May also resolve#9784 — the mask normalization fixes a class of dimensionality mismatches that can cause the `y, x = torch.where(mask)` crash in `get_mask_aabb`, though the root cause in that report is unconfirmed.
## Summary
`resolve_areas_and_cond_masks_multidim` assumes 2D spatial masks. This breaks for 1D audio models (StableAudio1, ACEAudio15) because upstream code (`ConditioningSetMask`, `set_mask_for_conditioning`) unconditionally unsqueezes masks with `ndim < 3`, corrupting valid `[B, L]` masks into `[1, B, L]` before they reach the sampler.
This PR:
- Normalizes masks to `[batch, *spatial_dims]` using `dims` as the source of truth
- Adds a 1D resize path via `F.interpolate(mode='linear')`
- Guards `set_area_to_bounds` with `len(dims) == 2` to prevent crashes on non-2D masks (the existing `get_mask_aabb` and `H, W, Y, X` unpacking are 2D-only)
The root cause is the hardcoded `if len(mask.shape) < 3` in `nodes.py:242` and `hooks.py:725`. Fixing it there would require threading latent dimensionality into the conditioning nodes — a much larger change. Normalizing in `resolve_areas_and_cond_masks_multidim` where `dims` is already available is the minimal fix.
Fully backwards compatible for existing 2D image and 3D video workflows.
## Test plan
- [x] 26 unit tests covering 1D/2D/3D mask normalization, resize, and `set_area_to_bounds` guard (`tests-unit/comfy_test/samplers_test.py`)
- [x] 2D image regression with hook masking: [lorahookmasking.json](https://github.com/Kosinkadink/ComfyUI/blob/workflows/lorahookmasking.json)
- [x] 2D image with `set_area_to_bounds` ("mask bounds" mode) — no crash, correct area computation
- [x] 1D audio with conditioning mask: [acestep-1.5-prompt-lora-blending.json](https://github.com/ryanontheinside/ComfyUI_RyanOnTheInside/blob/main/examples/ace1.5/acestep-1.5-prompt-lora-blending.json) (requires custom nodes that patch this function pending upstream)
