mlsanityData to 7 Oct 2026

An Improved Method for Personalizing Diffusion Models

arXiv 2407.05312 ↗First posted 7 Jul 2024cs.CVcited by 2

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

Diffusion models have demonstrated impressive image generation capabilities. Personalized approaches, such as textual inversion and Dreambooth, enhance model individualization using specific images. These methods enable generating images of specific objects based on diverse textual contexts. Our proposed approach aims to retain the model's original knowledge during new information integration, resulting in superior outcomes while necessitating less training time compared to Dreambooth and textual inversion.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 13 of its 14 references that are in the map. First posted on arXiv 7 Jul 2024.

Builds on

13 of 14 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion15 Mar
  2. 2022Multi-Concept Customization of Text-to-Image Diffusion8 Dec
  3. 2022Imagic: Text-Based Real Image Editing with Diffusion Models17 Oct
  4. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation25 Aug
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latents13 Apr

Other references · 8

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  4. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  5. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  6. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
  7. 2023Subject-driven Text-to-Image Generation via Apprenticeship Learningcited by 210
  8. 2023A Recipe for Watermarking Diffusion Modelscited by 136

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025What Lurks Within? Concept Auditing for Shared Diffusion Models at Scalecited by 2
  2. 2026Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Preserve and Personalize: Personalized Text-to-Image Diffusion Models without Distributional Drift
  2. 2023ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
  3. 2026Zero-Shot Personalization of Objects via Textual Inversion
  4. 2023Generate Anything Anywhere in Any Scene
  5. 2024Fast Personalized Text-to-Image Syntheses With Attention Injection
  6. 2023DreamDistribution: Learning Prompt Distribution for Diverse In-distribution Generation
  7. 2023Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation
  8. 2025Parallel Rescaling: Rebalancing Consistency Guidance for Personalized Diffusion Models