# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 4 Experimental Results and Analysis
### Experimental Setup  
The VQ-Diffusion mode, based on the diffusion principle and the Transformer network architecture, I has hundreds of millions of parameters, and requiring a large dataset for training to effectively learn image features. However, remote sensing images are difficult to acquire and limited in quantity, which cannot fully support the model's learning of image features. The Conceptual Captions dataset [32] consists of a large number of natural images and their descriptive texts, covering a wide range of scenes and object types with high visual diversity. Although this dataset is derived from natural images, the visual features it contains, such as object shapes, lighting, and perspective variations, have certain similarities to the common land-cover types and scenes found in remote sensing images. Therefore, pre-training with the Conceptual Captions dataset can help the model learn general visual features and provide effective initialization for training with remote sensing images. Especially in situations where remote sensing data are scarce, cross-domain knowledge transfer can significantly improve model performance and accelerate the training process. Hence, this study uses the pre-trained VQ-Diffusion model trained on the Conceptual Captions dataset to initialize the training of the VQ-Diffusion model on remote sensing datasets. In this experiment, efficient hardware and software configurations were employed for the training process. The hardware platform included an Intel Xeon Platinum 8352S CPU, 512 GB of memory, and an RTX 4090 GPU to ensure efficient computation while training large models (Santa Clara, CA, USA). The operating system used was Windows 11, and the deep was PyTorch1.12.0. To ensure the stability and efficiency of the training process, the AdamW optimizer was selected. This optimizer effectively prevents overfitting by combining L2 regularization and weight decay. Additionally, the Reduce LR On Plateau With Warmup learning rate scheduler was used, which automatically adjusts the learning rate based on the performance on the validation set, ensuring stable convergence during different training stages. A weight decay value of 4.5 \(\times\) 10\({}^{-2}\) was chosen to control overfitting and help the model achieve better generalization during training. For the training parameters, after multiple adjustments to determine the optimal settings, a batch size of 4 was chosen to fully utilize the GPU memory, and the number of epochs was set to 100 to ensure the model could learn effective image features over sufficient training cycles. The initial learning rate was set to 0.3 \(\times\) 10\({}^{-6}\) with a minimum learning rate of 1.0 \(\times\) 10\({}^{-6}\) to ensure rapid learning in the early stages and fine-tuning in the later stages with a smaller learning rate. Details regarding the model training environment and parameter settings are provided in Tables 1 and 2. The VQ-Diffusion model, which combines diffusion principles and the Transformer architecture, has significant computational complexity. The diffusion model generates images through multiple iterative processes, with each iteration (denoising process) requiring complex calculations using the Transformer's self-attention mechanism. Consequently, the training process demands substantial computational resources and longer training times. To accelerate training and reduce memory consumption, automatic mixed precision (AMP) training was employed during the model's training on the RSICD, taking approximately 52 h on a single RTX 4090 GPU. Despite the introduction of a new spatial positional \begin{table}
\begin{tabular}{c c c} \hline \hline
**Platform** & **Configuration Item** & **Configuration Value** \\ \hline \multirow{4}{*}{Hardware Platform} & CPU & Intel Xeon(R)Platinum 8352S CPU @ 2.2 GHz \\  & Memory & 512 GB \\  & GPU & RTX4090 \\  & Graphics Memory & 24 G \\  & Operating System & Windows11 \\ Software Platform & Deep Learning Framework & Pytorch \\ \hline \hline \end{tabular}
\end{table}