RodinHD is a novel method for generating high-fidelity 3D avatars from single portrait images. It addresses the challenge of capturing intricate details such as hairstyles by introducing a task-replay strategy and an identity-aware weight consolidation regularizer to improve the decoder's capability of rendering sharp details. The method also optimizes the guiding effect of the portrait image by computing a finer-grained hierarchical representation and injecting it into the 3D diffusion model at multiple layers via cross-attention. The model is trained on 46K avatars with an optimized noise schedule for triplanes, resulting in 3D avatars with better details and improved cross-view consistency compared to previous methods. The proposed techniques are general and can be applied to other 3D generation tasks.RodinHD is a novel method for generating high-fidelity 3D avatars from single portrait images. It addresses the challenge of capturing intricate details such as hairstyles by introducing a task-replay strategy and an identity-aware weight consolidation regularizer to improve the decoder's capability of rendering sharp details. The method also optimizes the guiding effect of the portrait image by computing a finer-grained hierarchical representation and injecting it into the 3D diffusion model at multiple layers via cross-attention. The model is trained on 46K avatars with an optimized noise schedule for triplanes, resulting in 3D avatars with better details and improved cross-view consistency compared to previous methods. The proposed techniques are general and can be applied to other 3D generation tasks.