The context of new media art for photography generation based on diffusion models.

To further improve the controllability and artistic expressiveness of photography generation in new media art creation, this study proposes a multi-level conditional control generation framework based on diffusion models. The framework constructs three-level conditional fusion methods of semantics, layout and style. It integrates Contrastive Language-Image Pre-training (CLIP) text guidance, spatial structure feature mapping and style loss optimization into the Latent Diffusion Model (LDM). Meanwhile, this study introduces self-attention mechanism and consistency loss to enhance the spatial coherence of generated images. Experimental results show that the Fréchet Inception Distance (FID) value of the patterns generated by the model is 15.3, the Inception Score (IS) value is 28.7, and the CLIP Score is 0.312, which are better than those of baseline models such as Stable Diffusion 1.5. The results of ablation experiments show that the layout control and style control modules play a key role in the generation quality of the model. After removing the layout control, the FID value of the model increases to 17.2. After removing the style control, the visual realism of the generated patterns decreases. The above results indicate that the proposed framework can generate photography works with both high visual quality and high artistic matching degree. This provides a new technical path for new media art creation, and also promotes the development of generation models in semantic understanding and narrative expression.