基于改进稳定扩散模型与噪声拼接的文本生成图像算法OA

To address the problems in text-to-image generation,such as missing features,low quality output,and layout attribute mismatches,this paper proposes ISD-NC,an algorithm based on Improved Stable Diffusion and NoiseCollage.First,a discriminator is introduced to maximize the mutual information between the latent representa-tion and the shallow features,thereby enhancing their similarity and preserving original image information.Second,based on the functions of the backbone network and skip connections,scale factors are incorporated to dynamically adjust the weight ratio of features to improve the generated image quality.Finally,by combining with the NoiseCol-lage network,layout conditions are incorporated,enabling the generation of images from complex multi-objective text conditions through a mask cross-attention mechanism.Qualitative and quantitative analyses,along with ablation studies,were conducted on the MS COCO dataset to compare the proposed ISD-NC against methods such as Cog-view,DF-GAN,Stable Diffusion,and KNN-diffusion.Experimental results demonstrate that ISD-NC generates ima-ges with superior detail fidelity and overall quality.Compared to diffusion-based models like Stable Diffusion and KNN-diffusion,ISD-NC reduces Frechet Inception Distance(FID)by an average of 28.99%and increases Incep-tion Score(IS)by an average of 10.21%.