Enhanced Diffusion Model Image Generation Capabilities for Complex Scenes
Abstract
In order to solve the problems of detail loss and confusing multi-object interaction logic in the generation of complex scene images by diffusion models, this study constructs a multi-scale latent space architecture and uses VQ-VAE-2 (Vector Quantized-Variational AutoEncoder version 2) hierarchical encoder to decompose the input image into discrete latent codes of four scales from$64 \times 64$to$512 \times 512$. The contrastive attention module (CAM) is designed, and the CLIP (Contrastive Language-Image Pre-training) image-text alignment loss is embedded in the skip connection layer of U-Net. The contrast similarity between the generated features and the text prompts is calculated as the attention weight to guide the model to focus on key semantic areas. The Dynamic Gradient Modulator (DGM) is further introduced to adjust the gradient weight of the diffusion step. A two-stage strategy is adopted in the training phase: the first stage pre-trains the basic diffusion model, and the second stage performs adversarial fine-tuning to optimize the fidelity of details. The quantitative evaluation results show that the FID (Fréchet Inception Distance) value of the enhanced model output image is reduced, and the enhanced IS (Inception Score) value has a minimum value of 5.1 and a maximum value of 9.8, indicating that the authenticity and diversity of the generated images have been improved. The enhanced diffusion model proposed in this paper has achieved remarkable results in complex…
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