Add batch processing support for faster frame interpolation

Processes multiple frame pairs simultaneously instead of one-by-one.
New batch_size input (1-64) lets users trade VRAM for speed.
Refactored pyr_level logic into shared _get_pyr_level() helper.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

inference.py

@@ -43,6 +43,18 @@ class BiMVFIModel:
         self.model.to(device)
         return self
 
+    def _get_pyr_level(self, h):
+        if self.auto_pyr_level:
+            if h >= 2160:
+                return 7
+            elif h >= 1080:
+                return 6
+            elif h >= 540:
+                return 5
+            else:
+                return 3
+        return self.pyr_level
+
     @torch.no_grad()
     def interpolate_pair(self, frame0, frame1, time_step=0.5):
         """Interpolate a single frame between two input frames.
@@ -59,19 +71,36 @@ class BiMVFIModel:
         img0 = frame0.to(device)
         img1 = frame1.to(device)
 
-        if self.auto_pyr_level:
-            _, _, h, _ = img0.shape
-            if h >= 2160:
-                pyr_level = 7
-            elif h >= 1080:
-                pyr_level = 6
-            elif h >= 540:
-                pyr_level = 5
-            else:
-                pyr_level = 3
-        else:
-            pyr_level = self.pyr_level
-
+        pyr_level = self._get_pyr_level(img0.shape[2])
+        time_step_tensor = torch.tensor([time_step], device=device).view(1, 1, 1, 1)
+
+        result_dict = self.model(
+            img0=img0, img1=img1,
+            time_step=time_step_tensor,
+            pyr_level=pyr_level,
+        )
+
+        interp = result_dict["imgt_pred"]
+        interp = torch.clamp(interp, 0, 1)
+        return interp
+
+    @torch.no_grad()
+    def interpolate_batch(self, frames0, frames1, time_step=0.5):
+        """Interpolate multiple frame pairs at once.
+
+        Args:
+            frames0: [B, C, H, W] tensor, float32, range [0, 1]
+            frames1: [B, C, H, W] tensor, float32, range [0, 1]
+            time_step: float in (0, 1), temporal position of interpolated frames
+
+        Returns:
+            Interpolated frames as [B, C, H, W] tensor, float32, clamped to [0, 1]
+        """
+        device = next(self.model.parameters()).device
+        img0 = frames0.to(device)
+        img1 = frames1.to(device)
+
+        pyr_level = self._get_pyr_level(img0.shape[2])
         time_step_tensor = torch.tensor([time_step], device=device).view(1, 1, 1, 1)
 
         result_dict = self.model(