🛰️ KAO-DIFFSAT-SD-BASE-v1
Clean Stable Diffusion Inpainting Backbone for Remote Sensing Research
🛰️ Visual Overview
Satellite-oriented inpainting baseline enabling structured reconstruction across diverse geospatial scenes.
🌍 Overview
KAO-DIFFSAT-SD-BASE-v1 is a cleaned and structured Stable Diffusion inpainting backbone , prepared for remote sensing adaptation and research .
This repository is designed to serve as:
🧩 A baseline model for satellite image inpainting
🧠 A foundation for fine-tuning (KAO-DiffSat)
🔬 A reproducible starting point for geospatial generative AI
⚠️ Important: does NOT introduce new training or optimization methods .clean, minimal, and research-ready version of the original inpainting model .
🧠 Positioning
Component Description This Repo Clean baseline diffusion backbone KAO-DiffSat Fine-tuned satellite adaptation (future) KAO (Paper) Optimization-based enhancement framework
🏗️ Base Model
This model is derived from:
Original model developed by Stability AI and contributors .
🔹 What we did
Removed redundant files and duplicates
Organized model structure for clarity
Ensured compatibility with Diffusers
Prepared for downstream fine-tuning
👉 No modification to core weights has been introduced in this release.
🚀 Key Features
🧼 Clean and minimal model structure
⚡ Reduced storage footprint (no redundant weights)
🔁 Fully compatible with Diffusers pipelines
🛰️ Ready for remote sensing fine-tuning
🧩 Modular design for research experiments
🔬 Results Overview
Qualitative reconstruction results using diffusion-based inpainting (baseline).
Demonstration of spatial consistency and texture recovery.
🛰️ Full Visual Examples
Example outputs across multiple scene types.
⚠️ What This Model Is (and Is Not)
✅ This model:
A clean baseline diffusion model
A directly usable inpainting backbone
A starting point for fine-tuning and research
❌ This model is NOT:
A fine-tuned satellite model
The KAO method
An optimized or domain-adapted system
👉 Those contributions will be introduced in future releases.
🚀 Usage (Diffusers)
1 from diffusers import StableDiffusionInpaintPipeline
2 import torch
3
4 pipe = StableDiffusionInpaintPipeline . from_pretrained (
5 "kaopanboonyuen/KAO-DIFFSAT-SD-BASE-v1" ,
6 torch_dtype = torch . float16
7 ) . to ( "cuda" )
8
9 prompt = "High-resolution satellite image with missing regions reconstructed realistically"
10
11 image = pipe (
12 prompt = prompt ,
13 image = init_image ,
14 mask_image = mask
15 ) . images [ 0 ]
🛰️ Prompt Guide (Satellite-Oriented Usage)
To simulate remote sensing behavior, use prompts emphasizing:
spatial consistency
structural realism
geospatial coherence
🔹 Example
High-resolution satellite image of urban area,
reconstruct missing regions with realistic buildings,
maintain spatial consistency and natural textures🧪 Use Cases
Cloud removal (baseline)
Image completion
Pre-training for remote sensing models
Generative data augmentation
Benchmark comparison
⚠️ Limitations
Prompt-dependent outputs
Not trained on real satellite datasets
No geospatial constraints
High GPU memory usage
🔮 Roadmap
🔜 KAO-DIFFSAT-INPAINTING-v1 (fine-tuned model)
🔜 KAO-integrated optimization
🔜 Prompt-free inference system
📚 Citation
🔹 Base Model
1 @misc{stabilityai2022sd2,
2 title = {Stable Diffusion v2},
3 author = {{Stability AI}},
4 year = {2022},
5 howpublished = {\url{https://github.com/Stability-AI/stablediffusion}},
6 note = {Including inpainting variant}
7 }
🔬 Project Context
This repository is part of the broader KAO-DiffSat research direction , aiming to bridge:
Diffusion Models × Remote Sensing × Optimization
🌐 Project Page
🤝 Acknowledgements
Stability AI for foundational diffusion models
Hugging Face for open model hosting
Open-source community for tools and infrastructure
🌟 Vision
This work aims to advance:
Generative AI for Earth Observation
toward scalable and research-driven geospatial intelligence systems.
