Add pure-PyTorch fallback for BIM-VFI cost volume kernel

When cupy is unavailable, the costvol_func.forward() now falls back to a
pure-PyTorch implementation using unfold + dot product instead of raising
a RuntimeError. The CUDA/cupy kernel path is preserved unchanged for when
cupy is available. This allows BIM-VFI to run on systems without cupy
(including CPU-only setups), matching the pattern used for the softsplat
fallbacks in SGM-VFI and GIMM-VFI.

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

bim_vfi_arch/costvol.py

@@ -4,6 +4,7 @@ import collections
 import os
 import re
 import torch
+import torch.nn.functional as F
 import typing
 
 cupy = None
@@ -15,11 +16,7 @@ def _ensure_cupy():
             import cupy as _cupy
             cupy = _cupy
         except ImportError:
-            raise RuntimeError(
+            pass  # cupy unavailable; PyTorch fallback will be used
-                "cupy is required for BIM-VFI. Install it with:\n"
-                "  pip install cupy-cuda12x  (or cupy-cuda11x for CUDA 11)\n"
-                "Or run install.py from the ComfyUI-Tween directory."
-            )
 
 
 ##########################################################
@@ -246,6 +243,28 @@ def cuda_launch(strKey:str):
 # end
 
 
+def _pytorch_costvol(tenOne, tenTwo, intKernelSize):
+    """Pure-PyTorch local cost volume via unfold + dot product."""
+    B, C, H, W = tenOne.shape
+    pad = (intKernelSize - 1) // 2
+
+    # Pad tenTwo so out-of-bounds yields 0 (matches CUDA kernel)
+    tenTwo_padded = F.pad(tenTwo, [pad, pad, pad, pad])
+
+    # Unfold into patches: (B, C, H, W, K, K)
+    patches = tenTwo_padded.unfold(2, intKernelSize, 1).unfold(3, intKernelSize, 1)
+    # Reshape to (B, C, H, W, K*K)
+    patches = patches.contiguous().view(B, C, H, W, intKernelSize * intKernelSize)
+
+    # Dot product over C dimension: (B, H, W, K*K)
+    tenOut = (tenOne.unsqueeze(-1) * patches).sum(dim=1)
+
+    # Permute to (B, K*K, H, W) to match CUDA output layout
+    tenOut = tenOut.permute(0, 3, 1, 2).contiguous()
+
+    return tenOut
+
+
 ##########################################################
 
 
@@ -253,6 +272,8 @@ class costvol_func(torch.autograd.Function):
     @staticmethod
     @torch.amp.custom_fwd(device_type='cuda', cast_inputs=torch.float32)
     def forward(self, tenOne, tenTwo, intKernelSize):
+        _ensure_cupy()
+        if tenOne.is_cuda and cupy is not None:
             tenOut = tenOne.new_empty([tenOne.shape[0], intKernelSize ** 2, tenOne.shape[2], tenOne.shape[3]])
 
             cuda_launch(cuda_kernel('costvol_out', '''
@@ -302,6 +323,8 @@ class costvol_func(torch.autograd.Function):
                 args=[cuda_int32(tenOut.shape[0] * tenOut.shape[2] * tenOut.shape[3]), tenOne.data_ptr(), tenTwo.data_ptr(), intKernelSize, tenOut.data_ptr()],
                 stream=collections.namedtuple('Stream', 'ptr')(torch.cuda.current_stream().cuda_stream)
             )
+        else:
+            tenOut = _pytorch_costvol(tenOne, tenTwo, intKernelSize)
 
         self.save_for_backward(tenOne, tenTwo)
         self.intKernelSize = intKernelSize