SD Model Merge Recipe Builder

Design model merge recipes with weighted coefficients and merge methods

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Model merge recipe builder

Merging Stable Diffusion checkpoints is a weighted average of their weights — no training required. The result inherits traits from each parent in proportion to the alpha coefficient and the merge method you choose. This builder assembles a clean, reproducible recipe and the exact Automatic1111 Checkpoint Merger settings.

How it works

Three methods take two models: weighted sum (A*(1-a) + B*a), sigmoid and inverse-sigmoid, which apply a non-linear blend curve so one model dominates more in the middle of the range. The add-difference method takes three models and computes A + (B - C) * a, transferring the difference between B and a base model C onto A. The tool validates that you supplied model C when add-difference is selected and clamps coefficients to the valid 0-1 range.

Tips for clean merges

  • Only merge same-architecture models. SD 1.5 with SD 1.5, SDXL with SDXL — never mix the two.
  • Sweep alpha. Generate the same prompt and seed at alpha 0.3, 0.5 and 0.7 to find the sweet spot rather than guessing once.
  • Use add-difference for style transfer. It is the cleanest way to move a fine-tune’s aesthetic onto a stronger base without dragging in its quirks.
  • Bake the VAE separately. Merging does not change the VAE; pick a known-good VAE at inference time for consistent colour.
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