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Paper Citation Record · LEDGER

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

As of 9 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 2026-08-08T06:32:00.761636+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=sha256:484e786515d5aa327cb670a994c15015dcc136d8fe8f6b3233682e275b4d2a54

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:e3881cd383cb6c636c9ef8e280d478ee10adbd8bb998f4f2069602134a4fb1a7

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:bdea586816d5ef9b359bfd11b6d06984ed4c57ac490521e1594fd78731677e9d

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=sha256:6c2d36f96d788dbb3da0b84aba864634ff3379877de74b54255d7eab298372a1

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=sha256:7474ae99076215648ad0aeddb9dcad86f62366ed40694f8d89a1db4df37dd464

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.045369Z digest=sha256:fbf82a780c8fd5573bd3dc2dc60f3c2000a1b58f65e285a970d5f55d4e338dbb

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=sha256:1d756fbcac15f05cb0e67a6b552acbff97b95dcf99e51654c195ce9ab79b01ad

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=sha256:465e3f5e0597bafc8268da32e26553d095fe332c542a3712f7e547dcfbfe685f

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.054541Z digest=sha256:e50069bcb6742f925b9438bd71e34110a2eb81f1ebf3e48eb20552485bb1f5b0

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=sha256:81a7a44aefdbc9838d05c4f3c4ed3f0a71bfe0f84c45f2d0fba24b320c6a6088

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.060286Z digest=sha256:b0a56980f47d29b7a40861746e5d8aae81fa586d2660df58f92364e82b5a0555

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=sha256:893b380d7f8524948b11d5c0fb4661075de5f7127490191aed78a156d830981b

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:d7d61d4914650cff3ebc444f3bbf3330993b4f2f33cc0f6492d1b4ccf0ab8263

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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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.068267Z digest=sha256:67a79498e23252d2eede737f82345ec431aed1646264b42d073a7d5c162da906

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=sha256:355cf28064c6b4ee4193240e11d2adbafd81ed3529687ea821502bd48e991e12

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:cf0ce6bc04d54a02d95b406f26d0f33a2fb6bbd0f1f61a455a4ceaacd26d449a

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:ad02aca86eefb6c66d4620a8a50873f4063a192ab559070d3fcb1e088b6545d6

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.079075Z digest=sha256:cf4b593e21e4e3c14096863d6570ef1f677cea22de02729b94a4634fc9d21022

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:edbea6ae9fb601de748150a1984dbc04b6ff64e1aedf331eb1f1c1fd1245b90e

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.084086Z digest=sha256:070aca459d10eed1f3704457481e37c82b8f08324c9f6005f2f5c3982d9db7b7

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:b39d9c5c7284d64558bce1b1014c24fac628122ec0c015779a81b12ccfcb33c7

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.088929Z digest=sha256:17567b0e541874e73dc998828dc2c89497fe53e2e79f3f26e6a874f149387739

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:f94d8be15cdb7e97002f15e43af224e3ff723e80e414e3562cb5562a3d0aab20

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.094480Z digest=sha256:bd2fbbe0ff2c4db18e23062f60425ef266e8abaa2f96e5742f2858adb072c6b1

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=sha256:2a6f0e7dbe725bccc5c607991e88f132ad6a7a0877f7c36799b75cdffffde201

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=sha256:1c96b93494643c7ed7f85d9b9a8f231a9c34e331dcd88b2852c0acc65206c2a4

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:dde46c7cc22066bcd233dcabb7592b31259bd2101450dacfaee36624f5335523

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=sha256:9fce83ba9f306578ae0bfd52370078a992bb5a89ff26ae504177deea51b4ddff

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=sha256:7a5d0f0a51da9cb7eb7e07811f76ed19aa20b49470450934b58544183cf8c2b1

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.111227Z digest=sha256:712a1c97fb53d505ba313ebc7a68e004c31bfed187c8bde5adebe589f75054f7

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.113835Z digest=sha256:60f48e4c9a4c88ce417837605dc797fa6d3eaa6a11e5cabbd8857e0237913d37

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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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.116949Z digest=sha256:df57e3e834f6d278e9a1ada54e16efd490869879dc8a5709c2adbd88ee7b8ec4

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.119585Z digest=sha256:376cd86f92604c6b2ff820ee9d35a0973aac6708442a01ef4384229af5cd0d77

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=sha256:0f4553bf9d1a9abe99f55c6dff362aba90a0d65334512b97e634fe0ecde9901e

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=pdf_text observed=2026-08-06T20:46:25.125226Z digest=sha256:1e21b213b9bedb6034549eacc4d914ed9ec14a81dc4e28ab792243589877b20a

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.128283Z digest=sha256:72f25710d580e9367a367a3316e5d6e1f95b32b60b989b742a87962970f31bc6

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=pdf_text observed=2026-08-06T20:46:25.131067Z digest=sha256:6bcc8994d33e38f3988f2a14203ea9c3cf29c5aaafd62caed384760d9a90c433

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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source=pdf_text observed=2026-08-06T20:46:25.134133Z digest=sha256:33068aacd47516f9acacd1506e64a2fa4e6e119df51892fde02f20d349045210

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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source=pdf_text observed=2026-08-06T20:46:25.137294Z digest=sha256:35b82edad13f813d5f94422bd73c6726bc781f4ca3d215d64078b65149b03e86

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.139909Z digest=sha256:787a7ce0525c5d997210bd9e6abf18703d6f7cb49a1b553646340a6cf91a2c1d

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:f841109522fe0711fa336b43a2efbc1bcabe0e0d209a972c65953f0e704194be

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:e94918d1f0e243bcc3e8d9aeb3bba1bd76b7fbb9041f22191aafb2eae78181c2

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=pdf_text observed=2026-08-06T20:46:25.148076Z digest=sha256:457686f0d9f54988c59cb6722c339a78e54fd74445b55da88428eb3d57e2e7e0

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=pdf_text observed=2026-08-06T20:46:25.150779Z digest=sha256:69d67a02d63218aba98604e5c3eaa314b6017e417966f8d655d7da1e581a9954

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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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.153481Z digest=sha256:22a450bba4ce987d57abe37ab95456dfbc997fe0ff1b8d2b697293bef917375a

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=pdf_text observed=2026-08-06T20:46:25.155847Z digest=sha256:e5e85076c5400601b9cda60772ac6401ff800f9018840e7b2ac8e1c3bd631e60

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=pdf_text observed=2026-08-06T20:46:25.158437Z digest=sha256:077eb0ea2fe5cb4c06ed581847ed634c1f32723cd0e4fed249a80e253a4d751f

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:cc4358f9db7e115cfa038a47dc2b98c063eaca862fbd4665d58ae9be46351e67

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:ebd6ee21a08b22f81ed5b61dc7a28b4be8e8de656d55ddd4e28ad1718a978821

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:bc80c62a720743707aa7a7cba970da5a9fd1883e5c68ed5b734f218637e79a4e

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:d0575a9c11ea045fabfcf0aeac93e5cfae525bf4ba0e2f5fb68faaff820a0922

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=sha256:32ce52ba07ee6005647162c78af349b5e74b53f1756b307519a51a515b6420d9

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.173889Z digest=sha256:2c088694d241667e4a9d5ed6ae0ef65eb90c158eba93bff473351785bbae1bbf

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

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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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:46:25.176398Z digest=sha256:52c41ec3adf91ddbcc96ff885353d00787e7e0c9f97fee8d3b6ef91e9a7151b0

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=sha256:428c954832152d4f62d918b788065b127fc852fa46c2902e87e8f3b5d3226198

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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source=pdf_text observed=2026-08-06T20:46:25.181575Z digest=sha256:6c0ae40ba450728463957810aea73cbb91608fd357da516f5219d70291735c26

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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source=pdf_text observed=2026-08-06T20:46:25.184165Z digest=sha256:e63da21b04cf947df501631d49f45dc0bc60f3ddd43f7011d48e0ab231be29dd

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T10:47:33.349484Z digest=sha256:40d7bcf07f0331390b3e74ca303ab0626bfaf99f78eba9f8886cefd9337a8832