Research on Text-to-Image Generation Based on DM Model

In recent years, the rapid development of deep learning technology has facilitated the integration of the text-to-image generation task in the fields of natural language processing and computer vision. However, existing research mainly focuses on portrait generation, and there is insufficient exploration of the generation effects of complex natural scenes and artistic styles (such as Chinese landscape paintings). Therefore, this paper proposes a text-to-image generation framework that integrates an improved Diffusion Model (DM) and a Latent Diffusion Model (LDM). By integrating the multi-modal feature alignment ability of the CLIP model and combining two types of datasets, namely Chinese-Landscape-Painting and Natural-Landscape, a data preprocessing method based on central cropping and Deepdanbooru automatic annotation is proposed, and the model parameters (3000 training epochs, a learning rate of 2e-6, and 250 sampling steps) are systematically optimized. Experiments show that the model significantly reduces the FID value when generating detailed scene images (FID = 23.44 for the natural landscape dataset and FID = 53.35 for the Chinese landscape painting dataset), and the visual Turing test verifies the visual similarity between the generated images and real images (80% of the testers could hardly distinguish between the generated images and human works). This research provides an effective technical solution for artistic style image generation and large-scale natural scene modeling, and has potential application values in fields such as digital cultural heritage protection and film and television rendering.