mlsanityData to 14 Oct 2026

Multi-Subject Personalization

arXiv 2405.12742 ↗First posted 21 May 2024cs.CVcited by 1

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Abstract (arXiv)

Creative story illustration requires a consistent interplay of multiple characters or objects. However, conventional text-to-image models face significant challenges while producing images featuring multiple personalized subjects. For example, they distort the subject rendering, or the text descriptions fail to render coherent subject interactions. We present Multi-Subject Personalization (MSP) to alleviate some of these challenges. We implement MSP using Stable Diffusion and assess our approach against other text-to-image models, showcasing its consistent generation of good-quality images representing intended subjects and interactions.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 16 references that are in the map. First posted on arXiv 21 May 2024.

Builds on

14 of 16 references.

References · 14

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  5. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  6. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  7. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  8. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  9. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,424
  10. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
  11. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,106
  12. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,064
2 more
  1. 2018ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentationcited by 623
  2. 2023Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuningcited by 191

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Personalized Image Generation with Deep Generative Models: A Decade Surveycited by 20

Similar, not cited

Close in wording, no citation either way

  1. 2024A Survey on Personalized Content Synthesis with Diffusion Models
  2. 2024Visual Concept-driven Image Generation with Text-to-Image Diffusion Model
  3. 2024Pick-and-Draw: Training-free Semantic Guidance for Text-to-Image Personalization
  4. 2026MS-CustomNet: Controllable Multi-Subject Customization with Hierarchical Relational Semantics
  5. 2025StorySync: Training-Free Subject Consistency in Text-to-Image Generation via Region Harmonization
  6. 2026CRAFT: Constrained Reward via Attention Fine-Tuning for Subject Personalization without Composed Targets
  7. 2025PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards
  8. 2026MultiCompose: Multi-Concept Personalized Composition with Per-Subject Attribute Binding