Run torch models in fp16 under inference_mode

- ViTMatte and BiRefNet load with torch_dtype=float16 (the BiRefNet
  checkpoint ships fp16 anyway; the old .float() upcast is gone) and
  floating-point inputs are cast to half to match.
- torch.no_grad() -> torch.inference_mode() in both predict paths.
- Outputs already downcast via .float() before .numpy(), so downstream
  stays float32. anime-seg is ONNX and unaffected.

Effect verified by the user beforehand.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

bgfilter/segmentation.py

@@ -38,10 +38,11 @@ class BiRefNetSegmenter:
 
         self.torch = torch
         self.model = AutoModelForImageSegmentation.from_pretrained(
-            resolve_model_source(settings.model_name), trust_remote_code=True
+            resolve_model_source(settings.model_name),
+            trust_remote_code=True,
+            torch_dtype=torch.float16,  # checkpoint ships fp16; run it as-is, inputs cast to match
         )
         self.model.eval()
-        self.model.float()  # checkpoint ships as fp16; force fp32 to match inputs
         self.device = self._resolve_device(settings.device)
         self.model.to(self.device)
 
@@ -64,8 +65,8 @@ class BiRefNetSegmenter:
     def mask(self, rgb: np.ndarray) -> np.ndarray:
         """Return a soft foreground mask, float32 0..1, at the input resolution."""
         image = Image.fromarray(rgb.astype(np.uint8), mode="RGB")
-        tensor = self.transform(image).unsqueeze(0).to(self.device)
-        with self.torch.no_grad():
+        tensor = self.transform(image).unsqueeze(0).to(self.device, dtype=self.torch.float16)
+        with self.torch.inference_mode():
             pred = self.model(tensor)[-1].sigmoid()
         pred = pred[0, 0].detach().float().cpu().numpy()
         if pred.shape != rgb.shape[:2]: