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.

Node families

Two-input and three-input variants are provided for checkpoints, standalone diffusion models, text encoders, LoRAs, and embeddings.

Checkpoint, diffusion-model, text-encoder, and LoRA outputs are anchored to Model A. Model A supplies the output key set, metadata, and naming. Keys found only in later inputs are ignored. Embedding mergers instead use the union of all keys and use the longest compatible first dimension as the alignment reference.

Presets and custom controls

baseline is the default. The standard clarity, smooth, varied, and diversity presets are joined by power_blend and the DSC presets demonstrated by the CWB conditioning implementation. Select custom to make every manual CWB control authoritative.

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.

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.

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. This includes the repository's plain .lora_A.weight / .lora_B.weight extraction output, already-supported alternative A/B or down/up suffixes, and direct .diff / .diff_b layers.

Model A's exact keys and suffix convention are retained. Secondary formats are matched by normalized logical layer name. Differing ranks are zero-padded like the DARE/TIES mergers: A/down tensors on dimension 0 and B/up tensors on dimension 1. An existing Model A alpha key is updated to the resulting maximum rank; no alpha key is invented when Model A has none. Incomplete pairs, auxiliary tensors, and unrecognized Model A tensors are copied unchanged.

include_1d_diffs is disabled by default. Disabled 1D direct layers preserve Model A. 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.