silveroxides-ComfyUI-ModelUtils/docs/consensus_mergers.md

Consensus-Weighted Blending Mergers

The dedicated CWB nodes merge two or three safetensors files using Consensus-Weighted Blending. They calculate an element-wise mean or median consensus, compare each aligned source vector to that consensus, and normalize similarity-derived weights before producing the output vector.

The broad mathematical and integration reference is maintained in references/Spec-CWB. This document describes the behavior actually exposed by this repository's offline merger nodes. The specification covers additional contexts, including online conditioning and spatial fusion, which are not controls of these nodes.

Node families

Two-input and three-input variants are provided for checkpoints, standalone diffusion models, text encoders, LoRAs, and embeddings. LoRA also provides a Multi-Merge variant for 2 to 8 equal-prior inputs.

Embedding coalescing

CWB Embedding Self-Coalesce reduces one embedding by replacing mutually nearest body-vector pairs that meet both cosine and normalized-position limits. CWB Embedding Multi-Merge accepts 2 to 8 embeddings, performs normal CWB alignment first, then applies the same reduction. A target row count of zero continues until no eligible pair remains.

Vision Boundary Embeddings preserves encoded vision-start and vision-end rows unchanged and applies CWB only to their interior. Normal vision inputs validate their outer rows before writing. Legacy Boundary Search trims accidental outer template rows by matching a selected known-clean visual embedding; an absent, ambiguous, or low-similarity boundary pair is an error rather than a guessed crop. Disable Vision Boundary Embeddings for open textual-inversion tensors.

Model A supplies output metadata and anchors shared-layer shape, naming, and mismatch behavior. The output key set is the union of all inputs. A valid tensor found only in a secondary input is copied unchanged. When Model A lacks a tensor that is present in multiple secondary inputs, those available sources are CWB-merged without inserting a zero contribution for Model A. Embedding mergers additionally use the longest compatible first dimension as their alignment reference.

Presets and custom controls

Presets are separated by merge type. Dense tensors, embeddings, and LoRA factor pairs never share one menu. Every named preset supplies every CWB setting and cannot inherit hidden widget state.

Manual controls live in the separate CWB Custom Configuration node. Connecting its CWB_CONFIG output completely overrides the selected preset. Leaving it unconnected uses the named preset.

Preset names expose categorical and boolean behavior. _idx and _sim identify alignment, _medn and _mean identify consensus, and _rn, _dsc, _softcb, and _pcp identify enabled boolean transformations. Dense presets omit alignment tokens because dense model coordinates are fixed.

Similarity alignment greedily matches source rows to the reference by cosine similarity. Index alignment keeps absolute row positions. Position weight can bias similarity matches toward nearby normalized positions. Preserve Common Prefix copies the longest numerically identical leading row span directly from the first input.

Inputs have equal prior weight. The similarity calculation supplies the effective contribution for each aligned vector. If every candidate is rejected or receives zero weight, CWB falls back to equal weighting.

Weighting sequence

For each aligned vector group, the merger performs these operations in order:

1. Compute an element-wise mean or median consensus.
2. Measure each vector's cosine similarity to that consensus.
3. Reject similarities below similarity_threshold.
4. Optionally remap unequal accepted similarities with Dynamic Similarity Contrast.
5. Apply similarity^power_alpha and, when enabled, the diversity bandpass.
6. Normalize the resulting contributor weights and calculate their weighted sum. If no usable weight remains, use equal contributor weights.
7. Optionally apply rescale_norm.
8. Apply global_scale.

Complete control reference

• Execution Mode: MERGE writes an output. DOCUMENTATION ONLY returns this document without opening model inputs.

• Model A: primary contributor and preservation anchor. It supplies output metadata and anchors shared names and shapes. The output key set remains the union of all inputs.

• Model B / Model C: additional equal-prior contributors. CWB derives their effective per-vector influence from consensus similarity; these nodes do not expose model-level weights.

• CWB Preset: complete use-case settings selected from the node's own dense, embedding, or LoRA registry. A connected CWB Config overrides it completely.

• Consensus Type: mean uses the arithmetic center; median uses the element-wise median and is less sensitive to coordinate outliers.

• Alignment Method: index pairs first-axis vectors by position; similarity uses greedy one-to-one cosine matching. Row reordering is enabled only for embeddings and paired LoRA rank components. Ordinary checkpoint, diffusion-model, and text-encoder tensors retain fixed coordinates.

• Alignment Threshold: minimum original cosine score for accepting a greedy row match. It does not filter already-paired contributors during CWB weighting; that is similarity_threshold.

• Similarity Threshold: minimum cosine similarity between a participating vector and the group consensus. Rejected vectors receive zero weight. If all are rejected, the implementation falls back to equal weights.

• Power Alpha: exponent applied to accepted non-negative similarities. Values above 1 increasingly favor contributors closest to consensus. Zero makes every accepted contributor's similarity term equal to one.

• Diversity Beta: exponent for the diversity bandpass. Values above zero suppress vectors extremely close to consensus relative to more varied accepted vectors. Zero disables this term.

• Rescale Norm: after blending, preserve the merged direction but replace its L2 magnitude with the mean L2 magnitude of the participating vectors:

  merged = merged / ||merged|| × mean(||source_i||)

  A zero merged vector remains unchanged. This is performed per aligned row, not once per complete tensor. For LoRA, it is performed separately for each paired A row and B column, so enabling it can materially change the effective low-rank delta magnitude.

• Global Scale: multiplies the merged result after CWB. For LoRA it is applied once through B/up so the represented delta is scaled once rather than once per factor.

• Dynamic Similarity Contrast: when consensus similarities differ, remaps them into the range 0.7–1.0 before alpha/beta weighting. It does not alter row-alignment scores.

• Soft Comfort Bandpass: when beta is positive, uses (1.5 - similarity)^beta instead of the narrower (1.001 - similarity)^beta. It has no effect when beta is zero.

• Position Weight: blends normalized positional affinity into greedy row selection. Zero uses cosine only and one uses positional affinity only. It affects only similarity-aligned embeddings and LoRA rank components; the original cosine score is still checked against the alignment threshold.

• Preserve Common Prefix: copies Model A's longest numerically identical leading first-axis span before CWB. The current implementation applies this to all tensors with at least two dimensions, including fixed-coordinate model tensors.

• Mismatch Mode: for anchored tensors that are missing or incompatible, skip preserves the anchor, zeros supplies a compatible zero contribution where possible, and error aborts. A valid tensor supplied by only one secondary input is copied unchanged.

• Output Filename: filename without extension, written atomically beneath the matching category in ComfyUI's models directory.

• Save Dtype: requested dtype for generated floating tensors. Unless Override Dtype is enabled, any participating FP32 source keeps the generated logical result FP32.

• Process Device: device for per-layer FP32 arithmetic. A CUDA OOM releases the failed working set and retries only that layer on CPU.

• Exclude Patterns: matching layers are preserved from their anchor instead of merged.

• Discard Patterns: matching tensors or logical LoRA groups are absent from the output.

• Glob Patterns: switches exclude/discard syntax from regular expressions to shell-style globs. Pattern fields accept one entry per line.

• Lazy Load: enables UEL low-memory loading so tensors are streamed by work unit and explicitly released.

• Force Clear Cache: runs garbage collection and clears the CUDA allocator cache before each layer. This can reduce retained memory at a substantial speed cost.

• Override Dtype: forces generated floating tensors to Save Dtype. Guarded low-bit tensors and enabled 1D direct differences are exempt.

• Include 1D Diffs (LoRA nodes): enables CWB merging of recognized 1D direct-difference tensors in FP32. When disabled, shared tensors preserve Model A and secondary-only tensors preserve their earliest provider.

Scalars and 1D generic tensors use direct mean/median handling. Alignment, similarity weighting, alpha/beta, DSC, and norm rescaling apply to vector groups in tensors with at least two dimensions, not to those scalar/1D paths.

Presets implemented by these nodes

Dense nodes offer balanced_mean, robust_medn, selective_mean, varied_mean_rn_softcb, diverse_medn_rn_dsc_softcb, and strongdiv_medn_rn_dsc_softcb.

Embedding nodes offer balanced_idx_mean, balanced_sim_mean, robust_idx_medn, robust_sim_medn, varied_sim_mean_rn_softcb, and diverse_sim_medn_rn_dsc_softcb, focused_strong_sim_medn, focused_balance_sim_medn, focused_soft_sim_medn, and focused_weak_sim_medn.

LoRA nodes offer broad_sim_medn_rn_softcb, moderate_sim_medn_rn_softcb, conservative_sim_medn_rn_softcb, direct_idx_medn_rn_softcb, broad_sim_mean_rn_softcb, neutral_sim_medn_rn, focused_sim_medn_rn_dsc_softcb, and strongfocus_sim_medn_rn_dsc_softcb, focused_strong_sim_medn, focused_balance_sim_medn, focused_soft_sim_medn, and focused_weak_sim_medn.

The LoRA default is broad_sim_medn_rn_softcb: median consensus, similarity alignment, zero similarity threshold, alpha 2, beta 4, norm rescaling, scale 1, DSC disabled, soft comfort bandpass enabled, position weight 0.05, and common-prefix preservation disabled. Broad, moderate, and conservative use alignment thresholds 0, 0.0005, and 0.0025. They represent increasing anchor preservation in automated mixed-rank LoRA tests.

Every LoRA preset fixes all 12 controls explicitly:

preset	consensus	alignment	align threshold	sim threshold	alpha	beta	RN	scale	DSC	soft CB	position	prefix	intent
broad_sim_medn_rn_softcb	median	similarity	0	0	2	4	on	1	off	on	0.05	off	Broad row matching; default
moderate_sim_medn_rn_softcb	median	similarity	0.0005	0	2	4	on	1	off	on	0.05	off	Moderate anchor preservation
conservative_sim_medn_rn_softcb	median	similarity	0.0025	0	2	4	on	1	off	on	0.05	off	Strong anchor preservation
direct_idx_medn_rn_softcb	median	index	0	0	2	4	on	1	off	on	0.05	off	Fixed rank-row correspondence
broad_sim_mean_rn_softcb	mean	similarity	0	0	2	4	on	1	off	on	0.05	off	Mean-consensus broad merge
neutral_sim_medn_rn	median	similarity	0	0	2	0	on	1	off	off	0.05	off	Similarity weighting without diversity modulation
focused_sim_medn_rn_dsc_softcb	median	similarity	0	0	2	4	on	1	on	on	0.05	off	Moderate DSC concentration
strongfocus_sim_medn_rn_dsc_softcb	median	similarity	0	0	2	7	on	1	on	on	0.05	off	Strong DSC concentration
focused_strong_sim_medn	median	similarity	0.85	0.60	1.25	0	off	1	off	off	0.20	off	Strongly focused matching
focused_balance_sim_medn	median	similarity	0.75	0.55	1.25	0	off	1	off	off	0.20	off	Balanced focused matching
focused_soft_sim_medn	median	similarity	0.55	0.50	1.25	0	off	1	off	off	0.20	off	Soft focused matching
focused_weak_sim_medn	median	similarity	0.25	0.35	1.25	0	off	1	off	off	0.20	off	Weak focused matching

The four focused median similarity presets have identical settings in the embedding and LoRA registries.

The LoRA Multi-Merge counterfactual weight sweep reuses each consensus vector already computed by the active merge. It reports mean and median consensus results across configured alpha, beta, similarity-threshold, DSC, and comfort bandpass values without additional model loads or output files.

Missing tensors and filtering

• skip preserves Model A when an anchored input is missing or incompatible. In embedding-union mode it omits that source for the affected key.
• zeros supplies a compatible zero tensor for the missing source.
• error stops the merge.
• Exclude patterns preserve the anchored source tensor.
• Discard patterns remove the matching output tensor or logical LoRA layer.

The missing-input modes above apply to Model-A-owned layers. Model A being absent from a secondary-only layer is not itself a mismatch. A shape conflict between multiple available secondary providers does use the selected mismatch mode, with the earliest available input as its preservation anchor.

Patterns use regular expressions by default. Enable Glob Patterns to use glob syntax instead.

LoRA behavior

LoRA inputs use the same parser as the resize and multi-merge nodes. It accepts all seven low-rank A/B spellings recognized by ComfyUI's LoRAAdapter, plus direct .diff, .diff_b, .w_norm, .b_norm, and .set_weight layers.

Recognized diffusion-model outputs use the canonical diffusion_model.<layer>.lora_A.weight / .lora_B.weight convention. Secondary formats are matched by normalized logical layer name. Alpha is absorbed mathematically into each B/up factor using its original rank before alignment, weighting, or merge. Genuine factor ranks remain variable during CWB. The first maximum-rank input supplies the output component space, so the output rank remains the largest genuine input rank. Merged outputs never contain or invent alpha tensors.

Every CWB merge node writes a cwb.merge safetensors metadata record. It contains selected source model names, fully resolved CWB settings, and merge options. Presets are stored as effective settings rather than by preset name. Non-empty exclude/discard filters and their glob mode are included. CWB reports and diagnostics are not saved in metadata. Similarity alignment operates on paired latent-rank components. A rows and B columns share one mapping; fixed input/output feature axes are never reordered. For a rank-R reference and rank-r source it evaluates an R-by-r similarity matrix, reduced only by a preserved common prefix. Every genuine source component can therefore match any genuine reference component. Index alignment maps source component i to reference component i. In either mode, reference components without a genuine contributor remain singleton groups and are not weighted or norm-rescaled against nonexistent rank padding.

Structural rank absence is different from mismatch_mode=zeros. The latter is an explicit request for a semantic zero contributor and continues to apply to every output-rank component where the mismatch policy is active. Numerical zero values inside a genuine factor do not identify padding; validity comes from the factor's original rank.

Global scale is applied once to the resulting pair rather than once per factor. Incomplete pairs and unrecognized tensors are copied from the earliest available source unchanged. Companion-bearing groups (lora_mid, reshape, DoRA, or set-weight) are kept atomically from their earliest available source rather than partially transformed. Secondary-only logical groups are included automatically: a group from one source is copied unchanged, while compatible groups from multiple secondary sources are merged using only those sources. Recognized roles are written with the same canonical output convention as shared layers.

include_1d_diffs is disabled by default. Disabled shared 1D direct layers preserve Model A, while secondary-only layers preserve their earliest source. Enabled 1D direct layers participate as complete tensors and are always saved in FP32.

Precision and quantized inputs

CWB computes in FP32. Unless Override Dtype is enabled, any participating FP32 source keeps the generated logical layer in FP32. Guarded low-bit tensors and enabled 1D direct diffs are never downcast by the override.

ComfyUI/comfy-kitchen quantized inputs are rejected before an output writer is opened. Files with three or more bare INT8, UINT8, FP8, or FP4 tensors are also rejected. One or two isolated low-bit tensors produce one warning per input and are preserved without CWB arithmetic; an affected LoRA causes its complete logical layer to be preserved.

CWB report output

Every CWB merge node provides a separate cwb_report string output after the existing filename and documentation outputs. Documentation-only execution returns an explicit message that no report exists because no tensors were processed.

The filename output uses ComfyUI's wildcard socket type and returns the path relative to the matching ComfyUI model category. A filename such as groupfolder/modelname therefore returns groupfolder/modelname.safetensors. It can connect directly to compatible loader Combo inputs without exposing the absolute models-directory path.

The report begins with model-wide counters. LoRA arithmetic then adds grouped mathematical evidence without retaining tensors or dumping complete similarity matrices. Layers are grouped only when they share:

• down non-rank dimensions;
• up non-rank dimensions;
• ordered genuine input ranks;
• selected reference input and rank;
• alignment method.

Each dimension group reports layer count, effective matrix dimensions, genuine source-component match coverage, reference-only components, structural rank slots excluded from arithmetic, explicit mismatch-zero contributors, candidate similarity min/mean/max, accepted-match similarity percentiles, and rank-one delta norm ratios. A compact exception section identifies the lowest match coverage and largest norm changes by layer name.

For a rank-384 reference and rank-128 source, the report identifies a 384x128 matrix, at most 128 genuine matches, and the 256 structural rank slots that were excluded rather than treated as zero-valued contributors.

Streaming and failure safety

Model parameters and direct patches use fixed-coordinate blending; similarity row matching is limited to embeddings and LoRA rank components. Each logical layer is loaded, transferred to the processing device, completed, copied back to CPU, queued for writing, and released before the next layer. Outputs are written to a temporary sibling and atomically published only after every layer and asynchronous write succeeds. Failed merges do not replace or finalize the requested output. A CUDA OOM retries only the affected layer on CPU after releasing the failed GPU working set; failure of that retry aborts atomically. Generated files are published under the matching category inside folder_paths.models_dir, never ComfyUI's general output directory.