ComfyUI-LoRABlockSurgeon

ComfyUI-LoRABlockSurgeon

Two nodes. Measure where a LoRA actually stores its learned change, then apply only the blocks you want.

Nothing is written to disk. The .safetensors is opened read-only and the block selection is applied to an in-memory copy of the state dict, so the file is byte-identical after a run. There is no "save pruned copy" path by design.

Nodes

LoRA Block Profiler — lora_name, sort_by, top_n → report (STRING). Prints the Frobenius norm of the effective weight delta per transformer block, its share of total energy, a bar profile, and how many blocks hold 90% of the energy.

LoRA Block Filter (Apply) — model, lora_name, strength_model, blocks, mode → model, applied. Drop-in for LoraLoaderModelOnly with a block filter. blocks takes 31-35 or 0-2,31,35. mode is keep or drop.

What is measured, and why that specific quantity

The effective delta — the tensor actually added to the base weight — not the norms of the stored factors. LoRA has a free scale: multiply up by 10 and divide down by 10 and the delta is identical, so individual factor norms carry no information and only their product does.

• LoRA: ‖(alpha/rank) · up @ down‖_F, computed as sqrt(sum((A Aᵀ) ⊙ (Bᵀ B))) via the cyclic property of trace. Exact, and it never materialises the full delta — a rank-32 adapter on a 6144-wide layer is two 32×32 matrices instead of a 6144×6144 product.
• LoKr: ‖kron(w1, w2)‖_F = ‖w1‖_F · ‖w2‖_F. Exact; no Kronecker product is built. Composition follows ComfyUI's own weight_adapter/lokr.py.
• diff: the norm of the stored tensor.
• LoHa and anything else: reported as NOT MEASURED and excluded from every number, never folded into a zero. A silent zero would make a block look prunable when it was merely not understood.

Per-block aggregation sums in quadrature (sqrt(Σ nᵢ²)), the norm of the block's stacked deltas. A plain sum would over-rank blocks that simply contain more adapted layers.

Blocks that carry no index

Embedders, heads and final layers match no blocks.N pattern. They are grouped as unblocked in the profile and are always applied by the filter, in both modes.

Tests

python -m pytest tests/ -q      # 39 tests, no network, no GPU, no ComfyUI import

39 tests. The load-bearing ones check the fast norms against explicitly materialised products. If those drift, every number printed here is wrong.