This paper introduces a novel method for portrait image animation using end-to-end diffusion models, addressing challenges in audio-driven facial dynamics synchronization and high-quality animation generation with temporal consistency. The proposed hierarchical audio-driven visual synthesis module enhances audio-visual alignment through cross-attention mechanisms and adaptive weighting. By integrating diffusion-based generative modeling, UNet denoising, temporal alignment, and ReferenceNet, the method improves animation quality and realism. Experimental evaluations demonstrate superior image and video quality, enhanced lip synchronization, and increased motion diversity, validated by superior FID and FVD metrics. The method allows flexible control over expression and pose diversity to accommodate diverse visual identities. The proposed approach is evaluated on the HDTF and CelebV datasets, showing significant improvements in image and video quality, lip synchronization precision, and motion diversity compared to existing methods. The method also demonstrates strong performance on a proposed "wild" dataset, achieving the lowest FID and FVD scores and the highest Sync-C score. The hierarchical audio-driven visual synthesis module is shown to be effective in achieving fine-grained and precise alignment between audio, lip motion, and facial expressions. The method is also evaluated through ablation studies, showing that the hierarchical audio-visual cross attention significantly improves the quality and coherence of audio-visual synthesis. The method is also evaluated for efficiency, showing that the hierarchical audio-driven visual synthesis requires 9.77 GB of GPU memory and takes 1.63 seconds for inference. Variations in video resolution significantly affect both GPU memory usage and inference time. The method is also evaluated for social risks, highlighting the potential ethical implications of creating highly realistic and dynamic portraits that could be misused for deceptive or malicious purposes. To mitigate these risks, the paper suggests establishing ethical guidelines and responsible use practices for the technology. The proposed method is shown to be effective in generating high-quality, temporally consistent animations with precise lip synchronization and diverse motion. The method is also shown to be flexible in controlling expression and pose diversity to accommodate different visual identities. The method is also shown to be efficient in terms of GPU memory usage and inference time, making it suitable for real-time applications. The method is also shown to be robust in handling various audio and image inputs, generating high-fidelity and visually coherent videos that align seamlessly with the audio content. The method is also shown to be effective in generating animations with enhanced diversity in expression and pose motion. The method is also shown to be effective in generating animations that closely resemble the targeted identity following personalized data fine-tuning. The method is also shown to be effective in generating animations with enhanced visual fidelity and motion diversity. The method is also shown to be effective in generating animations with enhanced temporal coherence and precise lip synchronization. The method is also shown to be effective in generating animations with enhanced visual quality and motion diversity. The method is also shown to be effective in generating animations with enhanced visual fidelity and motion diversity. The method is also shown to be effective in generating animations withThis paper introduces a novel method for portrait image animation using end-to-end diffusion models, addressing challenges in audio-driven facial dynamics synchronization and high-quality animation generation with temporal consistency. The proposed hierarchical audio-driven visual synthesis module enhances audio-visual alignment through cross-attention mechanisms and adaptive weighting. By integrating diffusion-based generative modeling, UNet denoising, temporal alignment, and ReferenceNet, the method improves animation quality and realism. Experimental evaluations demonstrate superior image and video quality, enhanced lip synchronization, and increased motion diversity, validated by superior FID and FVD metrics. The method allows flexible control over expression and pose diversity to accommodate diverse visual identities. The proposed approach is evaluated on the HDTF and CelebV datasets, showing significant improvements in image and video quality, lip synchronization precision, and motion diversity compared to existing methods. The method also demonstrates strong performance on a proposed "wild" dataset, achieving the lowest FID and FVD scores and the highest Sync-C score. The hierarchical audio-driven visual synthesis module is shown to be effective in achieving fine-grained and precise alignment between audio, lip motion, and facial expressions. The method is also evaluated through ablation studies, showing that the hierarchical audio-visual cross attention significantly improves the quality and coherence of audio-visual synthesis. The method is also evaluated for efficiency, showing that the hierarchical audio-driven visual synthesis requires 9.77 GB of GPU memory and takes 1.63 seconds for inference. Variations in video resolution significantly affect both GPU memory usage and inference time. The method is also evaluated for social risks, highlighting the potential ethical implications of creating highly realistic and dynamic portraits that could be misused for deceptive or malicious purposes. To mitigate these risks, the paper suggests establishing ethical guidelines and responsible use practices for the technology. The proposed method is shown to be effective in generating high-quality, temporally consistent animations with precise lip synchronization and diverse motion. The method is also shown to be flexible in controlling expression and pose diversity to accommodate different visual identities. The method is also shown to be efficient in terms of GPU memory usage and inference time, making it suitable for real-time applications. The method is also shown to be robust in handling various audio and image inputs, generating high-fidelity and visually coherent videos that align seamlessly with the audio content. The method is also shown to be effective in generating animations with enhanced diversity in expression and pose motion. The method is also shown to be effective in generating animations that closely resemble the targeted identity following personalized data fine-tuning. The method is also shown to be effective in generating animations with enhanced visual fidelity and motion diversity. The method is also shown to be effective in generating animations with enhanced temporal coherence and precise lip synchronization. The method is also shown to be effective in generating animations with enhanced visual quality and motion diversity. The method is also shown to be effective in generating animations with enhanced visual fidelity and motion diversity. The method is also shown to be effective in generating animations with