mlsanityData to 14 Oct 2026

Personalized Image Filter: Mastering Your Photographic Style

arXiv 2510.16791 ↗First posted 19 Oct 2025cs.CVcited by 2

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Abstract (arXiv)

Photographic style, as a composition of certain photographic concepts, is the charm behind renowned photographers. But learning and transferring photographic style need a profound understanding of how the photo is edited from the unknown original appearance. Previous works either fail to learn meaningful photographic concepts from reference images, or cannot preserve the content of the content image. To tackle these issues, we proposed a Personalized Image Filter (PIF). Based on a pretrained text-to-image diffusion model, the generative prior enables PIF to learn the average appearance of photographic concepts, as well as how to adjust them according to text prompts. PIF then learns the photographic style of reference images with the textual inversion technique, by optimizing the prompts for the photographic concepts. PIF shows outstanding performance in extracting and transferring various kinds of photographic style. Project page: https://pif.pages.dev/

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 62 of its 76 references that are in the map. First posted on arXiv 19 Oct 2025.

Builds on

62 of 76 references.

References · 62

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38 more
  1. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,937
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50 most cited of 62 All 62 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026SemiNFT: Learning to Transfer Presets from Imitation to Appreciation via Hybrid-Sample Reinforcement Learningcited by 0
  2. 2026PALATE: Personalized Aesthetic Learning through Adaptive Taste Evolution for Multi-User Portrait Retouchingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026StyleGallery: Training-free and Semantic-aware Personalized Style Transfer from Arbitrary Image References
  2. 2022Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models
  3. 2024RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control
  4. 2022Inversion-Based Style Transfer with Diffusion Models
  5. 2024PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction
  6. 2024InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation
  7. 2025ACCORD: Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffusion Personalization
  8. 2023Personalized Restoration via Dual-Pivot Tuning