Ethanfel
db64fc195a
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>
43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class LayerNorm(nn.Module):
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r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
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The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
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shape (batch_size, height, width, channels) while channels_first corresponds to inputs
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with shape (batch_size, channels, height, width).
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"""
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def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(normalized_shape))
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self.bias = nn.Parameter(torch.zeros(normalized_shape))
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self.eps = eps
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self.data_format = data_format
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if self.data_format not in ["channels_last", "channels_first"]:
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raise NotImplementedError
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self.normalized_shape = (normalized_shape,)
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def forward(self, x):
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if self.data_format == "channels_last":
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return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
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elif self.data_format == "channels_first":
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x = x.permute(0, 2, 3, 1)
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return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
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class ResBlock(nn.Module):
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def __init__(self, feat_channels, kernel_size=3, padding_mode='zeros'):
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super().__init__()
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self.conv1 = nn.Conv2d(feat_channels, feat_channels, kernel_size, padding=(kernel_size - 1) // 2,
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padding_mode=padding_mode)
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self.act = nn.LeakyReLU()
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self.conv2 = nn.Conv2d(feat_channels, feat_channels, kernel_size, padding=(kernel_size - 1) // 2,
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padding_mode=padding_mode)
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def forward(self, x):
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inp = x
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x = self.conv2(self.act(self.conv1(x)))
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return inp + x
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