ZhumingLian

ZhumingLian / SHINE

Public

[ICLR 2026] "Does FLUX Already Know How to Perform Physically Plausible Image Composition?" (Official Implementation)

diffusion-models flux image-composition image-editing
60
0
100% credibility
Found Feb 27, 2026 at 45 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

SHINE is a training-free tool for realistically compositing specific objects into scene images, handling complex physics like shadows and reflections using FLUX models.

How It Works

1
🖼️ Pick your scene and object

Choose a background photo like a beach and an object photo like a cat you want to add.

2
📍 Mark the spot

Draw a simple box on the scene photo showing exactly where the object should appear.

3
🎨 Fill the empty space

The tool smartly fills the box with scene-matching details so it looks natural.

4
🐱 Add your object

Provide the object photo and a short description of what it is.

5
✨ Magic blending happens

Watch the object drop in perfectly with realistic shadows, lights, and reflections.

🌟 Perfect photo ready

Download your seamless, lifelike composite image to share or print.

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AI-Generated Review

What is SHINE?

SHINE is a Python framework for physically plausible image composition using the FLUX diffusion model. It lets you drop a reference object (with mask and bbox) into any background scene, automatically generating realistic shadows, reflections, and lighting matches—without retraining or latent inversion hacks. Grab pretrained checkpoints from Hugging Face, run inference scripts on single images or benchmarks like ComplexCompo, and evaluate with metrics like DINOv2 or ImageReward; perfect for devs eyeing that GitHub ICLR 2026 openreview buzz.

Why is it gaining traction?

Unlike brittle attention hacks in other diffusion editors, SHINE unlocks FLUX's built-in physics priors training-free, delivering SOTA results on high-res inputs with tricky lighting via adapter or LoRA modes. Devs dig the seamless pipeline: JSON inputs for bg/fg/bbox yield seam-free outputs, plus ready HF datasets for benchmarking. Amid GitHub ICLR 2026 leak chatter and reviewer stats, it's a quick win for plausible edits without full fine-tuning.

Who should use this?

Computer vision researchers testing diffusion priors for ICLR 2026 workshops or rebuttals. AR/VR devs needing instant object insertion with real shadows. Prototype builders swapping SD into FLUX for better composition in apps like photo editors.

Verdict

Try it if you're prototyping FLUX edits—docs and scripts are solid for quick starts, benchmarks prove the physics. At 43 stars and 1.0% credibility, it's early (pre-ICLR 2026 publication), so expect tweaks, but the training-free hook makes it worth forking now.

(198 words)

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