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Paper Citation Record · LEDGER

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization

As of 11 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2507.01792.

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

pith.paper-citation-record.v1
2507.01792 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:46:25.184165Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T10:47:33.349484Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:36:27.298802Z

Reference resolution

57 of 57 outbound references displayed

  • verified exact3
  • verified fuzzy13
  • unresolved41
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 167cb2c2-ee55-4b97-ae9d-cd4272c8e5dd · outbound

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

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Emerging properties in self-supervised vision transformers

Reference 1

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source=pdf_text observed=2026-08-06T20:46:25.028880Z digest=sha256:664654000f9e0efd05733ecf69d5c10cb3ac10e18305b84237f9b8f7d77db633

Observation d054a812-3821-4f3a-bce9-b399a73db620 · outbound

This paper cites Re-Imagen: Retrieval-Augmented Text-to-Image Generator.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Re-Imagen: Retrieval-Augmented Text-to-Image Generator

Reference 2

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source=pdf_text observed=2026-08-06T20:46:25.032495Z digest=sha256:a8d6324237564e25bde7b7d3043cfdf7386dd9b3ec8f4dd3cfa18682d44ef7ec

Observation 31ad391d-7f20-4207-b166-ed9e0f0c5353 · outbound

This paper cites Fine-Tuning Visual Autoregressive Models for Subject-Driven Generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Fine-Tuning Visual Autoregressive Models for Subject-Driven Generation

Reference 3

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source=pdf_text observed=2026-08-06T20:46:25.035597Z digest=sha256:2a2630ba96468c68e829cbeabfa23573d758fcf978ae09d61ba4e2bf17afbf3c

Observation a73352d4-ad94-4a69-a9ad-4eba3d790769 · outbound

This paper cites Diffusion models beat gans on image synthesis.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Diffusion models beat gans on image synthesis

Reference 4

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source=pdf_text observed=2026-08-06T20:46:25.039451Z digest=sha256:38b9e846ec32a4cef8d9eda98ea4c52b56b44c26505417a398bfeafd096a0ec8

Observation 28332dc2-d5c0-40be-b0e7-8cd7f1bf6fb5 · outbound

This paper cites Freecustom: Tuning-free customized image generation for multi-concept composition.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Freecustom: Tuning-free customized image generation for multi-concept composition

Reference 5

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source=pdf_text observed=2026-08-06T20:46:25.042581Z digest=sha256:19eca093d8cf1a8e0615e7dae5f841d12b5dd2ed6d3dbaf070fa5f94460cbf0e

Observation 353f6925-f21c-457e-8976-4fc8c8e9418d · outbound

This paper cites How to continually adapt text-to-image diffusion models for flexible customization? Advances in Neural Information Processing Systems, 37:130057– 130083, 2024.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization How to continually adapt text-to-image diffusion models for flexible customization? Advances in Neural Information Processing Systems, 37:130057– 130083, 2024

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.045369Z digest=sha256:cf3699a27f7f945ae9f073094ba4256c6be1a671a6e5488ce3f65ded23c1b879

Observation 86bd7420-c899-4e96-9b17-393579d95aef · outbound

This paper cites Personalize Anything for Free with Diffusion Transformer.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Personalize Anything for Free with Diffusion Transformer

Reference 7

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source=pdf_text observed=2026-08-06T20:46:25.049029Z digest=sha256:eaf865083c021d48420db3abd8fe1f812db6476309edf1750e213e6441118c81

Observation 92e56349-3952-4fad-b58d-02baae021b17 · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 8

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source=pdf_text observed=2026-08-06T20:46:25.051723Z digest=sha256:28aec733e435dbdeb8920022b7e0f4b66dd5583b61ad2a687135b7ee49268a1a

Observation 31f846a3-0f91-4d1a-bf93-2fe5372e71d4 · outbound

This paper cites Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.054541Z digest=sha256:c81e95f44043f99f47b99600a056ce7888d5df052bee492a70b59f2a8f1ff8a4

Observation d132c8c1-a64a-4d34-9b98-fe717b4f3f61 · outbound

This paper cites Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

Reference 10

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source=pdf_text observed=2026-08-06T20:46:25.057308Z digest=sha256:e3b8dc03c328ce544793434524877ff3d487d0e6142c84e2eaa3e49b6ec3bc2d

Observation 3daeb8a1-c6b2-420d-b9ae-d9249e1a1eb6 · outbound

This paper cites an unresolved cited work.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Unresolved cited work

Reference 11

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.060286Z digest=sha256:23667e7363d6311929cd37333c3cfc33075fe562980624dc4c6361bb6a2831a8

Observation 99b509e4-117e-4b28-9ca2-1c3707f95bbb · outbound

This paper cites AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation

Reference 12

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source=pdf_text observed=2026-08-06T20:46:25.063313Z digest=sha256:345fed47e8be9aeff687856eee358dda991c2b495ef5b323b2b28f24898adf6f

Observation f836a9de-8462-4938-af5d-0b7253f5c689 · outbound

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

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Lora: Low-rank adaptation of large language models

Reference 13

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source=pdf_text observed=2026-08-06T20:46:25.065994Z digest=sha256:352d4feef157dbf55332a02e532c2acde494c9a8091c68579db8cbf04cab8582

Observation 8f391735-07ac-4300-91b5-dbfa85984df6 · outbound

This paper cites Resolving multi- condition confusion for finetuning-free personalized image generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Resolving multi- condition confusion for finetuning-free personalized image generation

Reference 14

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raw_fallback, observed 2026-08-06T20:46:25.907395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.068267Z digest=sha256:4d39eaf10028b99cdeac1267a15d10236c9776041146505d95f10e4712f345d9

Observation 3416f94e-0d6b-46db-abcc-b1c2dd9a0fa2 · outbound

This paper cites Flux Already Knows -- Activating Subject-Driven Image Generation without Training.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Flux Already Knows -- Activating Subject-Driven Image Generation without Training

Reference 15

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source=pdf_text observed=2026-08-06T20:46:25.070895Z digest=sha256:cf27586393335ba4f39573ffa25d25b719ebf132e585cc13afb1b9d4710b0b5d

Observation 8466ba5c-5d21-4684-8459-68c8e58b99f7 · outbound

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

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Multi- concept customization of text-to-image diffusion

Reference 16

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source=pdf_text observed=2026-08-06T20:46:25.073707Z digest=sha256:d8ed2a0ba61bc2bf609ea38b7040dce40ac57c9da85579602e153c77c68850bd

Observation 064eb7af-dd58-4c65-9870-5515ccf13194 · outbound

This paper cites Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing

Reference 17

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source=pdf_text observed=2026-08-06T20:46:25.076498Z digest=sha256:d40ae7fa4b2e9fdcd9f187674729d6d6bfabdb68208d49ac1c6efed07e03d037

Observation 50196571-73bb-4890-98f8-fe7b5f56ac8f · outbound

This paper cites Autoregressive image generation without vector quantization.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Autoregressive image generation without vector quantization

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.079075Z digest=sha256:36a0713efb73972c21ad22c46a40db7bcb650c19028d2fe5784814e1dd1e0f08

Observation 8435961e-0f4c-41df-b9cf-b6be601b9b84 · outbound

This paper cites Cones: Concept Neurons in Diffusion Models for Customized Generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Cones: Concept Neurons in Diffusion Models for Customized Generation

Reference 19

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source=pdf_text observed=2026-08-06T20:46:25.081451Z digest=sha256:8fcfeb17fad75e43ef56b7bd941707254dc8f3e24772f1bffeb758aca7128903

Observation 9ee6b897-399d-47f5-9a18-feff517941a7 · outbound

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

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Subject-diffusion: Open domain per- sonalized text-to-image generation without test-time fine-tuning

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.084086Z digest=sha256:de8a5f7e620a39d27a9c1dfcc0e92c31877a12f834e6af223eadd1048e8578d1

Observation 184c3bfd-f53c-4790-b513-d24ab9a734f4 · outbound

This paper cites Realcustom++: Representing images as real-word for real-time customization.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Realcustom++: Representing images as real-word for real-time customization

Reference 21

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source=pdf_text observed=2026-08-06T20:46:25.086500Z digest=sha256:6365a364dc75a0ec53100d93d65de89bfd74011e95921a4ad98184c28d1667ab

Observation 4fbb41c4-4cdf-4264-8c9a-fd24853709cc · outbound

This paper cites Contrastive Test-Time Composition of Multiple LoRA Models for Image Generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Contrastive Test-Time Composition of Multiple LoRA Models for Image Generation

Reference 22

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local_arxiv, observed 2026-08-06T20:46:25.620182Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.088929Z digest=sha256:af41718f0ce93d5ea69331c752b721786977619515683e9918b8f12cbf03b330

Observation 8e07b5f1-3843-4086-90c0-b4e8cae814b9 · outbound

This paper cites Dreamo: A unified framework for image customization.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Dreamo: A unified framework for image customization

Reference 23

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source=pdf_text observed=2026-08-06T20:46:25.091792Z digest=sha256:6af31013c27071438a3f5cdded3fa253c0684307ad591ea4c649abffa73b4b90

Observation e53d76d1-8a9f-4ed9-814e-c6aa7b432b57 · outbound

This paper cites Dreammatcher: appearance matching self-attention for semantically-consistent text-to-image personalization.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Dreammatcher: appearance matching self-attention for semantically-consistent text-to-image personalization

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.094480Z digest=sha256:42e0150a2d1d2a4e93640ddc9a8aa6de1b34e8ff67bac5a1422eff2c89973726

Observation 84c9b0e8-f94d-405c-9106-b7a2561077e9 · outbound

This paper cites K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs

Reference 25

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source=pdf_text observed=2026-08-06T20:46:25.097061Z digest=sha256:07590c4f7e8a121ace005befc0b8bd20056e8feb3e67abd9179f8bd6fe7950ea

Observation 88e5f9ab-6a6c-4bd2-996d-15b552a271cd · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 26

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source=pdf_text observed=2026-08-06T20:46:25.100071Z digest=sha256:e27f113064d76419403a2329cb891ad14bbaeb5c01835deddb5aa63d575da7ff

Observation 8821270a-c7a6-4894-a09b-e374cf55f68c · outbound

This paper cites BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models

Reference 27

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source=pdf_text observed=2026-08-06T20:46:25.102618Z digest=sha256:db21e952f60831d0743cd28d8f1dc6343a2ad7574b95bb1f892603bdd2d05e3c

Observation 51bd59b6-8b18-44d4-9b9f-c0a0341899a9 · outbound

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

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Learning transferable visual models from natural language supervision

Reference 28

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source=pdf_text observed=2026-08-06T20:46:25.105748Z digest=sha256:17ab8004f29506219bb551131889a3859ebf2d7f75fa570bd0514888114766e4

Observation b15c852f-b5e5-4e93-83d3-a785b4b11723 · outbound

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

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization High- resolution image synthesis with latent diffusion models

Reference 29

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source=pdf_text observed=2026-08-06T20:46:25.108416Z digest=sha256:570ee44ac843974e5f6bc4e382f8861d717255e75e1bc9a3e2f65d6a9aa1f580

Observation 080a2875-7a96-4f60-ad82-09f20c471452 · outbound

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

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 30

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raw_fallback, observed 2026-08-06T20:46:25.843195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.111227Z digest=sha256:1528f8f22d0f02ee38db3df4897fd9f2432ff584f2e9154de632d91c2f4d966c

Observation 5ff741cb-f447-4a7f-a467-0b95b850ef47 · outbound

This paper cites Low-rank adaptation for fast text-to-image diffusion fine-tuning.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Low-rank adaptation for fast text-to-image diffusion fine-tuning

Reference 31

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raw_fallback, observed 2026-08-06T20:46:25.833385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.113835Z digest=sha256:68e2c26ce94fe841b0d2f9ea8fab5572cce25412246647caadbfc084070fe597

Observation 6844e95e-d764-4373-ab3d-6481ebae83f4 · outbound

This paper cites Ziplora: Any subject in any style by effectively merging loras.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Ziplora: Any subject in any style by effectively merging loras

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-06T20:46:25.823848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.116949Z digest=sha256:60ad536446ee40ef987c71d5869b4cab5847f94a058c99759826e99be684b281

Observation 2876a87b-c445-4776-ae44-01f6a2031df1 · outbound

This paper cites Instantbooth: Personalized text-to- image generation without test-time finetuning.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Instantbooth: Personalized text-to- image generation without test-time finetuning

Reference 33

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raw_fallback, observed 2026-08-06T20:46:25.813999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.119585Z digest=sha256:03780e2837f8f2d77df491be645a44de0e2815dd928222a1f8012bdfff8f5e9c

Observation d2c473d0-10b9-4e03-938d-35c50e31c5ef · outbound

This paper cites LMFusion: Adapting Pretrained Language Models for Multimodal Generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization LMFusion: Adapting Pretrained Language Models for Multimodal Generation

Reference 34

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source=pdf_text observed=2026-08-06T20:46:25.122139Z digest=sha256:7d1f596b1bc56d937d70e3d2969022e04a1abcadf21113ecbfd83bc8931d055f

Observation d4a7eb96-39b5-49a4-a10e-94d32ea7ec29 · outbound

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

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:25.125226Z digest=sha256:c3f5ad88a51d7e272bf4eddd992151f9331885737c427e630820b229a9d2f5f8

Observation 6d30520e-af56-4eaa-94ec-2ec3f0cd33a4 · outbound

This paper cites Personalized Text-to-Image Generation with Auto-Regressive Models.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Personalized Text-to-Image Generation with Auto-Regressive Models

Reference 36

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local_arxiv, observed 2026-08-06T20:46:25.493407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.128283Z digest=sha256:a88968a21c7c038332bd715fe2db624d7f38070ef32955d581c9b5c7a40b3e1e

Observation 8a1efbc7-0eaf-4a23-bcba-d1d859027651 · outbound

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

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization OminiControl: Minimal and Universal Control for Diffusion Transformer

Reference 37

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:46:25.131067Z digest=sha256:a8bc0771ad321c8b7fb18c9fbf8fde0553dde6d03b54663cb254d598f6f48323

Observation 9cef4ad4-c3d5-42dd-9fab-7c553379faf5 · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 38

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:25.134133Z digest=sha256:6b1a06b8b08f8c05790b53edfa59178053209358854c698fbc8b72c0f93a4994

Observation 718177c7-6241-4a17-8f60-a388b90dd866 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Gemini: A Family of Highly Capable Multimodal Models

Reference 39

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:25.137294Z digest=sha256:09d2e37a802e68eb9827c6b8dcfddca5bcf13a65ccb359cbe745d58452f4b905

Observation cfe0b1b8-2fc4-408e-b4f4-bd0c26f872fb · outbound

This paper cites Visual autoregressive modeling: Scalable image generation via next-scale prediction.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 40

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raw_fallback, observed 2026-08-06T20:46:25.804927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.139909Z digest=sha256:cccc964b2480db65f3d245a371dff6d3178264ffcc7bbaf1477059accd017617

Observation 422d8613-9e19-4c61-8bd0-ac6ee5921553 · outbound

This paper cites MetaMorph: Multimodal Understanding and Generation via Instruction Tuning.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization MetaMorph: Multimodal Understanding and Generation via Instruction Tuning

Reference 41

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source=pdf_text observed=2026-08-06T20:46:25.142498Z digest=sha256:583b29f54ca3deaa1dea34a20259d701d40aa125765abf5b48c7da6b0226b3d5

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

This paper cites MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance.

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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source=pdf_text observed=2026-08-06T20:46:25.145387Z digest=sha256:ef7c856b35a76534f1549573d0d01ebb099ab5e475ba7ebd79a2468641fa9eff

Observation cb5a61bf-9868-47f0-8061-ffdbdac3eaaf · outbound

This paper cites Emu3: Next-Token Prediction is All You Need.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Emu3: Next-Token Prediction is All You Need

Reference 43

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:46:25.148076Z digest=sha256:d3e0c1f2d13f91df26bce03f2b644b7b50c3888a17cafc405c1ac295be71ead0

Observation 8aef106a-6b53-4159-ab89-819768ddd9f4 · outbound

This paper cites MaskBit: Embedding-free Image Generation via Bit Tokens.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization MaskBit: Embedding-free Image Generation via Bit Tokens

Reference 44

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:46:25.150779Z digest=sha256:54a4d17d76109e562dfbda4a3b4d7789f0a97a8b270a631224e0cbf14987953a

Observation 77bbd027-384c-4110-af1b-a363a1daa04a · outbound

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

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation

Reference 45

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verified fuzzy
raw_fallback, observed 2026-08-06T20:46:25.795510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.153481Z digest=sha256:a2729a3ca1376c2445bca343913d10e85c5e3534d5da905dd593a475ca0f3e5f

Observation b6c24b32-0c70-4d77-9ea5-2b4950a3001b · outbound

This paper cites Less-to-More Generalization: Unlocking More Controllability by In-Context Generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Less-to-More Generalization: Unlocking More Controllability by In-Context Generation

Reference 46

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:46:25.155847Z digest=sha256:698007a685b22a183b57412b0b787cd5bd9bfdfa4756fbd3ce307528ba826831

Observation 84399f32-9b05-44de-be7b-e6d63279cf5c · outbound

This paper cites VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation

Reference 47

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:46:25.158437Z digest=sha256:c1101a44bae8176f81bbdb98ca825b7d5d803676ba0f5fc3b4f96e58842d8464

Observation 16a9940c-6d55-4c0c-8409-f132b31d3964 · outbound

This paper cites Proxy-tuning: Tailoring multimodal autoregressive models for subject-driven image generation.arXiv preprint arXiv:2503.10125, 2025.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Proxy-tuning: Tailoring multimodal autoregressive models for subject-driven image generation.arXiv preprint arXiv:2503.10125, 2025

Reference 48

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source=pdf_text observed=2026-08-06T20:46:25.160916Z digest=sha256:df156a878543664d1ea3696f3b88834d9dc383a56a775eb94c1d073449788624

Observation 5219faa1-abd7-4c91-92e6-a98c9087b37c · outbound

This paper cites OmniGen: Unified Image Generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization OmniGen: Unified Image Generation

Reference 49

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source=pdf_text observed=2026-08-06T20:46:25.163303Z digest=sha256:6705536000d1359e3a2c5749a26e28ce7d23dc689e8fcc13037e6541bd9b73a9

Observation 2a250201-6323-47f5-aa9b-08bfbc18b5e5 · outbound

This paper cites Show-o: One Single Transformer to Unify Multimodal Understanding and Generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Reference 50

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source=pdf_text observed=2026-08-06T20:46:25.166001Z digest=sha256:a6cfd464743ac0fe44e50952e771e861dc3dd3ee801be4dbd3cebbe2b1bdeb6a

Observation ba274313-38db-4100-aad5-7433f328081c · outbound

This paper cites LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models

Reference 51

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source=pdf_text observed=2026-08-06T20:46:25.168524Z digest=sha256:3cb498c17789bd47567ca2bee18116357ee922fb86d82f5f215f925839384758

Observation 9e8881ac-002b-4dbf-962e-e43d4bddf7e9 · outbound

This paper cites Randomized Autoregressive Visual Generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Randomized Autoregressive Visual Generation

Reference 52

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source=pdf_text observed=2026-08-06T20:46:25.171340Z digest=sha256:63cc3f3994f57a1184c1891bc9bfc83f06dc8dd7a66ad29785a5bacedd33bf20

Observation 6c76c341-23da-4ae6-8b4a-634a321a2f69 · outbound

This paper cites IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:46:25.259185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.173889Z digest=sha256:e91438bb305c3d1abaf9eaa777dab94ff9edd039c64c8852d81f228be1334022

Observation 9a92617c-a5a8-4975-8e5d-49b5eb53fbf9 · outbound

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

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Ssr-encoder: Encoding selective subject representation for subject- driven generation

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:46:25.786130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:46:25.176398Z digest=sha256:3fcb9eff66a803a361606249696f8a53eb6e10b0c39855ed6bcfdb4e89b47cac

Observation e858c169-9f28-4f67-bf7f-92c83b2fc23f · outbound

This paper cites Multi-LoRA Composition for Image Generation.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Multi-LoRA Composition for Image Generation

Reference 55

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source=pdf_text observed=2026-08-06T20:46:25.179142Z digest=sha256:66d265ccca4837bd9f2fbafd8581645676200cbd367da2e92b88dfe3078abfdf

Observation c94e3f62-b8a5-4d1f-b77b-32154ac0ebf5 · outbound

This paper cites Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 56

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no resolver link, observed 2026-08-06T20:46:25.181575Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:25.181575Z digest=sha256:cc0c34d8a1876d22cd4fd826d1fc10ffcb61618f835cde639cd585e9badcee1d

Observation 241b398b-0eb7-4dce-a27f-cd8f7abdcbc2 · outbound

This paper cites MagicTailor: Component-Controllable Personalization in Text-to-Image Diffusion Models.

FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization MagicTailor: Component-Controllable Personalization in Text-to-Image Diffusion Models

Reference 57

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:46:25.184165Z digest=sha256:a81af45216b73bca8eec76208d34f2a2c2a91aff3aee0b197cd870aaca7083b4

Pith citing papers

Observation 9c232075-9860-4f4f-bd18-c1f7400c07a5 · inbound

Training-Free Multi-Concept LoRA Composition with Prompt-Aware Weighting cites this paper.

Training-Free Multi-Concept LoRA Composition with Prompt-Aware Weighting FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization

Reference 66

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arxiv_id, observed 2026-07-02T02:36:27.300440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-28T10:47:33.349484Z digest=sha256:3e5733d08608cb7280f1b2a0244bfd3508bb9a1bf11e6a24395b400961334d74