31 lines
1.9 KiB
Markdown
31 lines
1.9 KiB
Markdown
# LoRA-on-model analysis
|
||
|
||
**Analyze LoRA Effect on Diffusion Model** takes a LoRA as `model_a` and a base
|
||
diffusion model as `model_b`. It applies the LoRA transiently and compares the
|
||
original base weights against the patched weights. The measured update is
|
||
`patched − original`; no merged checkpoint, delta file, or inference is produced.
|
||
|
||
The comparison and CWB reports use original base as A and patched base as B.
|
||
The CSV outputs contain per-tensor difference metrics and row cosine summaries.
|
||
Unchanged floating base tensors remain in the analysis, so global metrics reflect
|
||
the impact on the selected base model, not just the layers touched by the LoRA.
|
||
Application and statistics use FP32, matching the existing analysis arithmetic;
|
||
this does not simulate rounding to a separately saved model dtype.
|
||
|
||
LoRA strength defaults to 1. Alpha/rank normalization, DoRA, LoCon, and reshape
|
||
handling use the existing application path and ComfyUI adapter. Direct deltas,
|
||
including bias deltas, are applied; explicit `set_weight` patches replace weights
|
||
regardless of strength, matching the existing application behavior.
|
||
|
||
Exclude Patterns and Include Mode filter **base tensor keys** before loading.
|
||
Unmapped LoRA groups and unused source tensors are listed separately. Unsupported
|
||
low-bit work units and non-floating base tensors are reported and omitted from
|
||
numeric metrics; they are not decoded or represented as successfully analyzed.
|
||
Application errors fail the operation rather than report a failed patch as zero
|
||
impact. A CUDA out-of-memory error retries the complete affected unit on CPU.
|
||
|
||
Loading uses the shared UEL work-unit streamer: one base tensor and its required
|
||
adapter tensors, batch size 1 and prefetch depth 1. Sources are marked processed
|
||
and streams closed on success or failure. Only scalar statistics and bounded top
|
||
differences survive each unit. No whole model or LoRA tensor dictionary is built.
|