Ethanfel
d642255e70
Integrates GIMM-VFI alongside existing BIM/EMA/SGM models. Key feature: generates all intermediate frames in one forward pass (no recursive 2x passes needed for 4x/8x). - Vendor gimm_vfi_arch/ from kijai/ComfyUI-GIMM-VFI with device fixes - Two variants: RAFT-based (~80MB) and FlowFormer-based (~123MB) - Auto-download checkpoints from HuggingFace (Kijai/GIMM-VFI_safetensors) - Three new nodes: Load GIMM-VFI Model, GIMM-VFI Interpolate, GIMM-VFI Segment Interpolate - single_pass toggle: True=arbitrary timestep (default), False=recursive like other models - ds_factor parameter for high-res input downscaling Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
294 lines
9.6 KiB
Python
294 lines
9.6 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 ResidualBlock(nn.Module):
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def __init__(self, in_planes, planes, norm_fn="group", stride=1):
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super(ResidualBlock, self).__init__()
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self.conv1 = nn.Conv2d(
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in_planes, planes, kernel_size=3, padding=1, stride=stride
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)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
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self.relu = nn.ReLU(inplace=True)
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num_groups = planes // 8
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if norm_fn == "group":
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self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
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self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
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if not stride == 1:
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self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
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elif norm_fn == "batch":
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self.norm1 = nn.BatchNorm2d(planes)
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self.norm2 = nn.BatchNorm2d(planes)
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if not stride == 1:
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self.norm3 = nn.BatchNorm2d(planes)
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elif norm_fn == "instance":
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self.norm1 = nn.InstanceNorm2d(planes)
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self.norm2 = nn.InstanceNorm2d(planes)
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if not stride == 1:
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self.norm3 = nn.InstanceNorm2d(planes)
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elif norm_fn == "none":
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self.norm1 = nn.Sequential()
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self.norm2 = nn.Sequential()
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if not stride == 1:
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self.norm3 = nn.Sequential()
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if stride == 1:
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self.downsample = None
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else:
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self.downsample = nn.Sequential(
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nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3
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)
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def forward(self, x):
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y = x
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y = self.relu(self.norm1(self.conv1(y)))
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y = self.relu(self.norm2(self.conv2(y)))
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if self.downsample is not None:
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x = self.downsample(x)
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return self.relu(x + y)
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class BottleneckBlock(nn.Module):
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def __init__(self, in_planes, planes, norm_fn="group", stride=1):
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super(BottleneckBlock, self).__init__()
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self.conv1 = nn.Conv2d(in_planes, planes // 4, kernel_size=1, padding=0)
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self.conv2 = nn.Conv2d(
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planes // 4, planes // 4, kernel_size=3, padding=1, stride=stride
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)
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self.conv3 = nn.Conv2d(planes // 4, planes, kernel_size=1, padding=0)
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self.relu = nn.ReLU(inplace=True)
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num_groups = planes // 8
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if norm_fn == "group":
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self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes // 4)
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self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes // 4)
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self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
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if not stride == 1:
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self.norm4 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
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elif norm_fn == "batch":
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self.norm1 = nn.BatchNorm2d(planes // 4)
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self.norm2 = nn.BatchNorm2d(planes // 4)
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self.norm3 = nn.BatchNorm2d(planes)
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if not stride == 1:
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self.norm4 = nn.BatchNorm2d(planes)
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elif norm_fn == "instance":
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self.norm1 = nn.InstanceNorm2d(planes // 4)
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self.norm2 = nn.InstanceNorm2d(planes // 4)
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self.norm3 = nn.InstanceNorm2d(planes)
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if not stride == 1:
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self.norm4 = nn.InstanceNorm2d(planes)
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elif norm_fn == "none":
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self.norm1 = nn.Sequential()
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self.norm2 = nn.Sequential()
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self.norm3 = nn.Sequential()
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if not stride == 1:
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self.norm4 = nn.Sequential()
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if stride == 1:
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self.downsample = None
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else:
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self.downsample = nn.Sequential(
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nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm4
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)
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def forward(self, x):
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y = x
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y = self.relu(self.norm1(self.conv1(y)))
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y = self.relu(self.norm2(self.conv2(y)))
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y = self.relu(self.norm3(self.conv3(y)))
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if self.downsample is not None:
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x = self.downsample(x)
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return self.relu(x + y)
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class BasicEncoder(nn.Module):
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def __init__(self, output_dim=128, norm_fn="batch", dropout=0.0, only_feat=False):
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super(BasicEncoder, self).__init__()
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self.norm_fn = norm_fn
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self.only_feat = only_feat
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if self.norm_fn == "group":
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self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64)
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elif self.norm_fn == "batch":
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self.norm1 = nn.BatchNorm2d(64)
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elif self.norm_fn == "instance":
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self.norm1 = nn.InstanceNorm2d(64)
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elif self.norm_fn == "none":
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self.norm1 = nn.Sequential()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
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self.relu1 = nn.ReLU(inplace=True)
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self.in_planes = 64
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self.layer1 = self._make_layer(64, stride=1)
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self.layer2 = self._make_layer(96, stride=2)
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self.layer3 = self._make_layer(128, stride=2)
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if not self.only_feat:
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# output convolution
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self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1)
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self.dropout = None
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if dropout > 0:
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self.dropout = nn.Dropout2d(p=dropout)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
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elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
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if m.weight is not None:
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nn.init.constant_(m.weight, 1)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def _make_layer(self, dim, stride=1):
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layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
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layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
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layers = (layer1, layer2)
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self.in_planes = dim
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return nn.Sequential(*layers)
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def forward(self, x, return_feature=False, mif=False):
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features = []
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# if input is list, combine batch dimension
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is_list = isinstance(x, tuple) or isinstance(x, list)
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if is_list:
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batch_dim = x[0].shape[0]
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x = torch.cat(x, dim=0)
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x_2 = F.interpolate(x, scale_factor=1 / 2, mode="bilinear", align_corners=False)
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x_4 = F.interpolate(x, scale_factor=1 / 4, mode="bilinear", align_corners=False)
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def f1(feat):
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feat = self.conv1(feat)
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feat = self.norm1(feat)
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feat = self.relu1(feat)
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feat = self.layer1(feat)
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return feat
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x = f1(x)
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features.append(x)
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x = self.layer2(x)
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if mif:
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x_2_2 = f1(x_2)
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features.append(torch.cat([x, x_2_2], dim=1))
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else:
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features.append(x)
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x = self.layer3(x)
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if mif:
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x_2_4 = self.layer2(x_2_2)
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x_4_4 = f1(x_4)
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features.append(torch.cat([x, x_2_4, x_4_4], dim=1))
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else:
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features.append(x)
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if not self.only_feat:
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x = self.conv2(x)
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if self.training and self.dropout is not None:
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x = self.dropout(x)
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if is_list:
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x = torch.split(x, [batch_dim, batch_dim], dim=0)
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features = [torch.split(f, [batch_dim, batch_dim], dim=0) for f in features]
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if return_feature:
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return x, features
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else:
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return x
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class SmallEncoder(nn.Module):
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def __init__(self, output_dim=128, norm_fn="batch", dropout=0.0):
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super(SmallEncoder, self).__init__()
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self.norm_fn = norm_fn
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if self.norm_fn == "group":
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self.norm1 = nn.GroupNorm(num_groups=8, num_channels=32)
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elif self.norm_fn == "batch":
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self.norm1 = nn.BatchNorm2d(32)
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elif self.norm_fn == "instance":
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self.norm1 = nn.InstanceNorm2d(32)
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elif self.norm_fn == "none":
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self.norm1 = nn.Sequential()
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self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3)
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self.relu1 = nn.ReLU(inplace=True)
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self.in_planes = 32
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self.layer1 = self._make_layer(32, stride=1)
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self.layer2 = self._make_layer(64, stride=2)
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self.layer3 = self._make_layer(96, stride=2)
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self.dropout = None
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if dropout > 0:
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self.dropout = nn.Dropout2d(p=dropout)
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self.conv2 = nn.Conv2d(96, output_dim, kernel_size=1)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
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elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
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if m.weight is not None:
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nn.init.constant_(m.weight, 1)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def _make_layer(self, dim, stride=1):
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layer1 = BottleneckBlock(self.in_planes, dim, self.norm_fn, stride=stride)
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layer2 = BottleneckBlock(dim, dim, self.norm_fn, stride=1)
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layers = (layer1, layer2)
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self.in_planes = dim
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return nn.Sequential(*layers)
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def forward(self, x):
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# if input is list, combine batch dimension
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is_list = isinstance(x, tuple) or isinstance(x, list)
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if is_list:
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batch_dim = x[0].shape[0]
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x = torch.cat(x, dim=0)
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x = self.conv1(x)
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x = self.norm1(x)
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x = self.relu1(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.conv2(x)
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if self.training and self.dropout is not None:
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x = self.dropout(x)
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if is_list:
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x = torch.split(x, [batch_dim, batch_dim], dim=0)
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return x
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