perf(visible): crop MI-GAN around the mask so peak RAM is bounded by mark size

erase_migan fed the whole frame to the ONNX model, so peak RSS scaled with the
upload (~0.6 GB at 4 MP up to ~2.4 GB at 25 MP). Mirror erase_lama: crop a padded
region around the mask (pad = max(256, 2*bbox)), feed only that crop (at native
resolution -- MI-GAN accepts arbitrary dims, unlike LaMa's fixed 512 square), and
paste only masked pixels back. Peak RSS is now bounded by the mark size
(~0.6-0.9 GB), so a memory-tight host (a 1-2 GB web worker) can run MI-GAN on a
25 MP upload.

Fill quality is unchanged: verified by eye on real Gemini/Doubao marks plus a
ground-truth reconstruction sweep -- a tighter view if anything reduces the GAN's
hallucination of large background structure.

Extract the shared padded-crop-box math into _padded_crop_box (used by both
erase_lama and erase_migan).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

tests/test_region_eraser.py

@@ -171,6 +171,7 @@ class TestMiganWrapper:
 
             def run(self, _outputs, feeds):
                 self.outer.captured["mask"] = feeds["mask"]
+                self.outer.captured["image_shape"] = feeds["image"].shape
                 img = feeds["image"]  # (1,3,H,W) RGB
                 red = np.zeros_like(img)
                 red[:, 0] = 255  # pure red in RGB
@@ -191,6 +192,21 @@ class TestMiganWrapper:
         assert m[50, 50] == 0
         assert m[10, 10] == 255
 
+    @pytest.mark.usefixtures("_fake_migan")
+    def test_crops_around_mask_so_onnx_input_is_bounded(self):
+        # Large frame, small corner mark: the tensor fed to MI-GAN is the padded
+        # CROP (pad = max(256, 2*bbox)), not the full image -- this is what holds the
+        # ONNX working set roughly constant on big uploads instead of scaling with
+        # the image (the memory fix). Untouched pixels stay exact; the mark is filled.
+        img = np.full((2000, 3000, 3), 120, np.uint8)
+        out = erase(img, boxes=[(2900, 1900, 60, 60)], backend="migan", dilate=0)
+        assert out.shape == img.shape
+        _, _, fh, fw = self.captured["image_shape"]
+        assert fh < 700  # crop height, not the 2000px frame
+        assert fw < 700  # crop width, not the 3000px frame
+        assert tuple(int(v) for v in out[1930, 2930]) == (0, 0, 255)  # mark -> red fill
+        assert np.array_equal(out[:100, :100], img[:100, :100])  # far corner untouched
+
     @pytest.mark.usefixtures("_fake_migan")
     def test_grayscale_2d_does_not_raise(self):
         gray = np.full((100, 100), 120, np.uint8)