Disentangling Dual Image References in Frequency Aware Diffusion Models for Personalized Generation

arXiv:2610.07684 · cs.CV, cs.AI · Submitted 2026-10-06 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-06

Updated: 2026-10-06

Comments: 28 pages, 14 figures, to appear at NeurIPS 2026, Sydney, Australia

Code: https://github.com/htyjers/Dual-FDM

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Diffusion Model — 1× in this paper · explained in 10 episodes
• Diffusion Models — 1× in this paper · explained in 9 episodes

Sources

• An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
• StyleShot: A Snapshot on Any Style
• StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
• StyleDrop: Text-to-Image Generation in Any Style
• InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation
• InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation
• Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
• CSGO: Content-Style Composition in Text-to-Image Generation
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

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