
-It is a **Stable Diffusion–based inpainting backbone adapted for satellite imagery**, used as one of the reference baselines in our study.
-
-👉 The full KAO framework (**training-free / prompt-free optimization**) will be released separately.
-
----
-
-## 🧠 Positioning
-
-| Component | Description |
-|----------|------------|
-| This Repo | Diffusion baseline for satellite inpainting |
-| KAO (Paper) | Optimization-based enhancement (no retraining required) |
-| Future Release | KAO-integrated inference (no prompt dependency) |
-
----
-
-## 🏗️ Base Model
-
-This model is built upon:
-
-- Stable Diffusion v2 Inpainting  
-- Provided via the Hugging Face ecosystem  
-
-We adapt the pipeline for **remote sensing characteristics**, including:
-
-- Large spatial structures (roads, rivers, urban layouts)  
-- Texture consistency across wide regions  
-- Geometric coherence in aerial perspective  
-
----
-
-## 🚀 Key Features
-
-- 🛰️ Satellite-aware inpainting behavior  
-- 🌆 Improved structural consistency for urban layouts  
-- 🌿 Better handling of vegetation and natural textures  
-- 🧩 Compatible with standard diffusion pipelines (Diffusers)
-
----
-
-## 🔬 Results Overview
-
-<p align="center">
-  <img src="img/re_show_01.png" width="90%">
-</p>
-
-**Qualitative comparison across multiple models. KAO demonstrates superior structural reconstruction and texture consistency.**
-
-<p align="center">
-  <img src="img/re_show_02.png" width="90%">
-</p>
-
-**Detailed comparison highlighting fine-grained structures and realistic spatial coherence.**
-
----
-
-## 🛰️ Full Benchmark Results
-
-<table>
-<tr>
-<td><img src="img/re_all_01.png"></td>
-<td><img src="img/re_all_02.png"></td>
-</tr>
-<tr>
-<td><img src="img/re_all_03.png"></td>
-<td><img src="img/re_all_04.png"></td>
-</tr>
-<tr>
-<td><img src="img/re_all_05.png"></td>
-<td><img src="img/re_all_06.png"></td>
-</tr>
-</table>
-
-<p align="center">
-  <b>Comparison across multiple scenes including urban, agricultural, and occluded environments.</b><br>
-  KAO consistently preserves spatial structure, enhances texture fidelity, and improves reconstruction realism.
-</p>
-
----
-
-## ⚠️ What This Model Is (and Is Not)
-
-### ✅ This model:
-- A **baseline diffusion model** for satellite inpainting  
-- Used for **comparative evaluation** in our research  
-- Suitable for prompt-based reconstruction tasks  
-
-### ❌ This model is NOT:
-- The full **KAO algorithm**  
-- A training-free optimization method  
-- A prompt-free system  
-
-👉 Those capabilities are introduced in our paper and will be released in future versions.
-
----
-
-## 🚀 Usage (Diffusers)
-
-```python
-from diffusers import StableDiffusionInpaintPipeline
-import torch
-
-pipe = StableDiffusionInpaintPipeline.from_pretrained(
-    "kaopanboonyuen/KAO-DIFFSAT-VHR-v1",
-    torch_dtype=torch.float16
-).to("cuda")
-
-prompt = "High-resolution satellite image of urban area with missing regions reconstructed"
-
-image = pipe(
-    prompt=prompt,
-    image=init_image,
-    mask_image=mask
-).images[0]
-````
-
----
-
-## 🛰️ Prompt Guide (Satellite Domain)
-
-Use prompts that enforce:
-
-* spatial coherence
-* structural realism
-* geospatial consistency
-
-### 🔹 Template
-
-```
-High-resolution satellite image of [SCENE],
-reconstruct missing regions with realistic [STRUCTURE],
-maintain spatial consistency, natural textures, and accurate geometry
-```
-
----
-
-## 🧪 Use Cases
-
-* Cloud removal in satellite imagery
-* Infrastructure reconstruction
-* Map completion
-* Environmental monitoring
-* Disaster recovery (flood, wildfire)
-
----
-
-## ⚠️ Limitations
-
-* Prompt-sensitive behavior
-* Not optimized for hyperspectral imagery
-* High memory footprint (~25GB)
-* No explicit geospatial constraints (baseline only)
-
----
-
-## 🔮 Roadmap
-
-* 🔜 KAO-DIFFSAT-v2 (KAO-integrated)
-* 🔜 Prompt-free inference (as proposed in paper)
-* 🔜 Lightweight and scalable variants
-
----
-
-## 📚 Citation
-
-### 🔹 Base Model
-
-```bibtex
-@misc{stabilityai2022sd2,
-  title        = {Stable Diffusion v2},
-  author       = {{Stability AI}},
-  year         = {2022},
-  howpublished = {\url{https://github.com/Stability-AI/stablediffusion}},
-  note         = {Including inpainting variant}
-}
-```
-
-### 🔹 Our Work (KAO)
-
-```bibtex
-@article{panboonyuen2025kao,
-  title={KAO: Kernel-Adaptive Optimization in Diffusion for Satellite Image},
-  author={Panboonyuen, Teerapong},
-  journal={IEEE Transactions on Geoscience and Remote Sensing},
-  year={2025},
-  publisher={IEEE}
-}
-```
-
----
-
-## 🔬 Project Overview
-This repository presents **KAO-DIFFSAT-VHR-v1**, a diffusion-based framework for **very high-resolution (VHR) satellite image inpainting**, designed for geospatial restoration and reconstruction tasks.
-
-The model builds upon Stable Diffusion-based inpainting and adapts it for remote sensing imagery.
-
----
-
-## 📄 Paper
-📘 Full paper available on arXiv:  
-https://arxiv.org/abs/2511.02462
-
----
-
-## 🌐 Project Page
-🚀 Project website:  
-https://kaopanboonyuen.github.io/KAO/
-
----
-
-## 🤝 Acknowledgements
-
-* Stability AI for foundational diffusion models
-* Hugging Face for open model hosting
-* Remote sensing community for datasets and benchmarks
-
----
-
-## 🌟 Vision
-
-This project aims to advance:
-
-> **Generative AI × Earth Observation**
-
-toward scalable, reliable, and interpretable satellite intelligence systems.
-
+---
+license: creativeml-openrail-m
+language:
+  - en
+tags:
+  - stable-diffusion
+  - image-inpainting
+  - remote-sensing
+  - satellite
+  - vhr
+  - diffusion
+  - geospatial
+library_name: diffusers
+pipeline_tag: image-to-image
+---
+
+# 🛰️ KAO-DIFFSAT-VHR-v1 (Beta Version)  
+**Diffusion-based Satellite Image Inpainting (VHR) — Baseline Release**
+
+🚀 Project website: https://kaopanboonyuen.github.io/KAO/
+
+---
+
+## 🛰️ Visual Overview
+
+<p align="center">
+  <img src="img/re_show_01.png" width="100%">
+  <img src="img/re_show_02.png" width="100%">
+</p>
+
+<p align="center">
+  <b>High-fidelity satellite image reconstruction across diverse scenes (urban, agricultural, coastal).</b>
+</p>
+
+---
+
+## 🌍 Overview
+
+**KAO-DIFFSAT-VHR-v1** is a satellite-domain adaptation of diffusion-based inpainting, designed for **Very High Resolution (VHR) remote sensing imagery**.
+
+This release serves as a **baseline model** in our IEEE TGRS work:
+
+> *KAO: Kernel-Adaptive Optimization in Diffusion for Satellite Image*
+
+> 🎉 **Accepted to IEEE Transactions on Geoscience and Remote Sensing (TGRS)**  
+> 🌏 Presentation at **AOGS 2026, Japan**
+
+⚠️ **Important:**  
+This model is **NOT the KAO method itself**.  
+It is a **Stable Diffusion–based inpainting backbone adapted for satellite imagery**, used as one of the reference baselines in our study.
+
+👉 The full KAO framework (**training-free / prompt-free optimization**) will be released separately.
+
+---
+
+## 🧠 Positioning
+
+| Component | Description |
+|----------|------------|
+| This Repo | Diffusion baseline for satellite inpainting |
+| KAO (Paper) | Optimization-based enhancement (no retraining required) |
+| Future Release | KAO-integrated inference (no prompt dependency) |
+
+---
+
+## 🏗️ Base Model
+
+This model is built upon:
+
+- Stable Diffusion v2 Inpainting  
+- Provided via the Hugging Face ecosystem  
+
+We adapt the pipeline for **remote sensing characteristics**, including:
+
+- Large spatial structures (roads, rivers, urban layouts)  
+- Texture consistency across wide regions  
+- Geometric coherence in aerial perspective  
+
+---
+
+## 🚀 Key Features
+
+- 🛰️ Satellite-aware inpainting behavior  
+- 🌆 Improved structural consistency for urban layouts  
+- 🌿 Better handling of vegetation and natural textures  
+- 🧩 Compatible with standard diffusion pipelines (Diffusers)
+
+---
+
+## 🔬 Results Overview
+
+<p align="center">
+  <img src="img/re_show_01.png" width="90%">
+</p>
+
+**Qualitative comparison across multiple models. KAO demonstrates superior structural reconstruction and texture consistency.**
+
+<p align="center">
+  <img src="img/re_show_02.png" width="90%">
+</p>
+
+**Detailed comparison highlighting fine-grained structures and realistic spatial coherence.**
+
+---
+
+## 🛰️ Full Benchmark Results
+
+<table>
+<tr>
+<td><img src="img/re_all_01.png"></td>
+<td><img src="img/re_all_02.png"></td>
+</tr>
+<tr>
+<td><img src="img/re_all_03.png"></td>
+<td><img src="img/re_all_04.png"></td>
+</tr>
+<tr>
+<td><img src="img/re_all_05.png"></td>
+<td><img src="img/re_all_06.png"></td>
+</tr>
+</table>
+
+<p align="center">
+  <b>Comparison across multiple scenes including urban, agricultural, and occluded environments.</b><br>
+  KAO consistently preserves spatial structure, enhances texture fidelity, and improves reconstruction realism.
+</p>
+
+---
+
+## ⚠️ What This Model Is (and Is Not)
+
+### ✅ This model:
+- A **baseline diffusion model** for satellite inpainting  
+- Used for **comparative evaluation** in our research  
+- Suitable for prompt-based reconstruction tasks  
+
+### ❌ This model is NOT:
+- The full **KAO algorithm**  
+- A training-free optimization method  
+- A prompt-free system  
+
+👉 Those capabilities are introduced in our paper and will be released in future versions.
+
+---
+
+## 🚀 Usage (Diffusers)
+
+```python
+from diffusers import StableDiffusionInpaintPipeline
+import torch
+
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    "kaopanboonyuen/KAO-DIFFSAT-VHR-v1",
+    torch_dtype=torch.float16
+).to("cuda")
+
+prompt = "High-resolution satellite image of urban area with missing regions reconstructed"
+
+image = pipe(
+    prompt=prompt,
+    image=init_image,
+    mask_image=mask
+).images[0]
+````
+
+---
+
+## 🛰️ Prompt Guide (Satellite Domain)
+
+Use prompts that enforce:
+
+* spatial coherence
+* structural realism
+* geospatial consistency
+
+### 🔹 Template
+
+```
+High-resolution satellite image of [SCENE],
+reconstruct missing regions with realistic [STRUCTURE],
+maintain spatial consistency, natural textures, and accurate geometry
+```
+
+---
+
+## 🧪 Use Cases
+
+* Cloud removal in satellite imagery
+* Infrastructure reconstruction
+* Map completion
+* Environmental monitoring
+* Disaster recovery (flood, wildfire)
+
+---
+
+## ⚠️ Limitations
+
+* Prompt-sensitive behavior
+* Not optimized for hyperspectral imagery
+* High memory footprint (~25GB)
+* No explicit geospatial constraints (baseline only)
+
+---
+
+## 🔮 Roadmap
+
+* 🔜 KAO-DIFFSAT-v2 (KAO-integrated)
+* 🔜 Prompt-free inference (as proposed in paper)
+* 🔜 Lightweight and scalable variants
+
+---
+
+## 📚 Citation
+
+### 🔹 Base Model
+
+```bibtex
+@misc{stabilityai2022sd2,
+  title        = {Stable Diffusion v2},
+  author       = {{Stability AI}},
+  year         = {2022},
+  howpublished = {\url{https://github.com/Stability-AI/stablediffusion}},
+  note         = {Including inpainting variant}
+}
+```
+
+### 🔹 Our Work (KAO)
+
+```bibtex
+@article{panboonyuen2025kao,
+  title={KAO: Kernel-Adaptive Optimization in Diffusion for Satellite Image},
+  author={Panboonyuen, Teerapong},
+  journal={IEEE Transactions on Geoscience and Remote Sensing},
+  year={2025},
+  publisher={IEEE}
+}
+```
+
+---
+
+## 🔬 Project Overview
+This repository presents **KAO-DIFFSAT-VHR-v1**, a diffusion-based framework for **very high-resolution (VHR) satellite image inpainting**, designed for geospatial restoration and reconstruction tasks.
+
+The model builds upon Stable Diffusion-based inpainting and adapts it for remote sensing imagery.
+
+---
+
+## 📄 Paper
+📘 Full paper available on arXiv:  
+https://arxiv.org/abs/2511.02462
+
+---
+
+## 🌐 Project Page
+🚀 Project website:  
+https://kaopanboonyuen.github.io/KAO/
+
+---
+
+## 🤝 Acknowledgements
+
+* Stability AI for foundational diffusion models
+* Hugging Face for open model hosting
+* Remote sensing community for datasets and benchmarks
+
+---
+
+## 🌟 Vision
+
+This project aims to advance:
+
+> **Generative AI × Earth Observation**
+
+toward scalable, reliable, and interpretable satellite intelligence systems.
+
 ---