用潜在扩散模型提升图像传输质量，降低计算开销。

Diffusion-based semantic communication methods have shown significant advantages in image transmission by harnessing the generative power of diffusion models. However, they still face challenges, including generation randomness that leads to distorted reconstructions and high computational costs. To address these issues, we propose CASC, a condition-aware semantic communication framework that incorporates a latent diffusion model (LDM)-based denoiser. The LDM denoiser at the receiver utilizes the received noisy latent codes as the conditioning signal to reconstruct the latent codes, enabling the decoder to accurately recover the source image. By operating in the latent space, the LDM reduces computational complexity compared to traditional diffusion models (DMs). Additionally, we introduce a condition-aware neural network (CAN) that dynamically adjusts the weights in the hidden layers of the LDM based on the conditioning signal. This enables finer control over the generation process, significantly improving the perceptual quality of the reconstructed images. Experimental results show that CASC significantly outperforms DeepJSCC in both perceptual quality and visual effect. Moreover, CASC reduces inference time by 51.7% compared to existing DM-based semantic communication systems, while maintaining comparable perceptual performance. The ablation studies also validate the effectiveness of the CAN module in improving the image reconstruction quality.