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Remove torch.no_grad

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Eugene Fairley 2025-07-16 13:03:29 -07:00
parent 932002dcf2
commit ef382b319d
1 changed files with 28 additions and 29 deletions
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57
comfy/model_base.py
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@ -1125,39 +1125,38 @@ def patch_motion(
topk: int = 2, topk: int = 2,
): ):
"""Apply motion patching based on tracks""" """Apply motion patching based on tracks"""
with torch.no_grad(): _, T, H, W = vid.shape
_, T, H, W = vid.shape N = tracks.shape[2]
N = tracks.shape[2] _, tracks_xy, visible = torch.split(
_, tracks_xy, visible = torch.split( tracks, [1, 2, 1], dim=-1
tracks, [1, 2, 1], dim=-1 ) # (B, T, N, 2) | (B, T, N, 1)
) # (B, T, N, 2) | (B, T, N, 1) tracks_n = tracks_xy / torch.tensor([W / min(H, W), H / min(H, W)], device=tracks_xy.device)
tracks_n = tracks_xy / torch.tensor([W / min(H, W), H / min(H, W)], device=tracks_xy.device) tracks_n = tracks_n.clamp(-1, 1)
tracks_n = tracks_n.clamp(-1, 1) visible = visible.clamp(0, 1)
visible = visible.clamp(0, 1)
xx = torch.linspace(-W / min(H, W), W / min(H, W), W) xx = torch.linspace(-W / min(H, W), W / min(H, W), W)
yy = torch.linspace(-H / min(H, W), H / min(H, W), H) yy = torch.linspace(-H / min(H, W), H / min(H, W), H)
grid = torch.stack(torch.meshgrid(yy, xx, indexing="ij")[::-1], dim=-1).to( grid = torch.stack(torch.meshgrid(yy, xx, indexing="ij")[::-1], dim=-1).to(
tracks_xy.device tracks_xy.device
) )
tracks_pad = tracks_xy[:, 1:] tracks_pad = tracks_xy[:, 1:]
visible_pad = visible[:, 1:] visible_pad = visible[:, 1:]
visible_align = visible_pad.view(T - 1, 4, *visible_pad.shape[2:]).sum(1) visible_align = visible_pad.view(T - 1, 4, *visible_pad.shape[2:]).sum(1)
tracks_align = (tracks_pad * visible_pad).view(T - 1, 4, *tracks_pad.shape[2:]).sum( tracks_align = (tracks_pad * visible_pad).view(T - 1, 4, *tracks_pad.shape[2:]).sum(
1 1
) / (visible_align + 1e-5) ) / (visible_align + 1e-5)
dist_ = ( dist_ = (
(tracks_align[:, None, None] - grid[None, :, :, None]).pow(2).sum(-1) (tracks_align[:, None, None] - grid[None, :, :, None]).pow(2).sum(-1)
) # T, H, W, N ) # T, H, W, N
weight = torch.exp(-dist_ * temperature) * visible_align.clamp(0, 1).view( weight = torch.exp(-dist_ * temperature) * visible_align.clamp(0, 1).view(
T - 1, 1, 1, N T - 1, 1, 1, N
) )
vert_weight, vert_index = torch.topk( vert_weight, vert_index = torch.topk(
weight, k=min(topk, weight.shape[-1]), dim=-1 weight, k=min(topk, weight.shape[-1]), dim=-1
) )
grid_mode = "bilinear" grid_mode = "bilinear"
point_feature = torch.nn.functional.grid_sample( point_feature = torch.nn.functional.grid_sample(