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Add GIMM-VFI support (NeurIPS 2024) with single-pass arbitrary-timestep interpolation

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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>
This commit is contained in:
Ethanfel
2026-02-13 13:11:45 +01:00
parent 3c3d4b2537
commit d642255e70
56 changed files with 9774 additions and 1 deletions
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gimm_vfi_arch/generalizable_INR/raft/other_raft.py Normal file
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .update import BasicUpdateBlock, SmallUpdateBlock
from .extractor import BasicEncoder, SmallEncoder
from .corr import BidirCorrBlock, AlternateCorrBlock
from .utils.utils import bilinear_sampler, coords_grid, upflow8
try:
autocast = torch.cuda.amp.autocast
except:
# dummy autocast for PyTorch < 1.6
class autocast:
def __init__(self, enabled):
pass
def __enter__(self):
pass
def __exit__(self, *args):
pass
# BiRAFT
class RAFT(nn.Module):
def __init__(self, args):
super(RAFT, self).__init__()
self.args = args
if args.small:
self.hidden_dim = hdim = 96
self.context_dim = cdim = 64
args.corr_levels = 4
args.corr_radius = 3
self.corr_levels = 4
self.corr_radius = 3
else:
self.hidden_dim = hdim = 128
self.context_dim = cdim = 128
args.corr_levels = 4
args.corr_radius = 4
self.corr_levels = 4
self.corr_radius = 4
if "dropout" not in args._get_kwargs():
self.args.dropout = 0
if "alternate_corr" not in args._get_kwargs():
self.args.alternate_corr = False
# feature network, context network, and update block
if args.small:
self.fnet = SmallEncoder(
output_dim=128, norm_fn="instance", dropout=args.dropout
)
self.cnet = SmallEncoder(
output_dim=hdim + cdim, norm_fn="none", dropout=args.dropout
)
self.update_block = SmallUpdateBlock(self.args, hidden_dim=hdim)
else:
self.fnet = BasicEncoder(
output_dim=256, norm_fn="instance", dropout=args.dropout
)
self.cnet = BasicEncoder(
output_dim=hdim + cdim, norm_fn="batch", dropout=args.dropout
)
self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim)
def freeze_bn(self):
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
def build_coord(self, img):
N, C, H, W = img.shape
coords = coords_grid(N, H // 8, W // 8, device=img.device)
return coords
def initialize_flow(self, img, img2):
"""Flow is represented as difference between two coordinate grids flow = coords1 - coords0"""
assert img.shape == img2.shape
N, C, H, W = img.shape
coords01 = coords_grid(N, H // 8, W // 8, device=img.device)
coords02 = coords_grid(N, H // 8, W // 8, device=img.device)
coords1 = coords_grid(N, H // 8, W // 8, device=img.device)
coords2 = coords_grid(N, H // 8, W // 8, device=img.device)
# optical flow computed as difference: flow = coords1 - coords0
return coords01, coords02, coords1, coords2
def upsample_flow(self, flow, mask):
"""Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination"""
N, _, H, W = flow.shape
mask = mask.view(N, 1, 9, 8, 8, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold(8 * flow, [3, 3], padding=1)
up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
up_flow = torch.sum(mask * up_flow, dim=2)
up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
return up_flow.reshape(N, 2, 8 * H, 8 * W)
def get_corr_fn(self, image1, image2, projector=None):
# run the feature network
with autocast(enabled=self.args.mixed_precision):
fmaps, feats = self.fnet([image1, image2], return_feature=True)
fmap1, fmap2 = fmaps
fmap1 = fmap1.float()
fmap2 = fmap2.float()
corr_fn1 = None
if self.args.alternate_corr:
corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
if projector is not None:
corr_fn1 = AlternateCorrBlock(
projector(feats[-1][0]),
projector(feats[-1][1]),
radius=self.args.corr_radius,
)
else:
corr_fn = BidirCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
if projector is not None:
corr_fn1 = BidirCorrBlock(
projector(feats[-1][0]),
projector(feats[-1][1]),
radius=self.args.corr_radius,
)
if corr_fn1 is None:
return corr_fn, corr_fn
else:
return corr_fn, corr_fn1
def get_corr_fn_from_feat(self, fmap1, fmap2):
fmap1 = fmap1.float()
fmap2 = fmap2.float()
if self.args.alternate_corr:
corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
else:
corr_fn = BidirCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
return corr_fn
def forward(
self,
image1,
image2,
iters=12,
flow_init=None,
upsample=True,
test_mode=False,
corr_fn=None,
mif=False,
):
"""Estimate optical flow between pair of frames"""
assert flow_init is None
image1 = 2 * (image1 / 255.0) - 1.0
image2 = 2 * (image2 / 255.0) - 1.0
image1 = image1.contiguous()
image2 = image2.contiguous()
hdim = self.hidden_dim
cdim = self.context_dim
if corr_fn is None:
corr_fn, _ = self.get_corr_fn(image1, image2)
# # run the feature network
# with autocast(enabled=self.args.mixed_precision):
# fmap1, fmap2 = self.fnet([image1, image2])
# fmap1 = fmap1.float()
# fmap2 = fmap2.float()
# if self.args.alternate_corr:
# corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
# else:
# corr_fn = BidirCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
# run the context network
with autocast(enabled=self.args.mixed_precision):
# for image1
cnet1, features1 = self.cnet(image1, return_feature=True, mif=mif)
net1, inp1 = torch.split(cnet1, [hdim, cdim], dim=1)
net1 = torch.tanh(net1)
inp1 = torch.relu(inp1)
# for image2
cnet2, features2 = self.cnet(image2, return_feature=True, mif=mif)
net2, inp2 = torch.split(cnet2, [hdim, cdim], dim=1)
net2 = torch.tanh(net2)
inp2 = torch.relu(inp2)
coords01, coords02, coords1, coords2 = self.initialize_flow(image1, image2)
# if flow_init is not None:
# coords1 = coords1 + flow_init
# flow_predictions1 = []
# flow_predictions2 = []
for itr in range(iters):
coords1 = coords1.detach()
coords2 = coords2.detach()
corr1, corr2 = corr_fn(coords1, coords2) # index correlation volume
flow1 = coords1 - coords01
flow2 = coords2 - coords02
with autocast(enabled=self.args.mixed_precision):
net1, up_mask1, delta_flow1 = self.update_block(
net1, inp1, corr1, flow1
)
net2, up_mask2, delta_flow2 = self.update_block(
net2, inp2, corr2, flow2
)
# F(t+1) = F(t) + \Delta(t)
coords1 = coords1 + delta_flow1
coords2 = coords2 + delta_flow2
flow_low1 = coords1 - coords01
flow_low2 = coords2 - coords02
# upsample predictions
if up_mask1 is None:
flow_up1 = upflow8(coords1 - coords01)
flow_up2 = upflow8(coords2 - coords02)
else:
flow_up1 = self.upsample_flow(coords1 - coords01, up_mask1)
flow_up2 = self.upsample_flow(coords2 - coords02, up_mask2)
# flow_predictions.append(flow_up)
return flow_up1, flow_up2, flow_low1, flow_low2, features1, features2
# if test_mode:
# return coords1 - coords0, flow_up
# return flow_predictions