Add minimal usage snippets (Kotlin + Python)

README.md

@@ -36,6 +36,40 @@ activation functions** (SimpleGate = channel-split multiply), so the whole netwo
 image[1,3,256,256] (RGB [0,1]) →[GPU: NAFNet U-Net]→ denoised[1,3,256,256]
 ```
 
+## Minimal usage
+
+**Android (Kotlin, CompiledModel GPU)**
+
+```kotlin
+val model = CompiledModel.create(context.assets, "nafnet_sidd_width32_fp16.tflite",
+    CompiledModel.Options(Accelerator.GPU), null)
+val inputs = model.createInputBuffers()
+val outputs = model.createOutputBuffers()
+inputs[0].writeFloat(chw)            // [1,3,256,256] RGB in [0,1], NCHW
+model.run(inputs, outputs)
+val denoised = outputs[0].readFloat()    // [1,3,256,256] in [0,1]
+```
+
+**Python (desktop verification)**
+
+```python
+import numpy as np
+from PIL import Image
+from ai_edge_litert.interpreter import Interpreter
+
+img = Image.open("noisy.jpg").convert("RGB").resize((256, 256))
+x = (np.asarray(img, np.float32) / 255.0).transpose(2, 0, 1)[None]   # [1,3,256,256]
+
+it = Interpreter(model_path="nafnet_sidd_width32_fp16.tflite"); it.allocate_tensors()
+it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
+y = it.get_tensor(it.get_output_details()[0]["index"])[0]            # [3,256,256], [0,1]
+Image.fromarray((y.transpose(1, 2, 0).clip(0, 1) * 255).astype(np.uint8)).save("restored.png")
+```
+
+A complete Android sample (image picker + before/after) is in the official
+[google-ai-edge/litert-samples](https://github.com/google-ai-edge/litert-samples) repo under
+`compiled_model_api/image_restoration`.
+
 ## How it converts (litert-torch)
 
 Pure CNN (no activations). Three numerically-exact re-authorings, the headline being **SafeLayerNorm**: