المفاهيم الأساسية
Unified framework MM-Diff enables rapid high-fidelity image personalization for single and multiple subjects without fine-tuning.
الملخص
The content discusses the challenges in personalized image generation and introduces MM-Diff, a tuning-free framework for generating high-fidelity images of single and multiple subjects. It addresses subject fidelity, text consistency, and multi-subject generation through vision-augmented text embeddings, Subject Embedding Refiner (SE-Refiner), and cross-attention map constraints. Extensive experiments demonstrate the superior performance of MM-Diff over other methods.
Introduction
Personalized image generation aims to render subjects in various scenes.
Diffusion-based methods have advanced personalized image generation.
Single Subject Generation
Existing methods require fine-tuning or dense visual embeddings for subject fidelity.
MM-Diff proposes a unified framework for rapid high-fidelity image generation.
Multi-Subject Generation
Attribute binding issue in multi-subject generation addressed by cross-attention map constraints.
MM-Diff demonstrates superior performance in multi-subject image generation.
Method
Vision-augmented text embeddings enhance text consistency and subject fidelity.
SE-Refiner enriches subject embeddings with patch-level details.
Cross-attention map constraints guide model to generate high-quality multi-subject images.
Experiments
Evaluation metrics include CLIP-I, DINO similarity for subject fidelity, and CLIP-T for text consistency.
Comparative analysis shows MM-Diff outperforms other leading methods in both single and multi-subject generation.
Conclusion
MM-Diff offers a tuning-free solution for fast and high-fidelity personalized image generation across various domains.
الإحصائيات
"MM-Diff is capable of accomplishing both single- and multi-subject personalization across various domains in seconds."
"Extensive experiments demonstrate the superior performance of MM-Diff over other leading methods."
اقتباسات
"MM-Diff integrates vision-augmented text embeddings and detail-rich subject embeddings efficiently."
"Cross-attention map constraints ensure flexible multi-subject image sampling during inference."