title: Imagine yourself: Tuning-Free Personalized Image Generation
summary: Diffusion models have demonstrated remarkable efficacy across various
  image-to-image tasks. In this research, we introduce Imagine yourself, a
  state-of-the-art model designed for personalized image generation. Unlike
  conventional tuning-based personalization techniques, Imagine yourself operates
  as a tuning-free model, enabling all users to leverage a shared framework
  without individualized adjustments. Moreover, previous work met challenges
  balancing identity preservation, following complex prompts and preserving good
  visual quality, resulting in models having strong copy-paste effect of the
  reference images. Thus, they can hardly generate images following prompts that
  require significant changes to the reference image, \eg, changing facial
  expression, head and body poses, and the diversity of the generated images is
  low. To address these limitations, our proposed method introduces 1) a new
  synthetic paired data generation mechanism to encourage image diversity, 2) a
  fully parallel attention architecture with three text encoders and a fully
  trainable vision encoder to improve the text faithfulness, and 3) a novel
  coarse-to-fine multi-stage finetuning methodology that gradually pushes the
  boundary of visual quality. Our study demonstrates that Imagine yourself
  surpasses the state-of-the-art personalization model, exhibiting superior
  capabilities in identity preservation, visual quality, and text alignment. This
  model establishes a robust foundation for various personalization applications.
  Human evaluation results validate the model's SOTA superiority across all
  aspects (identity preservation, text faithfulness, and visual appeal) compared
  to the previous personalization models.
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paper_title: Imagine yourself: Tuning-Free Personalized Image Generation
paper_summary: Diffusion models have demonstrated remarkable efficacy across various
  image-to-image tasks. In this research, we introduce Imagine yourself, a
  state-of-the-art model designed for personalized image generation. Unlike
  conventional tuning-based personalization techniques, Imagine yourself operates
  as a tuning-free model, enabling all users to leverage a shared framework
  without individualized adjustments. Moreover, previous work met challenges
  balancing identity preservation, following complex prompts and preserving good
  visual quality, resulting in models having strong copy-paste effect of the
  reference images. Thus, they can hardly generate images following prompts that
  require significant changes to the reference image, \eg, changing facial
  expression, head and body poses, and the diversity of the generated images is
  low. To address these limitations, our proposed method introduces 1) a new
  synthetic paired data generation mechanism to encourage image diversity, 2) a
  fully parallel attention architecture with three text encoders and a fully
  trainable vision encoder to improve the text faithfulness, and 3) a novel
  coarse-to-fine multi-stage finetuning methodology that gradually pushes the
  boundary of visual quality. Our study demonstrates that Imagine yourself
  surpasses the state-of-the-art personalization model, exhibiting superior
  capabilities in identity preservation, visual quality, and text alignment. This
  model establishes a robust foundation for various personalization applications.
  Human evaluation results validate the model's SOTA superiority across all
  aspects (identity preservation, text faithfulness, and visual appeal) compared
  to the previous personalization models.
paper_ai_summary: Imagine yourself is a tuning-free diffusion model for personalized image generation that enhances identity preservation, text alignment, and visual quality through synthetic data generation, parallel attention architecture, and multi-stage fine-tuning.
paper_ai_keywords: diffusion models personalization synthetic paired data parallel attention architecture text encoders vision encoder multi-stage fine-tuning identity preservation text alignment visual quality