Ship MI-GAN's bare 512 generator as the panorama border filler

Sargsyan et al., ICCV 2023; MIT code and weights (models/LICENCE.md),
exported by tools/export-migan.sh at a fixed 1×4×512×512 from the
authors' checkpoint — six operator types, 28 MB, in LFS like the rest.
The package installs it beside the scene model and the APK unpacks it
with the others.

tools/export-migan.sh

@@ -0,0 +1,65 @@
+#!/usr/bin/env bash
+# Re-export the border-fill model that ships in models/inpaint/.
+#
+# The .onnx is committed (D14, FR-MRG-4), so this is not part of any build —
+# it exists so the committed artefact is reproducible rather than a binary
+# someone once produced and nobody can regenerate. Run it when bumping the
+# model.
+#
+#   ./tools/export-migan.sh    # -> models/inpaint/migan-512.onnx
+#
+# Requires `uv`. Everything else is fetched into a throwaway venv, including
+# a CPU-only torch and `gdown` for the checkpoint, which the authors keep on
+# Google Drive (models/LICENCE.md has the licence; it is MIT).
+#
+# ## What is exported, and what is not
+#
+# The *bare* 512 generator, at a fixed 1×4×512×512: channel 0 is the mask
+# minus a half (1 where the picture is known), channels 1–3 the RGB in −1..1
+# with the unknown pixels zeroed; out come three planes in −1..1. The
+# authors' "pipeline" ONNX — crop around the mask, resize, blend, all in the
+# graph with dynamic shapes — is what tract cannot load, and every one of
+# those steps is done in Rust (`dr_pano::fill`), where a tile of a panorama
+# border needs different context from a brush stroke anyway.
+set -euo pipefail
+
+HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO="$(cd "${HERE}/.." && pwd)"
+OUT="${REPO}/models/inpaint"
+
+# Not `mktemp -d` under /tmp: a tmpfs, and torch is a gigabyte.
+WORK="$(mktemp -d -p "${TMPDIR:-/var/tmp}")"
+trap 'rm -rf "${WORK}"' EXIT
+
+echo "==> exporting MI-GAN in ${WORK}"
+cd "${WORK}"
+git clone -q --depth 1 https://github.com/Picsart-AI-Research/MI-GAN.git migan
+uv venv -q --python 3.12 venv
+VIRTUAL_ENV="${WORK}/venv" uv pip install -q --index-url https://download.pytorch.org/whl/cpu torch
+VIRTUAL_ENV="${WORK}/venv" uv pip install -q onnx onnxslim gdown
+
+# The 512 Places2 checkpoint, from the authors' Drive folder.
+"${WORK}/venv/bin/gdown" --quiet "https://drive.google.com/uc?id=1D_YCuCgo20S2256sqpedsmENNm2WMtVY" -O "${WORK}/migan_512_places2.pt"
+
+VIRTUAL_ENV="${WORK}/venv" "${WORK}/venv/bin/python" - "${WORK}/migan" "${WORK}/migan_512_places2.pt" "${WORK}/migan-512.onnx" <<'PY'
+import sys, torch, onnx, onnxslim
+sys.path.insert(0, sys.argv[1])
+from lib.model_zoo.migan_inference import Generator as MIGAN
+
+repo, ckpt, out = sys.argv[1], sys.argv[2], sys.argv[3]
+model = MIGAN(resolution=512).eval()
+model.load_state_dict(torch.load(ckpt, map_location="cpu"))
+torch.onnx.export(model, torch.zeros(1, 4, 512, 512), out, opset_version=17, dynamo=False,
+                  input_names=["input"], output_names=["output"],
+                  dynamic_axes=None, do_constant_folding=True)
+m = onnxslim.slim(onnx.load(out))
+onnx.checker.check_model(m)
+onnx.save(m, out)
+print("ops:", sorted({n.op_type for n in m.graph.node}))
+for o in m.graph.output:
+    print("out", o.name, [d.dim_value for d in o.type.tensor_type.shape.dim])
+PY
+
+mkdir -p "${OUT}"
+cp "${WORK}/migan-512.onnx" "${OUT}/migan-512.onnx"
+echo "==> ${OUT}/migan-512.onnx"