SciPy, einops and torchsde: distinguish missing imports from failed operations

Keep imports separate from operations

SciPy, einops and torchsde have different roles: scientific computing, tensor rearrangement and differentiable SDE solving respectively.123 Their presence in a traceback does not itself establish a missing package.

python -m pip show scipy einops torchsde numpy torch

Our suggested workflow has an import checkpoint followed by an operation checkpoint. Install a genuinely missing dependency according to the node's requirements. When execution already reaches a function, preserve the input shapes, dtype, parameters and first complete exception.

For SciPy native-loading failures, retain NumPy and platform details. For an einops pattern error, keep the dimension expression and actual sizes; those numbers are diagnostic evidence, not noise to remove. For a torchsde runtime error, preserve solver settings rather than silently substituting a different operation.

Build a small reproduction and compare one change at a time. Report a working import separately from a working node. A new package with a similar name is not a justified repair, and an untested version combination remains unverified.

Related checks

• Keep a reproducible ComfyUI repair record without exposing secrets
• Where to run ComfyUI commands: PowerShell, CMD and virtual environments

Research draft. No local/GPU reproduction or independent editorial sign-off. Upstream statements and our recommendations are distinguished.