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Fix ray_pose for non float32 dtypes CORE-135
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This commit is contained in:
Talmaj Marinc 2026-05-12 13:15:14 +02:00
parent b911c99fda
commit 8fecf69df0
1 changed files with 13 additions and 5 deletions
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18
comfy/ldm/depth_anything_3/ray_pose.py
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@ -38,7 +38,10 @@ def _ql_decomposition(A: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
P = torch.tensor([[0, 0, 1], [0, 1, 0], [1, 0, 0]],
device=A.device, dtype=A.dtype)
A_tilde = A @ P
Q_tilde, R_tilde = torch.linalg.qr(A_tilde)
# CUDA QR is not implemented for fp16/bf16; upcast just for this call.
Q_tilde, R_tilde = torch.linalg.qr(A_tilde.float())
Q_tilde = Q_tilde.to(A.dtype)
R_tilde = R_tilde.to(A.dtype)
Q = Q_tilde @ P
L = P @ R_tilde @ P
d = torch.diag(L)
@ -75,7 +78,9 @@ def _find_homography_weighted_lsq(
A1 = torch.cat([-x * w, -y * w, -w, zeros, zeros, zeros, x * u * w, y * u * w, u * w], dim=1)
A2 = torch.cat([zeros, zeros, zeros, -x * w, -y * w, -w, x * v * w, y * v * w, v * w], dim=1)
A = torch.cat([A1, A2], dim=0) # (2N, 9)
_, _, Vh = torch.linalg.svd(A)
# CUDA SVD is not implemented for fp16/bf16; upcast just for this call.
_, _, Vh = torch.linalg.svd(A.float())
Vh = Vh.to(A.dtype)
H = Vh[-1].reshape(3, 3)
return H / H[-1, -1]
@ -96,7 +101,9 @@ def _find_homography_weighted_lsq_batched(
A1 = torch.cat([-x * w, -y * w, -w, zeros, zeros, zeros, x * u * w, y * u * w, u * w], dim=2)
A2 = torch.cat([zeros, zeros, zeros, -x * w, -y * w, -w, x * v * w, y * v * w, v * w], dim=2)
A = torch.cat([A1, A2], dim=1) # (B, 2K, 9)
_, _, Vh = torch.linalg.svd(A)
# CUDA SVD is not implemented for fp16/bf16; upcast just for this call.
_, _, Vh = torch.linalg.svd(A.float())
Vh = Vh.to(A.dtype)
H = Vh[:, -1].reshape(B, 3, 3)
return H / H[:, 2:3, 2:3]
@ -260,8 +267,9 @@ def _camray_to_caminfo(
max_inlier_num=8000,
)
# Flip sign on dets that come out < 0 (so that the QL produces a
# right-handed rotation).
flip = torch.linalg.det(A) < 0
# right-handed rotation). ``det`` lacks fp16/bf16 CUDA kernels, so
# do the comparison in fp32.
flip = torch.linalg.det(A.float()) < 0
A = torch.where(flip[:, None, None], -A, A)
A_list.append(A)
A = torch.cat(A_list, dim=0) # (B*S, 3, 3)