block-wise merging

**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.