Initial commit: ComfyUI BIM-VFI node for video frame interpolation

Wraps BiM-VFI (CVPR 2025) as a ComfyUI custom node for long video
frame interpolation with memory-safe sequential processing.

- LoadBIMVFIModel: checkpoint loader with auto-download from Google Drive
- BIMVFIInterpolate: 2x/4x/8x recursive interpolation with per-pair
  GPU processing, configurable VRAM management (all_on_gpu for high-VRAM
  setups), progress bar, and backwarp cache clearing
- Vendored inference-only architecture from KAIST-VICLab/BiM-VFI
- Auto-detection of CUDA version for cupy installation

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

bim_vfi_arch/arch.py

@@ -0,0 +1,42 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LayerNorm(nn.Module):
+    r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
+    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
+    shape (batch_size, height, width, channels) while channels_first corresponds to inputs
+    with shape (batch_size, channels, height, width).
+    """
+
+    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(normalized_shape))
+        self.bias = nn.Parameter(torch.zeros(normalized_shape))
+        self.eps = eps
+        self.data_format = data_format
+        if self.data_format not in ["channels_last", "channels_first"]:
+            raise NotImplementedError
+        self.normalized_shape = (normalized_shape,)
+
+    def forward(self, x):
+        if self.data_format == "channels_last":
+            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
+        elif self.data_format == "channels_first":
+            x = x.permute(0, 2, 3, 1)
+            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
+
+class ResBlock(nn.Module):
+    def __init__(self, feat_channels, kernel_size=3, padding_mode='zeros'):
+        super().__init__()
+        self.conv1 = nn.Conv2d(feat_channels, feat_channels, kernel_size, padding=(kernel_size - 1) // 2,
+                               padding_mode=padding_mode)
+        self.act = nn.LeakyReLU()
+        self.conv2 = nn.Conv2d(feat_channels, feat_channels, kernel_size, padding=(kernel_size - 1) // 2,
+                               padding_mode=padding_mode)
+
+    def forward(self, x):
+        inp = x
+        x = self.conv2(self.act(self.conv1(x)))
+        return inp + x