Image composition aims to seamlessly insert a user-specified object into a
new scene, but existing models struggle with complex lighting (e.g., accurate
shadows, water reflections) and diverse, high-resolution inputs. Modern
text-to-image diffusion models (e.g., SD3.5, FLUX) already encode essential
physical and resolution priors, yet lack a framework to unleash them without
resorting to latent inversion, which often locks object poses into contextually
inappropriate orientations, or brittle attention surgery. We propose SHINE, a
training-free framework for Seamless, High-fidelity Insertion with Neutralized
Errors. SHINE introduces manifold-steered anchor loss, leveraging pretrained
customization adapters (e.g., IP-Adapter) to guide latents for faithful subject
representation while preserving background integrity. Degradation-suppression
guidance and adaptive background blending are proposed to further eliminate
low-quality outputs and visible seams. To address the lack of rigorous
benchmarks, we introduce ComplexCompo, featuring diverse resolutions and
challenging conditions such as low lighting, strong illumination, intricate
shadows, and reflective surfaces. Experiments on ComplexCompo and
DreamEditBench show state-of-the-art performance on standard metrics (e.g.,
DINOv2) and human-aligned scores (e.g., DreamSim, ImageReward, VisionReward).
Code and benchmark will be publicly available upon publication.