MV-Adapter is an adapter-based solution for multi-view image generation that enhances pre-trained text-to-image (T2I) models and their derivatives without altering the original network architecture or feature space. By updating fewer parameters, MV-Adapter achieves efficient training while retaining the embedded prior knowledge in the pre-trained models, thus reducing the risk of overfitting. This technology utilizes innovative designs, such as replicated self-attention layers and parallel attention architectures, allowing the adapter to inherit the powerful prior knowledge of pre-trained models for modeling new 3D knowledge. Moreover, MV-Adapter offers a unified conditional encoder that seamlessly integrates camera parameters and geometric information, supporting applications such as 3D generation based on text and images as well as texture mapping. MV-Adapter has demonstrated multi-view generation at a resolution of 768 on Stable Diffusion XL (SDXL), showcasing its adaptability and versatility for expansion into arbitrary view generation, unlocking broader application possibilities.