[2509.21278] Does FLUX Already Know How to Perform Physically Plausible Image Composition?

Computer Science > Computer Vision and Pattern Recognition arXiv:2509.21278 (cs) [Submitted on 25 Sep 2025 (v1), last revised 28 Feb 2026 (this version, v4)] Title:Does FLUX Already Know How to Perform Physically Plausible Image Composition? Authors:Shilin Lu, Zhuming Lian, Zihan Zhou, Shaocong Zhang, Chen Zhao, Adams Wai-Kin Kong View a PDF of the paper titled Does FLUX Already Know How to Perform Physically Plausible Image Composition?, by Shilin Lu and 5 other authors View PDF HTML (experimental) Abstract: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 lac...