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>

nodes.py

@@ -119,6 +119,10 @@ class BIMVFIInterpolate:
                     "default": False,
                     "tooltip": "Store all intermediate frames on GPU instead of CPU. Much faster (no transfers) but requires enough VRAM for all frames. Recommended for 48GB+ cards.",
                 }),
+                "batch_size": ("INT", {
+                    "default": 1, "min": 1, "max": 64, "step": 1,
+                    "tooltip": "Number of frame pairs to process simultaneously. Higher = faster but uses more VRAM. Start with 1, increase until VRAM is full. Recommended: 1 for 8GB, 2-4 for 24GB, 4-16 for 48GB+.",
+                }),
             }
         }
 
@@ -127,7 +131,7 @@ class BIMVFIInterpolate:
     FUNCTION = "interpolate"
     CATEGORY = "video/BIM-VFI"
 
-    def interpolate(self, images, model, multiplier, clear_cache_after_n_frames, keep_device, all_on_gpu):
+    def interpolate(self, images, model, multiplier, clear_cache_after_n_frames, keep_device, all_on_gpu, batch_size):
         if images.shape[0] < 2:
             return (images,)
 
@@ -161,26 +165,32 @@ class BIMVFIInterpolate:
             new_frames = []
             num_pairs = frames.shape[0] - 1
 
-            for i in range(num_pairs):
-                frame0 = frames[i:i+1]   # [1, C, H, W]
-                frame1 = frames[i+1:i+2] # [1, C, H, W]
+            for i in range(0, num_pairs, batch_size):
+                batch_end = min(i + batch_size, num_pairs)
+                actual_batch = batch_end - i
+
+                # Gather batch of pairs
+                frames0 = torch.cat([frames[j:j+1] for j in range(i, batch_end)], dim=0)
+                frames1 = torch.cat([frames[j+1:j+2] for j in range(i, batch_end)], dim=0)
 
                 if not keep_device:
                     model.to(device)
 
-                mid = model.interpolate_pair(frame0, frame1, time_step=0.5)
-                mid = mid.to(storage_device)
+                mids = model.interpolate_batch(frames0, frames1, time_step=0.5)
+                mids = mids.to(storage_device)
 
                 if not keep_device:
                     model.to("cpu")
 
-                new_frames.append(frames[i:i+1])
-                new_frames.append(mid)
+                # Interleave: original frame, then interpolated frame
+                for j in range(actual_batch):
+                    new_frames.append(frames[i + j:i + j + 1])
+                    new_frames.append(mids[j:j+1])
 
-                step += 1
+                step += actual_batch
                 pbar.update_absolute(step, total_steps)
 
-                if not all_on_gpu and (i + 1) % clear_cache_after_n_frames == 0 and torch.cuda.is_available():
+                if not all_on_gpu and (batch_end) % clear_cache_after_n_frames == 0 and torch.cuda.is_available():
                     clear_backwarp_cache()
                     torch.cuda.empty_cache()