Digital media image editing algorithm based on image generation and manual interaction

In the rapidly developing digital media era of artificial intelligence, image editing technology has become the core support for digital content creation and information transmission. However, existing image editing technologies still face key challenges such as unstable generation quality, low interaction efficiency, and insufficient cross domain adaptability. To address these issues, a digital media image editing algorithm based on image generation and manual interaction is proposed, aiming to achieve the unity of high-quality image generation and precise control by integrating hybrid generation architecture and dynamic attention mechanism. Experiments show that the research algorithm achieves a Fréchet Distance (FID) value of 21.3 in terms of generation quality, which is 44.8 % and 53.9 % higher than the control group’s values of 38.8 and 46.2, respectively. In the cross domain editing task, when the intersection to union ratio index reaches 89 %, the Dynamic Similarity Adjacency Matrix (DSAM) value of the research algorithm is 0.83, which is 21.4 % and 18.7 % higher than the control group method, respectively. In terms of system stability, the research algorithm has a misidentification rate of only 6.0 % and a crash frequency of 1 after 5 h of continuous operation, which is much better than the control group’s 34.5 % and 5 crashes. This proves that the research algorithm can effectively balance generation quality and computational efficiency, improve user interaction experience while maintaining structural consistency, and provide technical reference for professional level image editing.