Z-Image is the original base model with 6 billion parameters, focused on providing the highest image quality, strongest semantic understanding, and best fine-tuning potential - ideal for professional creation and model training. Turbo is a distilled version based on Base, sacrificing minimal details for 8-step ultra-fast generation. If you pursue ultimate quality or need to train models, choose Z-Image Base.

Traditional DiT architectures usually process text and images separately. Z-Image's S3-DiT (Single-Stream) architecture allows deep interaction between the two at every layer. This makes the model more accurate when handling complex prompts like "a cat sitting on a red chair with a blue ball beside it" - colors and objects bind more precisely.

Yes, as a foundational model, Z-Image has strong extensibility. Our platform has integrated mainstream control plugins - you can combine Pose, Canny, and other conditions for precise control.

No. Unlike early SD 1.5 that required piling up tags (like best quality, masterpiece, 8k), Z-Image benefits from its S3-DiT architecture for excellent natural language understanding. You can describe scenes conversationally (e.g., "a girl in a raincoat standing on a rainy Shanghai street, neon lights reflecting on the water"). It also supports traditional tag format, but natural language better utilizes its logical advantages.

This is a professional choice. Turbo models have undergone "distillation", which is fast but loses some high-dimensional feature space. Z-Image Base retains complete 6 billion parameter weights and feature details. As a training base, it better "absorbs" your new data, resulting in higher style fidelity and stronger generalization in trained LoRAs.

Very low dependency. Since Z-Image Base is a high-quality natively trained model, it rarely generates broken limbs or low-quality images. Usually, you can leave Negative Prompt empty. Only fill it if you have special exclusion needs (like "no red").