Aiming at the problems that current signal separation methods usually require a known number of signal components and have poor separation performance in the case of severe aliasing such as crossover in the time-frequency domain, an intelligent signal separation method based on diffusion generation is proposed. Firstly, perform semantic segmentation on the time-frequency graph of the aliased signal to obtain each signal region corresponding to the non-overlapping parts of time and frequency, and form a signal mask. Furthermore, the time-frequency graph of the single-component signal is obtained based on the mask, and after the inverse time-frequency transformation, the single-component signal with missing parts is obtained. Finally, taking this as a condition, the improved latent diffusion model was concatenated with noise. The improved model achieved the reconstruction of each signal component by removing the training module of latent variables, improving the network parameters, and designing the loss function. The proposed method does not require the known number of signal components. Experimental results show that it can adapt to three FM signal aliasing scenarios. When there is severe overlap in the time-frequency domain and the signal-to-noise ratio is 10dB, the correlation coefficient between each separated signal component and the original signal is higher than 0.98.