## 3 Methods and Materials
### Development of the CICH Paper-Cutting LoRA Model
#### 3.2.3 Model Training  
In this study, we used anything-v5-PrtRE_safetensors and FLUX.1-dev based on Stable Diffusion 1.5 as the base models and trained two sets of LoRA models to generate images in different styles. For generating Chinese paper-cutting art patterns, flux1-dev.safetensors was used as the pre-trained model, and training was conducted in flux_lora mode on an NVIDIA 4090 GPU for 48 epochs, resulting in several output models. The dataset included four categories: \"Spring Festival\" (25 images), \"Flowers and Birds\" (24 images), \"Zodiac Animals\" (12 images), and \"Women\" (13 images). Simultaneously, we trained a LoRA model based on anything-v5-PrtRE_safetensors using the same dataset. Both sets of mod \begin{table}
\begin{tabular}{c c} \hline \hline
**Spring Festival**[46] & **Image Labels** \\ \hline \multirow{4}{*}{**Chinese Zodiac**[47]} & No people, red, round image, monochrome, Chinese traditional pattern, white background, Chinese paper-cut, fish, wavy pattern, flower, text in the middle of the pattern is the word \"\#if\", good text meaning, visual design, graphic design. \\ \hline \multirow{2}{*}{**Chinese Zodiac**[47]} & **Image Labels** \\ \hline \multirow{4}{*}{**Chinese Zodiac**[47]} & Red Chinese paper-cut design featuring a dragon at the center of a circular pattern, surrounded by floral and leaf motifs. No people, red, circle, monochrome, white background, Chinese paper-cut, Chinese traditional, paper-cut pattern, Chinese paper-cut art, visual design, graphic design. \\ \hline \multirow{2}{*}{**Women**[48]} & **Image Labels** \\ \hline \multirow{4}{*}{**Women**[48]} & Red Chinese paper-cut design featuring a traditional female figure in elegant attire, standing next to a small table with flowers and a bird. Silhouette, monochrome, Chinese traditional pattern, white background, Chinese paper-cut art, visual design, graphic design. \\ \hline \multirow{2}{*}{**Flowers and Birds**[49]} & **Image Labels** \\ \hline \multirow{2}{*}{**Women**[48]} & No humans, Chinese red paper-cut design featuring two birds surrounded by flowers and leaves within a circular pattern. Monochrome, white background, traditional Chinese art, Chinese paper-cutting art, paper-cut pattern, graphic design. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training set image prompt labels. els were trained using appropriate learning rate schedulers and optimizers to ensure a balance between accuracy and efficiency. The details of the model training parameters are provided in Table 4.  
On the four datasets of \"Spring Festival\" paper-cutting, \"Zodiac Animals\" paper-cutting, \"Flowers and Birds\" paper-cutting, and \"Women\" paper-cutting, both the FLUX.1-dev model, as shown in Figure 3, and the Stable Diffusion 1.5 model, as shown in Figure 4, exhibited similar training trends. The details are provided in Figure 3. The training performance of both models was analyzed based on several key metrics, including the loss rate decline trend, convergence speed, stability, and generalization ability, detailed as follows:  
1. Loss Rate Decline Trend  
In all datasets, both FLUX.1-dev and Stable Diffusion 1.5 models demonstrated a rapid decline in loss rate during the early stages of training, indicating that both models were able to quickly learn effective features from the data. 2. Convergence Speed  
FLUX.1-dev model: Across all datasets, the FLUX.1-dev model had a faster convergence speed, especially in the early stages of training, where the loss rate decreased rapidly and reached a stable convergence state earlier. Stable Diffusion 1.5 model: This model showed a relatively slower convergence speed across all datasets. The decline in the loss rate slowed, particularly in the mid-to-late stages of training, and exhibited slight fluctuations.