silveroxides-ComfyUI-ModelUtils/docs/model_analysis.md

Two-Model Similarity Analysis

These nodes compare two safetensors models without merging them or writing an output file. Separate nodes are provided for checkpoints, diffusion models, text encoders, LoRAs, and embeddings.

Outputs

comparison_report contains conventional numerical differences and topology findings. cwb_report separately contains diagnostics derived from the similarity, alignment, and consensus stages of Consensus-Weighted Blending. documentation contains this reference.

The reports begin with a global summary, followed by inferred blockwise values and one entry for every comparable tensor. Block names are approximated from the tensor-key hierarchy through its first numeric path component; they are not an architecture-specific interpretation.

Standard metrics

• MAE, MSE, and RMSE describe absolute error at the original weight scale.
• Maximum absolute difference identifies the largest individual change.
• Relative L2 is ||A-B|| / ((||A||+||B||)/2) and is scale-aware.
• Cosine similarity compares direction; Pearson correlation compares centered variation.
• Exact equality, sign agreement, norm ratio, and finite-value coverage expose differences hidden by averages.

The global section reports both parameter-weighted metrics and equal-layer averages. No arbitrary combined similarity score is assigned. Undefined zero-norm or constant-tensor statistics are shown as N/A.

Only shared, same-shape floating tensors enter numerical aggregates. Missing keys and shape mismatches are listed separately and are never padded, cropped, or replaced with zeros. Non-floating tensors receive an exact-equality check. Quantized models carrying ComfyUI quantization metadata are rejected because encoded storage values do not represent directly comparable model weights.

CWB diagnostics

The CWB report provides pairwise row-cosine statistics and each input's affinity to both mean and median consensus. It stops before CWB's merge-weighting stage: no thresholds, contribution weights, diversity weighting, vector reconstitution, norm rescaling, or output writer are used.

Checkpoint, diffusion-model, and text-encoder tensors always retain their fixed coordinate alignment. Embedding and LoRA nodes expose one optional cwb_similarity_alignment control. When enabled, it performs threshold-independent greedy one-to-one matching derived from CWB and reports coverage, matched-score statistics, and improvement over index alignment. This operation is quadratic and may be slow for large embeddings.

LoRA down/up tensors are treated as one logical layer. Rank-component matching uses the product of down and up cosine similarity, including CWB's paired-sign equivalence for comparison. No source tensor is modified.

Memory and failure handling

Inputs always use Unified Efficient Loader's low-memory streaming mode. Matching A/B tensors are loaded concurrently through bounded async_stream() pipelines with batch_size=1 and prefetch_batches=1, but no subsequent logical layer is requested early. A complete LoRA down/up pair is loaded once and used for both reports. CUDA inputs use pinned transfer while retaining the current CPU source tensors for fallback.

Calculations use FP32. When CUDA processing runs out of memory, CUDA memory is cleared and the current logical layer is retried once on CPU. After success or failure, all tensor references for that layer are released and every loaded key is marked processed exactly once before advancing. Both reports list every CPU fallback. A failed CPU retry or any non-OOM error stops analysis with the layer name in the error.

top_weight_differences bounds the scalar-coordinate list; every comparable tensor is still present in the layerwise report. DOCUMENTATION ONLY loads no model data.