fix: adaptive VRAM strategy + force rembg CPU to prevent OOM

Two root causes of CUDA OOM fixed:

1. onnxruntime-gpu CUDAExecutionProvider pre-allocated ~12GB VRAM arena
   for bria-rmbg background removal, starving PyTorch models.
   Fix: force CPUExecutionProvider in BackgroundRemover (rembg is
   lightweight, runs fine on CPU, frees all VRAM for shape/tex).

2. Previous 'always delete' strategy was wasteful on high-RAM machines.
   New adaptive strategy checks available system RAM at runtime:
   - RAM >= 16GB free: offload i23d to CPU (.to('cpu')) — fast, ~1s
   - RAM <  16GB free: full del + reload from disk — safe, ~20-30s
   This gives instant model switching on 32GB+ machines while keeping
   16GB machines safe from OOM Killer.

Helper functions:
- _prepare_for_tex(): adaptive offload/delete based on RAM check
- _ensure_i23d_worker(): restore from CPU (fast) or disk (slow)
- _get_available_ram_gb(): reads /proc/meminfo
- _can_offload_to_cpu(): threshold check with logging

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

hy3dshape/hy3dshape/rembg.py

@@ -13,12 +13,21 @@
 # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
 
 from PIL import Image
+import onnxruntime as ort
 from rembg import remove, new_session
 
 
 class BackgroundRemover():
     def __init__(self, model_name: str = "bria-rmbg"):
-        self.session = new_session(model_name)
+        # Force CPU-only execution for onnxruntime to prevent CUDA arena
+        # from consuming ~12GB+ VRAM that PyTorch models need.
+        # Background removal is lightweight and runs fast on CPU.
+        _orig = ort.get_device
+        ort.get_device = lambda: "CPU"
+        try:
+            self.session = new_session(model_name)
+        finally:
+            ort.get_device = _orig
 
     def __call__(self, image: Image.Image):
         output = remove(image, session=self.session, bgcolor=[255, 255, 255, 0])