SDXL Fusion Model and Multi-Configuration LoRA for Ship Design
Project Overview
This project addresses the "semantic gap" issue when applying the base SDXL model directly to ship design scenarios, catering to the AI-generated needs of the ship design field. It provides two core assets:
A ship design-specific SDXL model fused via SLERP (combining material optimization and product design capabilities)
Five sets of ship rendering LoRA weights with different rank configurations (adapting to diverse precision and efficiency requirements)
All models have undergone validation in ship design scenarios and can be directly used for industrial design tasks such as ship structure generation, surface material rendering, and detail optimization, balancing generation quality with training/inference efficiency.
1. Core Technical Solutions
1.1 Model Adaptation Strategy for Industrial Design (Ship Design Direction)
1.1.1 Problem Background
The base SDXL model is not optimized for the "professional semantics" of ship design (e.g., hull structure, ship materials, industrial design specifications). Direct application leads to "feature mismatch" (such as shape distortion and material deviation), known as the semantic gap.
1.1.2 Fusion Solution
Domain adaptation is achieved through model fusion, as detailed below:
Fusion Objects: Two specifically pre-trained SDXL models
Material-Optimized SDXL: Focuses on the precision of ship surface material (e.g., metal, coating) and texture generation
Product-Design SDXL: Focuses on the rationality of overall ship structure (e.g., hull contour, cabin layout) and shape design
Let the parameters of the two models be $\theta_1$ (material-optimized) and $\theta_2$ (product-design), respectively.
Fusion Algorithm: Spherical Linear Interpolation (SLERP)
$\Omega$: The angle in the parameter space of the two models (measuring the degree of parameter difference)
$\lambda$: Fusion coefficient (adjusts the contribution ratio of features from the two models; default $\lambda=0.5$ in this project)
Stability Assurance: Perform Singular Value Decomposition (SVD) on the core weight matrices $W_1$ (material-optimized) and $W_2$ (product-design) of the two models. Eigenvalue constraints prevent "weight collapse/explosion" while maintaining the orthogonality of feature representation.
1.2 LoRA Low-Rank Decomposition Technology and Experimental Design
LoRA (Low-Rank Adaptation) is an efficient fine-tuning technology for large models. It reduces the number of parameters by injecting low-rank matrices, adapting to the precise fine-tuning needs of ship design scenarios.
1.2.1 LoRA Principle and Advantages
For the pre-trained weight matrix $W_0 \in \mathbb{R}^{d \times k}$ of the SDXL model ($d$ = input dimension, $k$ = output dimension):
Traditional Full-Parameter Fine-Tuning: Requires updating all $d \times k$ parameters, resulting in high costs and low efficiency.
LoRA Optimization Scheme: Converts weight updates into the product of low-rank matrices
Formula: $W_0 + \Delta W = W_0 + A \cdot B^T$
Where:
$W_0$: Frozen pre-trained weights (retains the basic capabilities of the SDXL model)
$A$: Down-projection matrix ($\mathbb{R}^{d \times r}$), initialized to a Gaussian distribution $\mathcal{N}(0, \sigma^2)$
$B$: Up-projection matrix ($\mathbb{R}^{k \times r}$), initialized to a zero matrix (minimizes initial impact)
$r$: LoRA rank parameter (much smaller than $d/k$, reducing the number of trainable parameters by 1-2 orders of magnitude)
1.2.2 Key Parameter Configuration
To ensure training stability, a strategy of fixed ratio between Network_dim (rank ) and Network_alpha (scaling factor ) is adopted:
Core Constraint: $\frac{\alpha}{r}$ remains constant (default ratio = 1.0 in this project)
Advantage: Avoids parameter scale differences under different rank configurations, eliminating the need to readjust the learning rate.
1.2.3 Controlled Experimental Design
To explore the impact of $r$ on ship rendering quality, five sets of LoRA configurations (all matching fixed $\alpha$) are designed:
Configuration ID
LoRA Rank ($r$)
Application Scenario
Generation Precision
Inference Efficiency
LoRA-r8
8
Quick preview, low-resource devices
Medium
Highest
LoRA-r16
16
Routine design iterations
Good
High
LoRA-r32
32
Core design scheme generation
Excellent
Medium
LoRA-r64
64
High-precision detail optimization
Extremely High
Low
LoRA-r128
128
Final rendering, professional demonstration
Top-tier
Lowest
ComfyUI_00016_
1.2.4 Weight Update Modulation
The actual weight update of LoRA is modulated by $\alpha$ to ensure consistent initialization scale:
Formula: $\Delta W = \frac{\alpha}{r} \cdot A \cdot B^T$
