# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 3 Methods
### RSVQ-Diffusion Model  
#### 3.1.1 VQ-Diffusion Model Network Architecture  
The VQ-Diffusion model primarily comprises three components: the text encoder CLIP [26], the image encoder VQVAE, and the diffusion image decoder. During the data preprocessing stage, the input text is first processed using byte pair encoding (BPE), which splits the text into smaller units (such as subwords or character-level representations), to enhance the model's processing capabilities. Next, the text is encodedusing the Contrastive Language-Image Pre-training (CLIP) model. The text is tokenized and converted into word embeddings and then processed through multiple Transformer layers to generate fixed-dimensional semantic vectors. The VQVAE image encoder is used to encode image data into a discrete latent space for data compression. Convolutional neural networks extract image features and map them to a predefined set of discrete vectors, completing the quantization step. This method effectively reduces the dimensionality of the information and, during the decoding phase, reconstructs the discrete representations into the original images using deconvolutional networks, ensuring image quality. Diffusion models are trained and inferred in the latent space, with the generated images decoded and output through VQVAE. During the training phase, noise is gradually added to real images, forming progressively blurred image sequences. In the inference phase, after inputting text descriptions, the model generates images starting from random noise; it then uses text embeddings as conditional inputs to guide the denoising process, resulting in high-quality images. The self-attention mechanism of the Transformer dynamically captures relevant information throughout this process, enhancing the quality and consistency of the generated images. As illustrated in Figure 1, the architecture of the VQ-Diffusion model comprises a reverse denoising process where each step is predicted by the diffusion image decoder for the next time step. The diffusion decoder is built from multiple stacked TransformerBlocks, each of which includes the following five processes: AdaLayerNorm: this layer adaptively normalizes each batch of input data independently to enhance the model's adaptability; FullAttention: this mechanism computes the correlation between each element in the input sequence and other elements, emphasizing important information through attention weights; CrossAttention: similar to FullAttention, this mechanism helps the model better understand and map textual descriptions to the corresponding image features; and Multi-layer Perceptron (MLP): this component processes and transforms feature data, gradually bringing it closer to the characteristics of the target image. By employing these network layers, the TransformerBlock performs multi-layer processing and feature transformation, achieving the complex generative process from text to image. VQ-Diffusion model is primarily designed for general image generation. However, there are significant differences between general images and remote sensing images. Firstly, spatial characteristics are one of the main distinctions. General images are typically cap  
Figure 1: Architecture of the VQ-Diffusion model. tured by digital cameras or smartphone cameras, featuring high resolution and rich image details, making them suitable for visual representation in daily life. In contrast, remote sensing images are primarily obtained from remote sensing platforms such as satellites, drones, or aircraft, usually with lower spatial resolution but capable of covering large surface areas. These images often contain multispectral information (e.g., infrared, visible light, and radar bands), providing unique reflectance characteristics of surface features under different spectral bands [27]. This results in complex structures and textures across multiple scales, spectra, and temporal states in remote sensing images.