🛰️ KAO-DIFFSAT-SD-BASE-v1
Clean Stable Diffusion Inpainting Backbone for Remote Sensing Research
🚀 Project website: https://kaopanboonyuen.github.io/KAO/
🛰️ 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:
This repository does NOT introduce new training or optimization methods.
It is a 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
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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)
from diffusers import StableDiffusionInpaintPipeline
import torch
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"kaopanboonyuen/KAO-DIFFSAT-SD-BASE-v1",
torch_dtype=torch.float16
).to("cuda")
prompt = "High-resolution satellite image with missing regions reconstructed realistically"
image = pipe(
prompt=prompt,
image=init_image,
mask_image=mask
).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
@misc{stabilityai2022sd2,
title = {Stable Diffusion v2},
author = {{Stability AI}},
year = {2022},
howpublished = {\url{https://github.com/Stability-AI/stablediffusion}},
note = {Including inpainting variant}
}
🔬 Project Context
This repository is part of the broader KAO-DiffSat research direction, aiming to bridge:
Diffusion Models × Remote Sensing × Optimization
🌐 Project Page
https://kaopanboonyuen.github.io/KAO/
🤝 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.
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