xinyun/ComfyUI
1
0
Fork 0
mirror of https://git.datalinker.icu/comfyanonymous/ComfyUI synced 2026-09-02 16:17:04 +08:00
Code Issues Packages Projects Releases Wiki Activity

Remove extra functions

Browse Source
This commit is contained in:
Eugene Fairley 2025-07-11 10:19:13 -07:00
parent b293fa4d5b
commit 67c4e7d98d
1 changed files with 0 additions and 45 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

45
comfy_extras/nodes_wan.py
View File

@ -416,7 +416,6 @@ def parse_json_tracks(tracks):
tracks_data = [] tracks_data = []
return tracks_data return tracks_data
def tracks_to_tensor(tracks_data, length, width, height, batch_size=1): def tracks_to_tensor(tracks_data, length, width, height, batch_size=1):
"""Convert parsed track data to tensor format (B, T, N, 4)""" """Convert parsed track data to tensor format (B, T, N, 4)"""
if not tracks_data: if not tracks_data:
@ -447,50 +446,6 @@ def tracks_to_tensor(tracks_data, length, width, height, batch_size=1):
return tracks_tensor return tracks_tensor
def ind_sel(target: torch.Tensor, ind: torch.Tensor, dim: int = 1):
"""Index selection utility function"""
assert (
len(ind.shape) > dim
), "Index must have the target dim, but get dim: %d, ind shape: %s" % (dim, str(ind.shape))
target = target.expand(
*tuple(
[ind.shape[k] if target.shape[k] == 1 else -1 for k in range(dim)]
+ [
-1,
]
* (len(target.shape) - dim)
)
)
ind_pad = ind
if len(target.shape) > dim + 1:
for _ in range(len(target.shape) - (dim + 1)):
ind_pad = ind_pad.unsqueeze(-1)
ind_pad = ind_pad.expand(*(-1,) * (dim + 1), *target.shape[(dim + 1) : :])
return torch.gather(target, dim=dim, index=ind_pad)
def merge_final(vert_attr: torch.Tensor, weight: torch.Tensor, vert_assign: torch.Tensor):
"""Merge vertex attributes with weights"""
target_dim = len(vert_assign.shape) - 1
if len(vert_attr.shape) == 2:
assert vert_attr.shape[0] > vert_assign.max()
new_shape = [1] * target_dim + list(vert_attr.shape)
tensor = vert_attr.reshape(new_shape)
sel_attr = ind_sel(tensor, vert_assign.type(torch.long), dim=target_dim)
else:
assert vert_attr.shape[1] > vert_assign.max()
new_shape = [vert_attr.shape[0]] + [1] * (target_dim - 1) + list(vert_attr.shape[1:])
tensor = vert_attr.reshape(new_shape)
sel_attr = ind_sel(tensor, vert_assign.type(torch.long), dim=target_dim)
final_attr = torch.sum(sel_attr * weight.unsqueeze(-1), dim=-2)
return final_attr
def process_tracks(tracks_np: np.ndarray, frame_size: Tuple[int, int], num_frames, quant_multi: int = 8, **kwargs): def process_tracks(tracks_np: np.ndarray, frame_size: Tuple[int, int], num_frames, quant_multi: int = 8, **kwargs):
# tracks: shape [t, h, w, 3] => samples align with 24 fps, model trained with 16 fps. # tracks: shape [t, h, w, 3] => samples align with 24 fps, model trained with 16 fps.
# frame_size: tuple (W, H) # frame_size: tuple (W, H)