feat: Analyze LoRA effects on a base model

docs/lora_model_analysis.md

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+# 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.