🚀 Initial Release: KAO-DIFFSAT-VHR-DEMO

.gitattributes

@@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+# Image files
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+*.bmp filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,6 +1,6 @@
 ---
 title: KAO DIFFSAT Demo
-emoji: 🖼
+emoji: 🛰️
 colorFrom: purple
 colorTo: red
 sdk: gradio

app.py

@@ -2,7 +2,11 @@ import torch
 import gradio as gr
 from PIL import Image
 from diffusers import StableDiffusionInpaintPipeline
+import os
 
+# -----------------------------
+# Model
+# -----------------------------
 MODEL_ID = "kaopanboonyuen/KAO-DIFFSAT-VHR-v1"
 
 pipe = StableDiffusionInpaintPipeline.from_pretrained(
@@ -10,34 +14,76 @@ pipe = StableDiffusionInpaintPipeline.from_pretrained(
     torch_dtype=torch.float16
 ).to("cuda")
 
+pipe.enable_attention_slicing()
+
+# -----------------------------
+# Inpainting Function
+# -----------------------------
 def inpaint(image, mask, prompt):
     image = image.convert("RGB")
     mask = mask.convert("RGB")
 
-    result = pipe(
-        prompt=prompt,
-        image=image,
-        mask_image=mask
-    ).images[0]
+    with torch.autocast("cuda"):
+        result = pipe(
+            prompt=prompt,
+            image=image,
+            mask_image=mask,
+            guidance_scale=7.5,
+            num_inference_steps=30
+        ).images[0]
 
     return result
 
-title = "🛰️ KAO-DIFFSAT-VHR-v1 Demo"
+
+# -----------------------------
+# Optional: Sample loader
+# -----------------------------
+SAMPLE_DIR = "satellite_sample_input"
+
+def load_sample(name):
+    path = os.path.join(SAMPLE_DIR, name)
+    return Image.open(path).convert("RGB")
+
+
+# -----------------------------
+# UI
+# -----------------------------
+title = "🛰️ KAO-DIFFSAT-VHR-v1"
 description = """
-Satellite Image Inpainting (VHR) — Baseline for KAO Framework  
-Upload an image, draw a mask, and reconstruct missing regions.
+**Diffusion-based Satellite Image Inpainting for Very High Resolution (VHR) Remote Sensing**
+
+This demo showcases KAO-DIFFSAT-VHR-v1, a baseline diffusion model for reconstructing missing or corrupted satellite imagery regions.
+
+Upload your own image or try preloaded samples.
 """
 
+examples = [
+    [load_sample("M-33-7-A-d-3-2.jpg"), None,
+     "Restore missing satellite regions with realistic urban and natural structures"],
+
+    [load_sample("M-33-20-D-c-4-2.jpg"), None,
+     "High-resolution satellite inpainting with consistent geography and texture"],
+
+    [load_sample("M-34-56-A-b-1-4.jpg"), None,
+     "Reconstruct VHR remote sensing image with spatial consistency"]
+]
+
+
 demo = gr.Interface(
     fn=inpaint,
     inputs=[
-        gr.Image(type="pil", label="Input Image"),
-        gr.Image(type="pil", label="Mask Image (white = inpaint)"),
-        gr.Textbox(value="High-resolution satellite image, reconstruct missing regions with realistic structures")
+        gr.Image(type="pil", label="🛰️ Input Satellite Image"),
+        gr.Image(type="pil", label="🎯 Mask (white = inpaint region)"),
+        gr.Textbox(
+            label="Prompt",
+            value="High-resolution satellite image reconstruction with realistic land cover, buildings, and terrain consistency"
+        )
     ],
-    outputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil", label="🧠 Reconstructed Output"),
     title=title,
-    description=description
+    description=description,
+    examples=examples,
+    allow_flagging="never"
 )
 
 demo.launch()