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>
155 lines
5.3 KiB
Python
155 lines
5.3 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 FlowHead(nn.Module):
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def __init__(self, input_dim=128, hidden_dim=256):
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super(FlowHead, self).__init__()
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self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
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self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x):
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return self.conv2(self.relu(self.conv1(x)))
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class ConvGRU(nn.Module):
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def __init__(self, hidden_dim=128, input_dim=192 + 128):
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super(ConvGRU, self).__init__()
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self.convz = nn.Conv2d(hidden_dim + input_dim, hidden_dim, 3, padding=1)
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self.convr = nn.Conv2d(hidden_dim + input_dim, hidden_dim, 3, padding=1)
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self.convq = nn.Conv2d(hidden_dim + input_dim, hidden_dim, 3, padding=1)
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def forward(self, h, x):
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hx = torch.cat([h, x], dim=1)
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z = torch.sigmoid(self.convz(hx))
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r = torch.sigmoid(self.convr(hx))
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q = torch.tanh(self.convq(torch.cat([r * h, x], dim=1)))
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h = (1 - z) * h + z * q
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return h
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class SepConvGRU(nn.Module):
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def __init__(self, hidden_dim=128, input_dim=192 + 128):
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super(SepConvGRU, self).__init__()
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self.convz1 = nn.Conv2d(
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hidden_dim + input_dim, hidden_dim, (1, 5), padding=(0, 2)
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)
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self.convr1 = nn.Conv2d(
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hidden_dim + input_dim, hidden_dim, (1, 5), padding=(0, 2)
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)
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self.convq1 = nn.Conv2d(
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hidden_dim + input_dim, hidden_dim, (1, 5), padding=(0, 2)
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)
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self.convz2 = nn.Conv2d(
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hidden_dim + input_dim, hidden_dim, (5, 1), padding=(2, 0)
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)
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self.convr2 = nn.Conv2d(
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hidden_dim + input_dim, hidden_dim, (5, 1), padding=(2, 0)
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)
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self.convq2 = nn.Conv2d(
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hidden_dim + input_dim, hidden_dim, (5, 1), padding=(2, 0)
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)
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def forward(self, h, x):
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# horizontal
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hx = torch.cat([h, x], dim=1)
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z = torch.sigmoid(self.convz1(hx))
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r = torch.sigmoid(self.convr1(hx))
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q = torch.tanh(self.convq1(torch.cat([r * h, x], dim=1)))
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h = (1 - z) * h + z * q
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# vertical
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hx = torch.cat([h, x], dim=1)
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z = torch.sigmoid(self.convz2(hx))
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r = torch.sigmoid(self.convr2(hx))
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q = torch.tanh(self.convq2(torch.cat([r * h, x], dim=1)))
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h = (1 - z) * h + z * q
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return h
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class SmallMotionEncoder(nn.Module):
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def __init__(self, args):
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super(SmallMotionEncoder, self).__init__()
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cor_planes = args.corr_levels * (2 * args.corr_radius + 1) ** 2
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self.convc1 = nn.Conv2d(cor_planes, 96, 1, padding=0)
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self.convf1 = nn.Conv2d(2, 64, 7, padding=3)
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self.convf2 = nn.Conv2d(64, 32, 3, padding=1)
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self.conv = nn.Conv2d(128, 80, 3, padding=1)
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def forward(self, flow, corr):
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cor = F.relu(self.convc1(corr))
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flo = F.relu(self.convf1(flow))
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flo = F.relu(self.convf2(flo))
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cor_flo = torch.cat([cor, flo], dim=1)
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out = F.relu(self.conv(cor_flo))
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return torch.cat([out, flow], dim=1)
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class BasicMotionEncoder(nn.Module):
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def __init__(self, args):
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super(BasicMotionEncoder, self).__init__()
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cor_planes = args.corr_levels * (2 * args.corr_radius + 1) ** 2
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self.convc1 = nn.Conv2d(cor_planes, 256, 1, padding=0)
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self.convc2 = nn.Conv2d(256, 192, 3, padding=1)
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self.convf1 = nn.Conv2d(2, 128, 7, padding=3)
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self.convf2 = nn.Conv2d(128, 64, 3, padding=1)
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self.conv = nn.Conv2d(64 + 192, 128 - 2, 3, padding=1)
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def forward(self, flow, corr):
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cor = F.relu(self.convc1(corr))
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cor = F.relu(self.convc2(cor))
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flo = F.relu(self.convf1(flow))
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flo = F.relu(self.convf2(flo))
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cor_flo = torch.cat([cor, flo], dim=1)
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out = F.relu(self.conv(cor_flo))
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return torch.cat([out, flow], dim=1)
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class SmallUpdateBlock(nn.Module):
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def __init__(self, args, hidden_dim=96):
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super(SmallUpdateBlock, self).__init__()
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self.encoder = SmallMotionEncoder(args)
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self.gru = ConvGRU(hidden_dim=hidden_dim, input_dim=82 + 64)
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self.flow_head = FlowHead(hidden_dim, hidden_dim=128)
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def forward(self, net, inp, corr, flow):
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motion_features = self.encoder(flow, corr)
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inp = torch.cat([inp, motion_features], dim=1)
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net = self.gru(net, inp)
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delta_flow = self.flow_head(net)
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return net, None, delta_flow
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class BasicUpdateBlock(nn.Module):
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def __init__(self, args, hidden_dim=128, input_dim=128):
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super(BasicUpdateBlock, self).__init__()
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self.args = args
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self.encoder = BasicMotionEncoder(args)
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self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128 + hidden_dim)
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self.flow_head = FlowHead(hidden_dim, hidden_dim=256)
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self.mask = nn.Sequential(
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nn.Conv2d(128, 256, 3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(256, 64 * 9, 1, padding=0),
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)
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def forward(self, net, inp, corr, flow, upsample=True):
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motion_features = self.encoder(flow.to(inp), corr.to(inp))
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inp = torch.cat([inp, motion_features], dim=1)
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net = self.gru(net, inp)
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delta_flow = self.flow_head(net)
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# scale mask to balence gradients
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mask = 0.25 * self.mask(net)
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return net, mask, delta_flow
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