Block-wise model merging is a technique combining different neural network layers from multiple models — selecting the best-performing blocks from each model to create a superior merged model.

What Is Block-wise Merging?

• Definition: Merge models at the block/layer level, not whole weights.
• Method: Choose which blocks come from which source model.
• Granularity: Transformer blocks, ResNet stages, attention layers.
• Benefit: Combine specialized capabilities from different models.
• Contrast: Weight averaging merges all parameters uniformly.

Why Block-wise Merging Matters

• Selective: Take best parts from each model.
• Capabilities: Combine different strengths (style, anatomy, etc.).
• Control: Fine-grained customization of merged result.
• Community: Popular in Stable Diffusion model mixing.
• No Training: Create new models without additional training.

Common Block Types

Stable Diffusion:

• IN blocks: Input processing, encoding.
• MID block: Core processing.
• OUT blocks: Output, decoding, final layers.

Merging Strategy

1. Analyze: Understand what each block contributes. 2. Experiment: Try different source assignments. 3. Evaluate: Test merged model outputs. 4. Iterate: Refine block selections.

Block-wise merging enables surgical model combination — pick the best layers from multiple models.