bg_remove_local: local RMBG-2.0 background removal on MPS

New operator: briaai/RMBG-2.0 image->transparent cutout, fully local on Apple
Silicon (weights unlocked by owner HF license). Free local alternative to the
cloud fal_bg_remove; the recommended pre-pass before SF3D / image->3D. Verified:
clean 1024px cutout of the FLUX astrolabe (alpha 0-255, 86.6% removed, thin rings
preserved). Same OpenMP guardrails as sf3d. scripts/install_rmbg.sh; venvs/rmbg.

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

scripts/install_rmbg.sh

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+#!/usr/bin/env bash
+# briaai/RMBG-2.0 local background removal on Apple Silicon (MPS). Weights are
+# HF-gated (owner has access); downloaded on first run via the HF token in Settings.
+set -euo pipefail
+cd "$(dirname "$0")/.."
+UV=/opt/homebrew/bin/uv
+echo "[rmbg] creating venv (python 3.11) ..."
+$UV venv --python 3.11 venvs/rmbg
+PY="$(pwd)/venvs/rmbg/bin/python"
+echo "[rmbg] installing deps (transformers + RMBG-2.0 remote-code deps) ..."
+$UV pip install --python "$PY" torch torchvision transformers pillow numpy kornia timm scikit-image einops
+echo "[rmbg] verifying ..."
+"$PY" -c "import torch, transformers, kornia, timm; print('[rmbg] torch', torch.__version__, 'mps', torch.backends.mps.is_available(), '| transformers', transformers.__version__)"
+echo "[rmbg] done"

server/operators/bg_remove_local/manifest.json

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+{
+  "id": "bg_remove_local",
+  "name": "Remove Background (local)",
+  "category": "image-prep",
+  "description": "Image → clean subject cutout (transparent PNG) entirely on this Mac via briaai/RMBG-2.0 (MPS). Free local alternative to fal_bg_remove — run before SF3D / image→3D for a big quality jump. Weights unlocked by the owner's HF license.",
+  "accepts": ["image"],
+  "produces": ["image"],
+  "resources": "gpu",
+  "entry": "run.py",
+  "python": "/Users/m3ultra/Documents/MODELBEAST/venvs/rmbg/bin/python",
+  "params_schema": {
+    "type": "object",
+    "properties": {
+      "resolution": {"type": "integer", "enum": [1024, 2048], "default": 1024, "description": "Processing resolution (2048 = finer edges, slower)"},
+      "background": {"type": "string", "enum": ["transparent", "white", "black"], "default": "transparent", "description": "Fill for removed background (transparent RGBA, or a solid matte)"}
+    }
+  }
+}

server/operators/bg_remove_local/run.py

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+import argparse
+import json
+import os
+import sys
+from pathlib import Path
+
+# Same libomp collision guardrails as sf3d (kornia/timm/torch each may link one).
+os.environ.setdefault("PYTORCH_ENABLE_MPS_FALLBACK", "1")
+os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "TRUE")
+os.environ.setdefault("OMP_NUM_THREADS", "1")
+os.environ.setdefault("MKL_NUM_THREADS", "1")
+
+import torch  # noqa: E402
+from PIL import Image  # noqa: E402
+from torchvision import transforms  # noqa: E402
+from transformers import AutoModelForImageSegmentation  # noqa: E402
+
+ap = argparse.ArgumentParser()
+ap.add_argument("--input", action="append", default=[])
+ap.add_argument("--outdir", required=True)
+ap.add_argument("--params", default="{}")
+a = ap.parse_args()
+p = json.loads(a.params)
+
+if not a.input:
+    print("ERROR: no input image")
+    sys.exit(1)
+
+res = int(p.get("resolution", 1024))
+bg = p.get("background", "transparent")
+device = "mps" if torch.backends.mps.is_available() else "cpu"
+print(f"loading briaai/RMBG-2.0 on {device} (first run downloads weights) ...", flush=True)
+
+model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-2.0", trust_remote_code=True)
+try:
+    torch.set_float32_matmul_precision("high")
+except Exception:
+    pass
+model.eval().to(device)
+
+transform = transforms.Compose([
+    transforms.Resize((res, res)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
+
+src = Path(a.input[0])
+image = Image.open(src).convert("RGB")
+inp = transform(image).unsqueeze(0).to(device)
+print("running segmentation ...", flush=True)
+with torch.no_grad():
+    preds = model(inp)[-1].sigmoid().cpu()
+mask = transforms.ToPILImage()(preds[0].squeeze()).resize(image.size)
+
+outdir = Path(a.outdir)
+name = f"{src.stem}_cutout.png"
+if bg == "transparent":
+    out = image.copy()
+    out.putalpha(mask)
+else:
+    fill = (255, 255, 255) if bg == "white" else (0, 0, 0)
+    canvas = Image.new("RGB", image.size, fill)
+    canvas.paste(image, (0, 0), mask)
+    out = canvas
+out.save(outdir / name)
+print(f"done: {name}", flush=True)
+(outdir / "result.json").write_text(json.dumps(
+    {"outputs": [{"path": name, "meta": {"tool": "rmbg-2.0", "resolution": res, "background": bg}}]}))