Multi-Instance Generation has advanced significantly in spatial placement and attribute binding. However, existing approaches still face challenges in fine-grained semantic understanding, particularly when dealing with complex textual descriptions.To overcome these limitations, we propose $\textbf{DEIG}$, a novel framework for fine-grained and controllable multi-instance generation. DEIG integrates an $\textit{Instance Detail Extractor}$ (IDE) that transforms text encoder embeddings into compact, instance-aware representations, and a $\textit{Detail Fusion Module}$ (DFM) that applies instance-based masked attention to prevent attribute leakage across instances. These components enable DEIG to generate visually coherent multi-instance scenes that precisely match rich, localized textual descriptions. To support fine-grained supervision, we construct a high-quality dataset with detailed, compositional instance captions generated by VLMs. We also introduce $\textbf{DEIG-Bench}$, a new benchmark with region-level annotations and multi-attribute prompts for both humans and objects.Experiments demonstrate that DEIG consistently outperforms existing approaches across multiple benchmarks in spatial consistency, semantic accuracy, and compositional generalization. Moreover, DEIG functions as a plug-and-play module, making it easily integrable into standard diffusion-based pipelines.