Add EMA-VFI (CVPR 2023) frame interpolation support

Integrate EMA-VFI alongside existing BIM-VFI with three new ComfyUI nodes:
Load EMA-VFI Model, EMA-VFI Interpolate, and EMA-VFI Segment Interpolate.

Architecture files vendored from MCG-NJU/EMA-VFI with device-awareness
fixes (removed hardcoded .cuda() calls), warp cache management, and
relative imports. InputPadder extended to support EMA-VFI's replicate
center-symmetric padding. Auto-installs timm dependency on first load.

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

ema_vfi_arch/warplayer.py

@@ -0,0 +1,25 @@
+import torch
+
+backwarp_tenGrid = {}
+
+
+def clear_warp_cache():
+    """Free all cached grid tensors (call between frame pairs to reclaim VRAM)."""
+    backwarp_tenGrid.clear()
+
+
+def warp(tenInput, tenFlow):
+    k = (str(tenFlow.device), str(tenFlow.size()))
+    if k not in backwarp_tenGrid:
+        tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=tenFlow.device).view(
+            1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
+        tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=tenFlow.device).view(
+            1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
+        backwarp_tenGrid[k] = torch.cat(
+            [tenHorizontal, tenVertical], 1).to(tenFlow.device)
+
+    tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
+                         tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)
+
+    g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)
+    return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)