LAMIC: Layout-Aware Multi-Image Composition via Scalability of Multimodal Diffusion Transformer
Source
- Person key:
alfredyao - Source kind:
paper - Canonical URL: https://arxiv.org/abs/2508.00477
- License:
NOASSERTION - Public handling:
public-metadata-summary-hash-link-only - Semantic hash:
367944518de20b4bb94f209fd11b11b51f47388f30faa4f128156a819b3e2260 - First seen: 2026-05-16
- Last changed: 2026-05-16
- Identity guard: Do not confuse with yao-shunyu-ysymyth, the OpenAI language-agents researcher at ysymyth.github.io.
Classification
- Category: Reinforcement learning and reasoning
- Topic hub: shunyu-yao-public-corpora
- Project taxonomy: shunyu-yao-project-taxonomy
- Paper map: shunyu-yao-paper-map
Summary
In controllable image synthesis, generating coherent and consistent images from multiple references with spatial layout awareness remains an open challenge. We present LAMIC, a Layout-Aware Multi-Image Composition framework that, for the first time, extends single-reference diffusion models to multi-reference scenarios in a training-free manner. Built upon the MMDiT model, LAMIC introduces two plug-and-play attention mechanisms: 1) Group Isolation Attention (GIA) to enhance entity disentanglement; and 2) Region-Mod…
What This Teaches
How learning, planning, and feedback loops shape agent behavior and model capability.
Related
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