IMAGGarment-1 is a garment generation framework that allows for high-fidelity synthesis with precise control over silhouette, color, and logo placement, addressing the limitations of existing methods by enabling multi-conditional inputs. It utilizes a two-stage training approach, incorporating both a global appearance model and a local enhancement model, and is supported by the GarmentBench dataset, which comprises over 180K garment samples with various design conditions. Extensive experiments indicate that this framework significantly outperforms current baselines in terms of structural stability and visual fidelity.