Paper Citation Record · LEDGER

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing

As of 23 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 3 inbound Pith citation observations for arXiv:2505.02823.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.02823 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:43:56.299030Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:37:04.577206Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T21:10:09.698231Z

Reference resolution

49 of 49 outbound references displayed

verified exact0
verified fuzzy17
unresolved32
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 29d79784-9209-4079-b923-e552f445071d · outbound

This paper cites Blended diffusion for text-driven editing of natural images.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Blended diffusion for text-driven editing of natural images

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.069236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41b2d281-2d2c-471e-abb0-02a55605de6a · outbound

This paper cites Stable flow: Vital layers for training-free image editing, 2024.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Stable flow: Vital layers for training-free image editing, 2024

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.074798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 899dd1ff-07a2-4124-bea6-1cfe5bdd3efc · outbound

This paper cites Diffusion Self-Distillation for Zero-Shot Customized Image Generation.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Diffusion Self-Distillation for Zero-Shot Customized Image Generation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.079822Z

Source-reported events for the cited work

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Observation 9d6c18d2-640a-4e7b-8c0f-19b4ab977e29 · outbound

This paper cites Emerging properties in self-supervised vision transformers, 2021.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Emerging properties in self-supervised vision transformers, 2021

Reference 4

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Source-reported events for the cited work

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Observation d13ccb1b-aaeb-4c3c-9c2a-5b23915b2ea6 · outbound

This paper cites Anydoor: Zero-shot object-level image customization.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Anydoor: Zero-shot object-level image customization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.930122Z

Source-reported events for the cited work

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Observation 01fefccd-c054-450c-8b23-faa792628daa · outbound

This paper cites UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.095195Z

Source-reported events for the cited work

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Observation 7602f7ba-4b4b-471a-a2ec-8d4653d97a18 · outbound

This paper cites Diffusion models beat gans on image synthesis.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Diffusion models beat gans on image synthesis

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.914484Z

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Observation e7997c02-a016-4b96-9618-d28fcec7dbd9 · outbound

This paper cites Scaling rectified flow transform- ers for high-resolution image synthesis.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Scaling rectified flow transform- ers for high-resolution image synthesis

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.898818Z

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Observation 67d17c2a-eed3-43ea-9c61-ae4eaf0cbbdf · outbound

This paper cites An image is worth one word: Personalizing text-to-image generation using textual inversion.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing An image is worth one word: Personalizing text-to-image generation using textual inversion

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.882984Z

Source-reported events for the cited work

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Observation 51ebebb6-d147-47ff-bccf-1a33c5235e6b · outbound

This paper cites An image is worth one word: Personalizing text-to-image generation using textual inversion.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing An image is worth one word: Personalizing text-to-image generation using textual inversion

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.115151Z

Source-reported events for the cited work

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Observation 46cfbd4d-2cd5-4251-8cb3-daad7d4ea556 · outbound

This paper cites Bermano, Gal Chechik, and Daniel Cohen-Or.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Bermano, Gal Chechik, and Daniel Cohen-Or

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.857505Z

Source-reported events for the cited work

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Observation 898ee990-0328-4414-ae32-6178e54c214a · outbound

This paper cites Pulid: Pure and lightning id customization via contrastive alignment.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Pulid: Pure and lightning id customization via contrastive alignment

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.842676Z

Source-reported events for the cited work

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Observation 262c6221-7b35-4e9b-b5db-280b57ac2197 · outbound

This paper cites Svdiff: Compact parameter space for diffusion fine-tuning.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Svdiff: Compact parameter space for diffusion fine-tuning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.827266Z

Source-reported events for the cited work

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Observation 6d308c08-2e75-4c7f-8ef8-00e10a6c628c · outbound

This paper cites Denoising diffusion probabilistic models.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Denoising diffusion probabilistic models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.134495Z

Source-reported events for the cited work

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Observation e4693aea-af85-4328-baed-d250441d032d · outbound

This paper cites Lora: Low-rank adaptation of large language models.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Lora: Low-rank adaptation of large language models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.139177Z

Source-reported events for the cited work

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Observation 78b06020-0cf3-4d30-b4dd-6a71d8c250ac · outbound

This paper cites Group Diffusion Transformers are Unsupervised Multitask Learners.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Group Diffusion Transformers are Unsupervised Multitask Learners

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7eaa74cf-b9ca-4584-b045-3ba110e553f4 · outbound

This paper cites In-Context LoRA for Diffusion Transformers.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing In-Context LoRA for Diffusion Transformers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.148674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09d62c7a-8d49-4a25-8381-00ee0fade02f · outbound

This paper cites Realcustom: Narrowing real text word for real-time open-domain text-to-image customization.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Realcustom: Narrowing real text word for real-time open-domain text-to-image customization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.792679Z

Source-reported events for the cited work

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Observation d05f296f-9aee-402f-abbc-3acfaba3a659 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Elucidating the design space of diffusion-based generative models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.776713Z

Source-reported events for the cited work

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Observation 7cd9660a-ae43-4a66-9e9e-d71cd241d1b2 · outbound

This paper cites Multi- concept customization of text-to-image diffusion.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Multi- concept customization of text-to-image diffusion

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.760719Z

Source-reported events for the cited work

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Observation e8f46b52-8820-4690-bf85-30fda10b2157 · outbound

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MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Unresolved cited work

Reference 21

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Source-reported events for the cited work

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Observation 130c68ac-2439-4081-9721-0e6351cda81f · outbound

This paper cites One Diffusion to Generate Them All.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing One Diffusion to Generate Them All

Reference 22

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Source-reported events for the cited work

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Observation 65b284df-7624-4203-b09a-e1c022f9acdd · outbound

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MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Unresolved cited work

Reference 23

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Source-reported events for the cited work

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Observation 31f01ff4-f5fb-4e20-8a97-9311b0ff1f7a · outbound

This paper cites Repaint: Inpainting using denoising diffusion probabilistic models.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Repaint: Inpainting using denoising diffusion probabilistic models

Reference 24

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Source-reported events for the cited work

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Observation c90a42ca-080b-4310-b6c7-e987653f8ed3 · outbound

This paper cites Subject-diffusion: Open domain personal- ized text-to-image generation without test-time fine-tuning.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Subject-diffusion: Open domain personal- ized text-to-image generation without test-time fine-tuning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.716022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 09efb1a8-b1f5-47b0-a487-4f01a69eb43b · outbound

This paper cites ACE++: Instruction-Based Image Creation and Editing via Context-Aware Content Filling.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing ACE++: Instruction-Based Image Creation and Editing via Context-Aware Content Filling

Reference 26

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Source-reported events for the cited work

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Observation d978f873-dd67-43a2-b555-0f92bd9c4216 · outbound

This paper cites SDEdit: Guided image synthesis and editing with stochastic differential equations.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing SDEdit: Guided image synthesis and editing with stochastic differential equations

Reference 27

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Source-reported events for the cited work

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Observation cf6b42a6-f1ea-40f3-831a-aeac4923cf1f · outbound

This paper cites T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.201828Z

Source-reported events for the cited work

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Observation b500700c-9c31-44d7-913b-ac17e7cfeb24 · outbound

This paper cites Scalable diffusion models with transformers.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Scalable diffusion models with transformers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.206557Z

Source-reported events for the cited work

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Observation b48f7942-4f99-4877-aced-8546419199a7 · outbound

This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Sdxl: Improving latent diffusion models for high-resolution image synthesis

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.211128Z

Source-reported events for the cited work

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Observation 86e11435-8962-4c36-ac73-7ed570dd3e13 · outbound

This paper cites Learning transferable visual models from natural language supervision.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Learning transferable visual models from natural language supervision

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.215664Z

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Observation 854e1c48-2929-492b-ba58-39bc1258c57d · outbound

This paper cites Learning transferable visual models from natural language supervision, 2021.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Learning transferable visual models from natural language supervision, 2021

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.220082Z

Source-reported events for the cited work

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Observation 0db645d7-53fd-42bc-bdf5-11fc5e035657 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing High-resolution image synthesis with latent diffusion models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.224854Z

Source-reported events for the cited work

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Observation 18d81252-707c-4969-bed3-d8b2eb3e5c66 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 34

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Source-reported events for the cited work

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Observation e2911901-7e17-4253-9491-7fcab65b168a · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.233933Z

Source-reported events for the cited work

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Observation 95ab05fb-cad3-45aa-90d7-1b0eba04df8a · outbound

This paper cites Palette: Image-to-image diffusion models.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Palette: Image-to-image diffusion models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.238995Z

Source-reported events for the cited work

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Observation 07b8b4be-f8a7-42b7-a72e-26b4c34b5712 · outbound

This paper cites Deep unsuper- vised learning using nonequilibrium thermodynamics.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Deep unsuper- vised learning using nonequilibrium thermodynamics

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.604034Z

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Observation 28beea2d-ecc0-45e8-a830-e0f65a14bfd7 · outbound

This paper cites Consistency models.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Consistency models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.588410Z

Source-reported events for the cited work

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Observation 3de05775-7cac-4083-b708-433e97286b36 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Generative modeling by estimating gradients of the data distribution

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.252483Z

Source-reported events for the cited work

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Observation 74041210-070d-43d4-a13f-d35ee0bab0d7 · outbound

This paper cites OminiControl: Minimal and Universal Control for Diffusion Transformer.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing OminiControl: Minimal and Universal Control for Diffusion Transformer

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.257134Z

Source-reported events for the cited work

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Observation d98d4ba1-207b-49ee-ac76-1aad9534885a · outbound

This paper cites P+: Extended Textual Conditioning in Text-to-Image Generation.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing P+: Extended Textual Conditioning in Text-to-Image Generation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.261868Z

Source-reported events for the cited work

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Observation 19d961c4-631b-4754-86fa-0c40dbc50e3e · outbound

This paper cites Ms-diffusion: Multi- subject zero-shot image personalization with layout guidance.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Ms-diffusion: Multi- subject zero-shot image personalization with layout guidance

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.266738Z

Source-reported events for the cited work

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Observation d5f68ef3-bdc2-44c9-854e-2cabdf94491b · outbound

This paper cites Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.552852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1d979d23-2b70-4061-b9c9-5d85d41404b6 · outbound

This paper cites OmniGen: Unified Image Generation.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing OmniGen: Unified Image Generation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.276308Z

Source-reported events for the cited work

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Observation 8c683772-7d43-4f67-9327-66b282cf48dd · outbound

This paper cites Smartbrush: Text and shape guided object inpainting with diffusion model.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Smartbrush: Text and shape guided object inpainting with diffusion model

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.537202Z

Source-reported events for the cited work

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Observation 366a65a2-697a-4049-8de7-1cadb26b5da7 · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.285725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eff177dd-07db-4863-9a90-d6095c07c6b4 · outbound

This paper cites Adding conditional control to text-to-image diffusion models, 2023.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Adding conditional control to text-to-image diffusion models, 2023

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T00:43:56.290300Z

Source-reported events for the cited work

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Observation 89806b50-1493-46c2-a344-33232d384df6 · outbound

This paper cites Ssr-encoder: Encoding selective subject representation for subject-driven generation.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Ssr-encoder: Encoding selective subject representation for subject-driven generation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.511870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation df3dfc8d-fe53-4b0b-a200-832e344477c1 · outbound

This paper cites Egsde: Unpaired image-to-image transla- tion via energy-guided stochastic differential equations.

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing Egsde: Unpaired image-to-image transla- tion via energy-guided stochastic differential equations

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:43:56.496107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 60b4cc64-40c8-473c-a7b9-8547a08d6dd5 · inbound

Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling cites this paper.

Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:41:19.241247Z

Source-reported events for the cited work

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Observation 31533263-82fe-4d36-af2f-63c19dbb5506 · inbound

Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling cites this paper.

Reference 9

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Source-reported events for the cited work

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Observation 6f53dd1b-de7a-451d-a984-9cbe7ecd310d · inbound

DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation cites this paper.

DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-04T21:10:09.700412Z

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