Does FLUX Already Know How to Perform Physically Plausible Image
  Composition?
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.
68d5f6788ccd91bdd39ffdb3
Shilin Lu
claimed_verified
2025-09-26T12:19:30.705Z
631c4a23aa346997917bcb89
/avatars/4a562ba78e6be289049027c27798cc6e.svg
Shilin Lu
user
Shilin-LU
68d5f6788ccd91bdd39ffdb4
Zhuming Lian
68d5f6788ccd91bdd39ffdb5
Zihan Zhou
claimed_verified
2025-09-26T12:16:56.963Z
66923522e32997bdf7f37462
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/-RE4FqyZnDF9PcpY6ZBGH.png
Zihan Zhou
user
Edennnnn
68d5f6788ccd91bdd39ffdb6
Shaocong Zhang
68d5f6788ccd91bdd39ffdb7
Chen Zhao
68d5f6788ccd91bdd39ffdb8
Adams Wai-Kin Kong
Does FLUX Already Know How to Perform Physically Plausible Image
  Composition?
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.
SHINE is a training-free framework that uses manifold-steered anchor loss and pretrained customization adapters to seamlessly insert objects into new scenes with high fidelity, addressing challenges like complex lighting and diverse inputs.
text-to-image diffusion models SD3.5 FLUX manifold-steered anchor loss pretrained customization adapters IP-Adapter degradation-suppression guidance adaptive background blending ComplexCompo DreamEditBench DINOv2 DreamSim ImageReward VisionReward