Kohya-style files carry an already-underscored base (lora_unet_layers_0_
mlp_gate_proj), which the loader resolves by comparing network_prefix +
path.replace('.', '_') against each module's stamped name, so both sides
are underscored and the file loads. The analyzer instead looked the base
up as a literal dotted module path, so every module of such a file was
reported unmatched: 76 files in a local collection, including 36 of 57
anima and 5 of 10 chroma.
Fall back to a stamped-name index when the direct lookup misses. Dotted
bases are unaffected.
The analyzer only mapped plain-lora groups, so a file carrying no plain
lora (a pure lokr, for example) analyzed zero modules and fell through to
a 1.0 default: it reported perfect fidelity for exactly the files that
degrade most. Measured on the shipped krea 2 uint4 checkpoint, those
files land between 0.04 and 0.34.
Every targeted module is now rebuilt with the loader's own module class
and its delta read from the production calc_updown, so lokr, loha, oft,
full, ia3, glora, norm and the dora / dense-bias / diff_b variants are
measured as they apply; factor-path eligibility is decided by calling the
loader's own predicate. Modules carrying several families sum their
deltas the way the loader stacks them, and a family the tool cannot
rebuild is reported instead of counting as clean.
- report per-module applied fidelity (1.0 on the factor path, measured
rho on the requantize path) as a median and an energy-weighted mean
- add --dtype bf16 to measure the unquantized reference rather than
assert it
- drop the per-module empty_cache: it cost 16ms per module against 1ms
of reuse, and the caching allocator already reuses the buffers
- keep shard handles open across modules
Checkpoints quantized without hadamard must attach factors unrotated;
checkpoints carrying their own svd correction must keep it under apply
and get the original factors back on remove. Both pinned in both svd
layouts.
Offline analyzer for a (model, lora) pair: maps lora modules onto the
transformer, measures per-module delta-to-step ratio and requantize
retention, and reports factor-path eligibility. Loads pre-quantized
sdnq repos or simulates quantization on bf16 repos; supports --json
and --fail-under for scripted checks.
Baking a lora into a quantized weight requantizes it, and on low-bit
formats round-to-nearest erases sub-step deltas (uint4 retains roughly
2/group_size of the signal). Plain lora deltas now ride the sdnq svd
side-channel: factors append to svd_up/svd_down with the down factor
hadamard-rotated, applied by the dequantizer at full precision in every
forward mode. Apply and remove are exact and take no weight backup.
- non-factorable families (dora, lokr, loha, oft, cp mid, dense bias)
fall back to requantize with a per-pass summary warning
- native fuse now honors the quantized-model guard; fuse requantized in
place on every network swap and accumulated drift
- layers that fell back on a mixed set restore from backup before
re-entering the factor path; untargeted quantized layers are no
longer flagged
- test/test-sdnq-lora-factors.py pins the erasure law, factor-path
exactness, memory accounting and set transitions
A delta that does not fit its target module cannot apply, and applying only
the layers that do fit leaves the model in a state nothing was trained for,
so try_load_chain drops the whole file when any family reports a mismatch.
Bias deltas were never checked against the target bias and could only surface
at apply time; a module with no bias stays a non-mismatch, since whole
architectures are built bias=False.
- check bias deltas against the module bias in the lora, norm and full loaders
- carry the mismatch count on the network so the chain can refuse the file
- record refused writes in the infotext so a partial apply is not read as clean
- point the krea2 full-diff test at a module that has a bias
network_add_weights defaulted its base tensor to self.weight for the bias
delta as well, so in fuse mode a diff_b was added to the weight matrix and
the result written into the bias. Layers where in and out differ threw a
shape error and had the weight matrix installed as their bias, square layers
broadcast silently, and either way the summary still counted the delta as
applied.
- pick the base tensor from the bias flag
- name the layer, target and both shapes in the mismatch error
- return which of (weight, bias) took a write, count the rest as refused
- report refused= on partially applied and partially removed networks
- cover both apply paths in test/test-lora-apply.py
CalamitousFelicitousness
Vladimir Mandic
Dity0