# 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. 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 `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. Every named merger preset keeps `global_scale` at `1.0`; scaling persisted model or LoRA weights is only performed when explicitly selected in the custom controls. 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. 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..lora_A.weight` / `.lora_B.weight` convention. 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 anchor-source alpha key is updated to the resulting maximum rank; no alpha key is invented when the anchor has none. Similarity alignment operates on paired latent-rank components. A rows and B columns share one mapping; fixed input/output feature axes are never reordered. Input alpha scaling is absorbed into the B factor before blending, and 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. ## 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.