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Paper Citation Record · LEDGER

MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 36 inbound Pith citation observations for arXiv:2406.07209.

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

pith.paper-citation-record.v1
2406.07209 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:32:32.342607Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 16e67ab2-97dc-4c69-83ca-d7536a8ad75a · inbound

LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation cites this paper.

LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 24

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no resolver link, observed 2026-08-12T14:57:41.712876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:57:41.712876Z digest=sha256:223fbacd1da843ea78c83436c842b8345389c42ff135fb2a3797957ab32ca8b3

Observation 1bd4993e-dd9a-48fc-ab45-0499e4c12f62 · inbound

Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator cites this paper.

Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 50

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no resolver link, observed 2026-08-12T14:20:16.157314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:20:16.157314Z digest=sha256:02e95fcb2b4b78fbf4f9849db6f84476f9851b36409b5d28fb4f64f85525de4f

Observation 07fd6cc7-b14b-4ecc-afa0-ed82f43fb338 · inbound

Controllable Human Image Generation with Personalized Multi-Garments cites this paper.

Controllable Human Image Generation with Personalized Multi-Garments MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 48

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no resolver link, observed 2026-08-12T13:19:11.797139Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:11.797139Z digest=sha256:639a6e2aa2db76fa38cb3514695b5a3a05dd2a9dd8b9e360f95b65bfab5cbe6d

Observation 630cb608-16f1-44db-b777-b835ebd84150 · inbound

PersonaCraft: Personalized and Controllable Full-Body Multi-Human Scene Generation Using Occlusion-Aware 3D-Conditioned Diffusion cites this paper.

PersonaCraft: Personalized and Controllable Full-Body Multi-Human Scene Generation Using Occlusion-Aware 3D-Conditioned Diffusion MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 83

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no resolver link, observed 2026-08-12T11:36:17.376187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:36:17.376187Z digest=sha256:a47be63b7c9da18de4131cc51c90aa6f131b604435338e7a2e0bd1c2e5992115

Observation ca150fd3-3187-4dba-a3b2-6b5f1e1454b1 · inbound

Personalized Multimodal Large Language Models: A Survey cites this paper.

Personalized Multimodal Large Language Models: A Survey MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 65

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no resolver link, observed 2026-08-11T23:49:58.599089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T23:49:58.599089Z digest=sha256:794ea2a20d13a91efcfe7a221dbc26ac8408387baee8045a3b5d329ee2eaa6f3

Observation 7bc0acfa-86de-4ee7-a5ef-6b40c029923b · inbound

PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation cites this paper.

PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 39

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no resolver link, observed 2026-08-11T22:45:31.104742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:45:31.104742Z digest=sha256:a980c73e34166ba93a58b424058e5c1580923b9ca6c1df6f9419f08ab4866870

Observation f2f202ca-55c7-436d-ad5d-3d1d62dda530 · inbound

T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts cites this paper.

T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 52

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arxiv_id, observed 2026-05-23T08:02:43.066441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-23T08:00:12.781392Z digest=sha256:c848260cc25a114fc7750d532f7ac08cf2a7dc7e95c0ae7207aeaac917b51791

Observation 5c9e2ff2-4851-4628-98ba-d9049b3e2f36 · inbound

DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation cites this paper.

DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 39

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no resolver link, observed 2026-08-11T18:45:41.290187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:45:41.290187Z digest=sha256:13919cbcf779dbafa0c0537fbea353fb6bfb965fd0c13c0854480d72d986c779

Observation 4e6ed6cb-3327-4014-95a6-3b5ced9aa4e9 · inbound

TryOffAnyone: Tiled Cloth Generation from a Dressed Person cites this paper.

TryOffAnyone: Tiled Cloth Generation from a Dressed Person MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 43

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no resolver link, observed 2026-08-11T17:48:01.810276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:48:01.810276Z digest=sha256:61449f8436f758d32b3f667f7bd58fd698cbacbb7611b9df7f8044f049d6622a

Observation 9c72c3d1-1274-4250-817f-e914161b1b8c · inbound

UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation cites this paper.

UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 56

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no resolver link, observed 2026-08-11T04:24:20.217599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T04:24:20.217599Z digest=sha256:ab25d67f6272f435bcbd14d6df6e1aa857b9c341844e105a86d146221e383fba

Observation 918fc634-a9f3-4d9c-9e37-1a8e8e529f79 · inbound

ConceptMaster: Multi-Concept Video Customization on Diffusion Transformer Models Without Test-Time Tuning cites this paper.

ConceptMaster: Multi-Concept Video Customization on Diffusion Transformer Models Without Test-Time Tuning MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 61

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no resolver link, observed 2026-08-10T21:32:12.149744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:32:12.149744Z digest=sha256:33e848d1210db6dc0c943f5aeb647313c1724d2f1b7d80e3a757a5b65636ce84

Observation a2edb817-dc78-4de9-b11b-ba4f2366217b · inbound

FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image Generation cites this paper.

FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image Generation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 24

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metadata mismatch
arxiv_id, observed 2026-05-22T18:11:54.549732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-22T18:09:50.633826Z digest=sha256:6cc76c192e0efa5da69c5c773b54eb56e55c2eda821b8a759b2d82ebf53615e2

Observation 471aa53f-5e5b-4900-9b34-b71aad0147e2 · inbound

Learning Zero-Shot Subject-Driven Video Generation Using 1% Compute cites this paper.

Learning Zero-Shot Subject-Driven Video Generation Using 1% Compute MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 48

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arxiv_id, observed 2026-05-22T17:51:54.324109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-22T17:51:41.947939Z digest=sha256:a5baf2490b860907abffb36cccf05af3612744b7fca77b399d293e75c156f880

Observation aa235487-63b2-43a4-ae93-ecce87215173 · inbound

BridgeIV: Bridging Customized Image and Video Generation through Test-Time Autoregressive Identity Propagation cites this paper.

BridgeIV: Bridging Customized Image and Video Generation through Test-Time Autoregressive Identity Propagation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 41

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no resolver link, observed 2026-08-15T22:32:32.342607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:32:32.342607Z digest=sha256:d504d3c795edd4057eb7b8c7d0689650a73b2d545b0c93ff764d4baefdfe9669

Observation 3d81d521-1b15-49d3-a362-31d62bcf6695 · inbound

MMIG-Bench: Towards Comprehensive and Explainable Evaluation of Multi-Modal Image Generation Models cites this paper.

MMIG-Bench: Towards Comprehensive and Explainable Evaluation of Multi-Modal Image Generation Models MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 54

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no resolver link, observed 2026-08-07T14:17:36.838755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:36.838755Z digest=sha256:6c085015e512ff5aa8302882616b99671d40281ba9a7dacf2ede86fe3d3ac26a

Observation f9fc5dca-64d6-4fd3-ac47-eb9cc2073c7c · inbound

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects cites this paper.

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 16

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no resolver link, observed 2026-08-07T13:48:34.780116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:48:34.780116Z digest=sha256:da3c6067bdf1696f098fea898db019e4024271c54703b3efdfc416b09382cfb3

Observation 450ed4af-5e58-40fc-8b51-702408054b51 · inbound

Identity-Preserving Text-to-Image Generation via Dual-Level Feature Decoupling and Expert-Guided Fusion cites this paper.

Identity-Preserving Text-to-Image Generation via Dual-Level Feature Decoupling and Expert-Guided Fusion MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 59

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no resolver link, observed 2026-08-07T13:14:51.605547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:14:51.605547Z digest=sha256:f108908ad7dd55c47ea15b13515bca7ccc3f99510d59b7e257e16fe5157bfa73

Observation 35f5ae99-6e02-40e4-bc21-64cd33791876 · inbound

Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis cites this paper.

Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 46

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no resolver link, observed 2026-08-07T12:53:28.265120Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:53:28.265120Z digest=sha256:9231add5aefb68c64f00c891ba451d1b444ba092af17c37d4941db75575010cf

Observation 1a75200a-4fdf-459b-8c8a-150a57d29d68 · inbound

FontAdapter: Instant Font Adaptation in Visual Text Generation cites this paper.

FontAdapter: Instant Font Adaptation in Visual Text Generation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 37

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no resolver link, observed 2026-08-07T10:18:26.877834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:18:26.877834Z digest=sha256:bb7ab62d5212e4ee1bddb9a19f893b349fed68a9b3a0272d9627255a046860ad

Observation 01d4ffab-c112-4897-a612-a5bcaabc56c8 · inbound

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization cites this paper.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 42

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no resolver link, observed 2026-08-06T20:46:25.145387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:25.145387Z digest=sha256:b97c406fb9b5d4882d0c9e3719b4770539fd0c9dd317599026b166c6e44f78f1

Observation 961c9dc6-24ff-43a8-8f69-930a481a52df · inbound

FreeCus: Free Lunch Subject-driven Customization in Diffusion Transformers cites this paper.

FreeCus: Free Lunch Subject-driven Customization in Diffusion Transformers MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 62

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no resolver link, observed 2026-08-06T15:42:23.898097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:42:23.898097Z digest=sha256:61fa4bd102fdf60e9649f74ef903bb446444fc042f1160f22fde17ed6ff6667c

Observation 8ae67de0-cdcb-48a3-91c3-d219d47017c6 · inbound

FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy cites this paper.

FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 54

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no resolver link, observed 2026-08-05T18:37:22.326432Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:37:22.326432Z digest=sha256:a159618717ceb3e42ee9334f97b2fd64304a816f2adde2c6e444ac69f9c85514

Observation 1e93c7eb-55eb-448e-8970-9f027a610277 · inbound

OmniCache: A Trajectory-Oriented Global Perspective on Training-Free Cache Reuse for Diffusion Transformer Models cites this paper.

OmniCache: A Trajectory-Oriented Global Perspective on Training-Free Cache Reuse for Diffusion Transformer Models MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 50

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no resolver link, observed 2026-08-05T17:42:59.002057Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T17:42:59.002057Z digest=sha256:f7027d3e4098483512bc5a5b48392a9e859e122ad2962122dca5a83cded7ba23

Observation e1af5e4c-63e2-45e3-bca6-ec8d16b5a907 · inbound

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward cites this paper.

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 29

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no resolver link, observed 2026-08-04T23:06:09.104752Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T23:06:09.104752Z digest=sha256:663ab28151d96eac5427926e7520e81a83cb4f7b5fdcc631cffb54b0d4213067

Observation 3edebac2-ca7b-45fa-981c-ffb08a5d2651 · inbound

Animalbooth: multimodal feature enhancement for animal subject personalization cites this paper.

Animalbooth: multimodal feature enhancement for animal subject personalization MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 17

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arxiv_id, observed 2026-05-18T15:11:32.545104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-18T15:08:36.365653Z digest=sha256:4586343c2e3cd2b9b286f4a46496cf4be9f8d32ff130493e3ffa6c5aa599ff41

Observation 3c035540-f3ff-47d4-b4c3-b9d9e80a53da · inbound

PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards cites this paper.

PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 35

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arxiv_id, observed 2026-05-17T03:51:29.447762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-17T03:49:05.489626Z digest=sha256:1019580b01f51a1c245c26b55e8e45d36e2bed5dca2284d86b746401ac8e36c8

Observation 7cc84ab5-5e7a-46de-a1ce-a5763b1880bd · inbound

LooseRoPE: Content-aware Attention Manipulation for Semantic Harmonization cites this paper.

LooseRoPE: Content-aware Attention Manipulation for Semantic Harmonization MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 42

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arxiv_id, observed 2026-05-16T16:08:04.470931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-16T16:06:20.797660Z digest=sha256:f7600c8b07bdee5f864f2044164aaf222ffe2d6735b215082ebaff2d549a921c

Observation d32c443f-6bb8-4682-9186-922de763ece1 · inbound

DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation cites this paper.

DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 63

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metadata mismatch
arxiv_id, observed 2026-05-15T15:20:09.088674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-15T15:16:59.801347Z digest=sha256:d1e17ac4a41d4348e079a38b8f1ebf561674fafb2d1f218d0712212c7956bef7

Observation 3b4d2939-9722-491b-a16d-372f81f77ff0 · inbound

DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation cites this paper.

DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 63

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no resolver link, observed 2026-07-15T12:51:20.969593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-15T12:51:20.969593Z digest=sha256:8ea29cc61ff5521e20a5a4c0eac8f0e0c57ba9b40281c3a0f0fdcc055aef853c

Observation a33e9608-d78b-4d90-85ee-624103db4a0c · inbound

ASTRA: Enhancing Multi-Subject Generation with Retrieval-Augmented Pose Guidance and Disentangled Position Embedding cites this paper.

ASTRA: Enhancing Multi-Subject Generation with Retrieval-Augmented Pose Guidance and Disentangled Position Embedding MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 40

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metadata mismatch
arxiv_id, observed 2026-05-10T14:10:29.263978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-10T13:49:59.632467Z digest=sha256:025fb5a51de85cf8fd9b28f92979a24212b6779804b3165c2d8dd50e848af0c7

Observation 524961f5-bf00-4db4-8037-e997ead0bfa9 · inbound

EM-Vid: Training-Free Entity-Centric Memory for Efficient and Consistent Multi-Shot Video Generation cites this paper.

EM-Vid: Training-Free Entity-Centric Memory for Efficient and Consistent Multi-Shot Video Generation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 17

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verified exact
arxiv_id, observed 2026-05-25T05:00:22.391492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-25T04:57:13.479165Z digest=sha256:17c77e78dd121b42e2c4d582f588eb836b140270857e83a83a09c5daebce8e9b

Observation c267c162-97ff-4d12-87a4-a4796848549b · inbound

MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale cites this paper.

MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 52

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metadata mismatch
arxiv_id, observed 2026-06-29T18:33:50.277158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T18:32:37.613906Z digest=sha256:b21fbb27640e2ec03de1b16894f9f398c899bc13ca0066ce2ec6ad56b0c36810

Observation e303529f-b4e6-4b23-83fe-9d02c1583eb9 · inbound

UniVerse: A Unified Modulation Framework for Segmentation-Free,Disentangled Multi-Concept Personalization cites this paper.

UniVerse: A Unified Modulation Framework for Segmentation-Free,Disentangled Multi-Concept Personalization MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 31

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metadata mismatch
arxiv_id, observed 2026-07-01T19:26:00.744974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-28T22:34:05.043186Z digest=sha256:bab997f4224760e8433bfe31bb84335790ba16469eed4a6ac8ab2decbba2dab3

Observation 6ceb7e35-cca1-47b8-88cc-3309a06cbfd9 · inbound

A Comprehensive Ecosystem for Open-Domain Customized Video Generation cites this paper.

A Comprehensive Ecosystem for Open-Domain Customized Video Generation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T10:17:57.913110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-27T10:06:33.572182Z digest=sha256:d32d60507a00c671478c94c007f7182c9a4ea5857bf7f2fffde27fb293c55b79

Observation c14420b5-2fe6-4c09-8906-9aabacf9cc11 · inbound

Atomistic Language Models Understand and Generate Materials cites this paper.

Atomistic Language Models Understand and Generate Materials MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 72

Resolution
verified exact
arxiv_id, observed 2026-07-04T06:09:36.519416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-26T14:54:28.608607Z digest=sha256:4ff26ce6b8389a6ad5409ce1ecb26a2818984a3175c6afe8574867c378aba0b3

Observation f1390630-648d-4d6c-b17d-f6e3e1daf51c · inbound

MIBE: Multi-subject Interaction Benchmark and Evaluator for Personalized Image Generation cites this paper.

MIBE: Multi-subject Interaction Benchmark and Evaluator for Personalized Image Generation MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

Reference 18

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T21:08:57.526105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-03T21:05:29.882088Z digest=sha256:00da03aff1e589a2cb0b22e5b149e07544313bb30a2082738546b61f13972d85