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Paper Citation Record · LEDGER

One Diffusion to Generate Them All

As of 17 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 9 inbound Pith citation observations for arXiv:2411.16318.

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

pith.paper-citation-record.v1
2411.16318 v2

Coverage vector

measured 83 of 83 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:19:54.961392Z

measured 92 of 92 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:43:56.173090Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T22:21:53.151455Z

Reference resolution

83 of 83 outbound references displayed

  • verified exact0
  • verified fuzzy28
  • unresolved55
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cf8494b5-cded-4037-aa02-3614879faf3f · outbound

This paper cites GPT-4 Technical Report.

One Diffusion to Generate Them All GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.464824Z digest=sha256:eee8ae9dae316fae0ce4f2ff92778b57512cc63532ffe8aaa57066b60b5c3688

Observation c78b3267-a615-449f-ac22-44cde4c9f572 · outbound

This paper cites Building Normalizing Flows with Stochastic Interpolants.

One Diffusion to Generate Them All Building Normalizing Flows with Stochastic Interpolants

Reference 2

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.471603Z digest=sha256:8a107c96459a85370830d18d04ace53a33375b9d0a5dda16e728be93c65d8c69

Observation 9a677541-6406-4f6f-8e2a-46f72623c04c · outbound

This paper cites Imagen 3.

One Diffusion to Generate Them All Imagen 3

Reference 3

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.478202Z digest=sha256:068b77d345609e263704ce711dd41be5c54d84e1e649df36e685c5731a22b913

Observation a1070b78-6b0b-4402-ace0-9fc249262780 · outbound

This paper cites Improving image generation with better captions.

One Diffusion to Generate Them All Improving image generation with better captions

Reference 4

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.484215Z digest=sha256:cb0fd68b9cc7097c30de10bf4c80ee2cb9965226ffabadec705d1354d540f1bb

Observation 89e84cd2-9528-4dc8-af5e-08080793882c · outbound

This paper cites In- structpix2pix: Learning to follow image editing instructions.

One Diffusion to Generate Them All In- structpix2pix: Learning to follow image editing instructions

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.490332Z digest=sha256:f9ae0e6715fbf508ac005e41eeeffa630b3cc47af4f4b889b336e9cae239be3f

Observation 5351d285-6e57-4c6a-80a2-8e2ef36fb1ea · outbound

This paper cites Coyo-700m: Image-text pair dataset.

One Diffusion to Generate Them All Coyo-700m: Image-text pair dataset

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.495401Z digest=sha256:26f57decbbae48ea2b99188be81375afe96132940fcc0b281f4cf932c30780bd

Observation eb4ecfcd-6090-4fa3-9716-671400c73941 · outbound

This paper cites Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion.

One Diffusion to Generate Them All Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.500397Z digest=sha256:535eeed3c06440400b90d36dd1ca64c4993eefa3fde73e8d1389781be5ab53fb

Observation f4156981-2d3c-4e72-8968-4512a3dc0a77 · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

One Diffusion to Generate Them All PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.506221Z digest=sha256:7a8b7bb6c38e94724ab0387ed8fc4b209585280a7bd6084cebdbd35035684781

Observation 6f860720-154b-4264-8d97-d5ad29d99229 · outbound

This paper cites PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation.

One Diffusion to Generate Them All PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Reference 9

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no resolver link, observed 2026-08-12T13:19:54.512025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.512025Z digest=sha256:678391ceb653d1e9f27fef304b45b1dbcfeda969afbf5501b91536d3d5f5fc7a

Observation ef563955-b7a7-48e8-b2e2-c0e982937f0d · outbound

This paper cites Objaverse: A universe of annotated 3d objects.

One Diffusion to Generate Them All Objaverse: A universe of annotated 3d objects

Reference 10

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.518378Z digest=sha256:816a0fa5b3362a2b7a81f23c0ad5afa7ef9f68f9c678a66f3b028c35523beaae

Observation b5f20538-2d1f-4d84-ab9a-e10d6b4971f4 · outbound

This paper cites Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models.

One Diffusion to Generate Them All Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Reference 11

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source=pdf_text observed=2026-08-12T13:19:54.525078Z digest=sha256:5f2a6afb66c5e25e6549f7949e2d273662775f067397d3de14c0df3e618c40b6

Observation 68a2851b-5c0a-40c9-bf67-50f77cfc4714 · outbound

This paper cites Objaverse-xl: A universe of 10m+ 3d objects.

One Diffusion to Generate Them All Objaverse-xl: A universe of 10m+ 3d objects

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.530539Z digest=sha256:e2a8fc424faafe6ab4bdaa45ccc41330b1abad1c5ada38e77596b7dfac67358b

Observation f30a31e0-1964-4894-a54c-3eafd2d904ae · outbound

This paper cites Google scanned objects: A high- quality dataset of 3d scanned household items.

One Diffusion to Generate Them All Google scanned objects: A high- quality dataset of 3d scanned household items

Reference 13

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no resolver link, observed 2026-08-12T13:19:54.536342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.536342Z digest=sha256:4d151f4f765b5538c4a0d0b78d65e3973377d71eef2ebc6f5b80e156ffa5a291

Observation 4ba4e6d3-c870-4bcd-81fc-c07a281b1a51 · outbound

This paper cites Omnidata: A scalable pipeline for making multi- task mid-level vision datasets from 3d scans.

One Diffusion to Generate Them All Omnidata: A scalable pipeline for making multi- task mid-level vision datasets from 3d scans

Reference 14

Resolution
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raw_fallback, observed 2026-08-12T13:19:56.534691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.541736Z digest=sha256:4c635ac9e30e34c32142e9c3295ce668b8a5abd796fc70caf6e6350b017a1177

Observation 200a8bd4-105b-423e-bf4b-6358eb96a70a · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

One Diffusion to Generate Them All Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 15

Resolution
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raw_fallback, observed 2026-08-12T13:19:56.516133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.548081Z digest=sha256:35e52dfeb29bb68b64664c6e2fcba8cf7c224d93cd44a91e5fdf661da79d0f7b

Observation ed962154-b03f-4e43-84bd-628ba6abc8bf · outbound

This paper cites LCM-Lookahead for Encoder-based Text-to-Image Personalization.

One Diffusion to Generate Them All LCM-Lookahead for Encoder-based Text-to-Image Personalization

Reference 16

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source=pdf_text observed=2026-08-12T13:19:54.553802Z digest=sha256:f4e39a1bb74fea69047e1980087479536a85496ea1828440613f4c379225e93c

Observation 659d5230-96c4-43ac-9e12-0ee87e688d04 · outbound

This paper cites InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists.

One Diffusion to Generate Them All InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists

Reference 17

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.560167Z digest=sha256:62983bab6ae3669636ba6729224bb0a5099f2ab9293a5e5caa29424446c7930a

Observation 0e93154a-2232-4f12-9c43-74f68711bb6d · outbound

This paper cites CAT3D: Create Anything in 3D with Multi-View Diffusion Models.

One Diffusion to Generate Them All CAT3D: Create Anything in 3D with Multi-View Diffusion Models

Reference 18

Resolution
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no resolver link, observed 2026-08-12T13:19:54.565511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.565511Z digest=sha256:3efa07c5c6ad88c020587911bcdc4ed5a512af2e5bcd84a51c8f85edabc65f83

Observation 1d6e1dc2-38dc-4ce2-9baa-df29b403fc09 · outbound

This paper cites Instructdiffusion: A generalist modeling inter- face for vision tasks.

One Diffusion to Generate Them All Instructdiffusion: A generalist modeling inter- face for vision tasks

Reference 19

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no resolver link, observed 2026-08-12T13:19:54.571563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.571563Z digest=sha256:651622d2da40ebca3792f13e3aeb201bf9d0b238e97d2288f725a9ba99a2f174

Observation ccc04734-5824-4655-ac80-9125f7b67794 · outbound

This paper cites Geneval: An object-focused framework for evaluating text- to-image alignment.

One Diffusion to Generate Them All Geneval: An object-focused framework for evaluating text- to-image alignment

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:56.480486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.576811Z digest=sha256:5682b6cfd26f95a04adf54f9bf4b073eb0f8164f4d38c838f0fc3a349efaba10

Observation 035fb181-b18d-48e0-8261-bb794ce0eaff · outbound

This paper cites PuLID: Pure and Lightning ID Customization via Contrastive Alignment.

One Diffusion to Generate Them All PuLID: Pure and Lightning ID Customization via Contrastive Alignment

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.582094Z digest=sha256:88173e2d4e9c91342c01e38a95c1088faeb1957894d9a924601a1e37093df82e

Observation bc68be48-973a-4e5d-995a-0b26a12a2f32 · outbound

This paper cites Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code.

One Diffusion to Generate Them All Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.588226Z digest=sha256:772897e4e482c65e5346ccfc35535f5152cfad1a9d06b7be6c11a54238e5997d

Observation b14f1e57-a0f4-4341-a25a-828adaeef7c4 · outbound

This paper cites Repurpos- ing diffusion-based image generators for monocular depth estimation.

One Diffusion to Generate Them All Repurpos- ing diffusion-based image generators for monocular depth estimation

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:56.460565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.594395Z digest=sha256:d8069a982497811ebfe7554d9ff3c6ffb9ea5deaddffcb95537408ef139d6c75

Observation 7991dbbb-ea6c-4fcb-ba76-959f94f275af · outbound

This paper cites Segment any- thing.

One Diffusion to Generate Them All Segment any- thing

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.600770Z digest=sha256:0301c2dcaa134dccf43c00ff24e95edceb4b70340a5381c70d2d7f8543fe118d

Observation c18a1442-6ba1-44d3-be96-07b2525f1f53 · outbound

This paper cites Eschernet: A genera- tive model for scalable view synthesis.

One Diffusion to Generate Them All Eschernet: A genera- tive model for scalable view synthesis

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:56.427820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.606842Z digest=sha256:1c5cbc6c4f35b55a00fac8f29b15f3d5d5a650424e75d299f8cd7d472c5e0c49

Observation 16bb9fb8-deaf-4385-969c-0803efa35d55 · outbound

This paper cites Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation.

One Diffusion to Generate Them All Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation

Reference 26

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.614498Z digest=sha256:3ba4acfa21ff3c50fc230951b6815323678cbffbf357802d75def927ba66e778

Observation dc1ead36-96c7-412e-9788-1ade260ea938 · outbound

This paper cites Open-vocabulary object segmenta- tion with diffusion models.

One Diffusion to Generate Them All Open-vocabulary object segmenta- tion with diffusion models

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:56.409589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.621028Z digest=sha256:f0cb56e88b4d891331ac7e298b166eaab0af8bf72d299f05c265b032de789abf

Observation eb788920-837a-4d3b-a691-fa6e1c824e65 · outbound

This paper cites Photomaker: Customizing re- alistic human photos via stacked id embedding.

One Diffusion to Generate Them All Photomaker: Customizing re- alistic human photos via stacked id embedding

Reference 28

Resolution
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no resolver link, observed 2026-08-12T13:19:54.626781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.626781Z digest=sha256:d8a8806b89ac0ee7b86d231ba34ca34d0d88cdef14fa7396cc0c8023050f5f5d

Observation 3153ac62-9931-449c-a407-586072d518d3 · outbound

This paper cites Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.

One Diffusion to Generate Them All Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-12T13:19:54.633105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.633105Z digest=sha256:e5d653246a784092bf5400c5d7f45c201c51c44e0d1ae10641ee034e93c4586b

Observation a6cdc6e0-a30b-4a07-94da-74ccb1bda603 · outbound

This paper cites Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision.

One Diffusion to Generate Them All Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:56.379836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.639401Z digest=sha256:bc22ed5faa5211460aea781c72904988992bbc0f6c42bebb43ff5093e6570f5a

Observation 0952a6e0-d818-4f1e-9dc6-4cc99cbea25f · outbound

This paper cites an unresolved cited work.

One Diffusion to Generate Them All Unresolved cited work

Reference 31

Resolution
unresolved
raw_fallback, observed 2026-08-12T13:19:56.358263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.645399Z digest=sha256:748d67d906591a1b725f17eecd45541354314554030d164eaca1cc6311557ba9

Observation f63effee-1b34-44c7-becb-1fa8fb2fdb0b · outbound

This paper cites Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024.

One Diffusion to Generate Them All Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

Reference 32

Resolution
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no resolver link, observed 2026-08-12T13:19:54.651416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.651416Z digest=sha256:c880b6285ffb06477e91b0843184e1212da219819bf84fe97f94ad45e51c0079

Observation ad872d12-5b99-420b-a569-0ca807de4fe9 · outbound

This paper cites Zero-1-to- 3: Zero-shot one image to 3d object.

One Diffusion to Generate Them All Zero-1-to- 3: Zero-shot one image to 3d object

Reference 33

Resolution
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no resolver link, observed 2026-08-12T13:19:54.657347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.657347Z digest=sha256:6439b8ed2410d9d166b615f6e07e813c9222861e73a8802ab7acccf8ca1b0179

Observation d000bf70-6c77-4a06-add6-7361967f59bf · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

One Diffusion to Generate Them All Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 34

Resolution
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no resolver link, observed 2026-08-12T13:19:54.664281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.664281Z digest=sha256:fccd7915980240cb6495b56652dc71221b6e2329b6c7ab5e98039627d5419835

Observation 94c7c278-0752-4a83-b16b-c01f32c51681 · outbound

This paper cites SyncDreamer: Generating Multiview-consistent Images from a Single-view Image.

One Diffusion to Generate Them All SyncDreamer: Generating Multiview-consistent Images from a Single-view Image

Reference 35

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no resolver link, observed 2026-08-12T13:19:54.669462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.669462Z digest=sha256:e3566c751dc42df1635e1a4c2aaf2ee7c0d40fa07d8a03d5f3b324e20232fa01

Observation 8cbd9749-f917-4874-9daa-cc3af7444c4d · outbound

This paper cites Unified-io: A unified model for vision, language, and multi-modal tasks.

One Diffusion to Generate Them All Unified-io: A unified model for vision, language, and multi-modal tasks

Reference 36

Resolution
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no resolver link, observed 2026-08-12T13:19:54.674592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.674592Z digest=sha256:1eaef6f17ce3d270ea34584a747b930a3551d45d80611a5ab14cd37cefc8ec7e

Observation cf89c0f2-b74a-44b5-9376-208df931765b · outbound

This paper cites Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action.

One Diffusion to Generate Them All Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action

Reference 37

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no resolver link, observed 2026-08-12T13:19:54.679704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.679704Z digest=sha256:c06ce86e112b951ff77231acf9d5f8eeb9e4253c2d10bb1cd67f726b6d25e11f

Observation e37625ab-dc20-4af7-be5d-6afb4d5ce337 · outbound

This paper cites Flowdiffuser: Advancing optical flow estimation with diffusion models.

One Diffusion to Generate Them All Flowdiffuser: Advancing optical flow estimation with diffusion models

Reference 38

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.685564Z digest=sha256:62af71992c380ee80740562344615ecb212a6b9960cbfd96ae9d7991aae8d38f

Observation e6e07c77-7f8e-45f4-8038-2ba36a239db6 · outbound

This paper cites Scalable 3d captioning with pretrained models.

One Diffusion to Generate Them All Scalable 3d captioning with pretrained models

Reference 39

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.691022Z digest=sha256:099a2acfba188c748295438348f30b2c42f17118f19c040374cf3e2123918793

Observation 083f117a-e73a-4694-aba3-65cf6340e760 · outbound

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

One Diffusion to Generate Them All T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

Reference 40

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T13:19:54.696049Z digest=sha256:001d47d0f17b14932c96d51915ab9e5620f1cece4362a936b44492d135059ead

Observation a48e3177-2124-4ea1-9be0-d58b994eae0b · outbound

This paper cites Jour- neydb: A benchmark for generative image understanding,.

One Diffusion to Generate Them All Jour- neydb: A benchmark for generative image understanding,

Reference 41

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source=pdf_text observed=2026-08-12T13:19:54.701524Z digest=sha256:db77577dee25eb87b0d773417cdb539819249f3963331af349807a45158bc5dd

Observation 89957fab-3f6f-4a47-a73f-acf5abd81fc2 · outbound

This paper cites Fast samplers for inverse problems in iterative refinement mod- els.

One Diffusion to Generate Them All Fast samplers for inverse problems in iterative refinement mod- els

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-12T13:19:56.239461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.707407Z digest=sha256:1ffdc707d1afd6cf24ad6ce679744fc35f37f837a9ed7d3e32df85c854bccf41

Observation 354f7868-1ccc-4855-b3d4-68885ce15077 · outbound

This paper cites Variational Control for Guidance in Diffusion Models.

One Diffusion to Generate Them All Variational Control for Guidance in Diffusion Models

Reference 43

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.712775Z digest=sha256:d669604909ad43dc95dba1505ffc28a573613a947a317f4a5e178471a9079767

Observation 9c6e6570-d29d-4737-bd7b-ca76fd115953 · outbound

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

One Diffusion to Generate Them All SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 44

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no resolver link, observed 2026-08-12T13:19:54.718195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.718195Z digest=sha256:13911faa560fea53c8b548323a7443a915406ffc2f1b73b1973e6582507022d2

Observation 4a7afa06-5219-4bc4-bdc1-ca744bd4fea0 · outbound

This paper cites UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild.

One Diffusion to Generate Them All UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild

Reference 45

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no resolver link, observed 2026-08-12T13:19:54.723805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.723805Z digest=sha256:184919b813e5fc0048669e25aeb9e17296021e28523f1733b25568a1b50a6016

Observation 86ee48b3-5f91-40d5-954c-5c0422e9ba78 · outbound

This paper cites Zero-shot text-to-image generation.

One Diffusion to Generate Them All Zero-shot text-to-image generation

Reference 46

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.729741Z digest=sha256:0cee4f28147be50c34bc36e1884553150020c45dbcfd26064019dfd62e71b079

Observation 8211186b-19ee-4890-98fc-5afff61d1d96 · outbound

This paper cites Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer.

One Diffusion to Generate Them All Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer

Reference 47

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.735741Z digest=sha256:eef234e57b596ebb769e42a5535cf5dc776967757828115bc269e72412dbe1f1

Observation 86a011e2-326a-4f94-bf42-ddfbef541e7d · outbound

This paper cites Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction.

One Diffusion to Generate Them All Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:56.196934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.740901Z digest=sha256:39f2e503c0eccce05d7a7eecaf90c283fea6f90142016c0a921f423dd3d364b3

Observation ebdd6d3f-7fb5-454b-9e6f-50dffd0350cf · outbound

This paper cites Hypersim: A photorealistic syn- thetic dataset for holistic indoor scene understanding.

One Diffusion to Generate Them All Hypersim: A photorealistic syn- thetic dataset for holistic indoor scene understanding

Reference 49

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.746781Z digest=sha256:70ec51305e302247dec91c18f99735efdf1e67bf33a7d0056a0aa171be5d301f

Observation 8bfe6dca-79b5-44d9-9a85-504fc09e42d3 · outbound

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

One Diffusion to Generate Them All High-resolution image synthesis with latent diffusion models

Reference 50

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.753381Z digest=sha256:ec6202455b34f841fef827e5b02699a342a7d05c56639e8cd5b1873bdfe7d008

Observation 68d3a600-5ead-46ee-afd5-1c05f50087e9 · outbound

This paper cites Rolling Diffusion Models.

One Diffusion to Generate Them All Rolling Diffusion Models

Reference 51

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.760565Z digest=sha256:86bdfc24aff21f088ca58497be34842c12ab000e2d8c4ba2563cd0166e07d9b0

Observation f822a7de-d104-46cf-af43-9a2a483ce222 · outbound

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

One Diffusion to Generate Them All Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 52

Resolution
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no resolver link, observed 2026-08-12T13:19:54.766596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.766596Z digest=sha256:95d00bd17a90a783d2e3ba5599f32d8d799909939236a45d6e6c6f9f63a99c69

Observation 3e3365e1-5652-41ca-bb8b-00754dc418a8 · outbound

This paper cites The surprising effectiveness of diffusion models for optical flow and monocular depth estimation.

One Diffusion to Generate Them All The surprising effectiveness of diffusion models for optical flow and monocular depth estimation

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:56.137986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.772789Z digest=sha256:306ab14bf07ff4e4c295dd00f7c8f2c67b662bdfa8431dc8e1699146c848add1

Observation ba0a5187-c7ae-4d9d-b01b-02c99ab10529 · outbound

This paper cites MVDream: Multi-view Diffusion for 3D Generation.

One Diffusion to Generate Them All MVDream: Multi-view Diffusion for 3D Generation

Reference 54

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no resolver link, observed 2026-08-12T13:19:54.778439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.778439Z digest=sha256:6acf6ada097bbdbac538c55ef98315a57e7c6f97b43d16cba14cb7f6235e2397

Observation cb3516d3-e46d-43aa-b7eb-8c973fb02760 · outbound

This paper cites Indoor segmentation and support inference from rgbd images.

One Diffusion to Generate Them All Indoor segmentation and support inference from rgbd images

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:56.120358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.783846Z digest=sha256:fc2b1845834f93b58c6f7e1f0f48e2f4b9b6664fa2b363f00b3b3e43f3987859

Observation cfb7ee43-0e18-45e6-abc1-725456d3d18b · outbound

This paper cites From Pixels to Prose: A Large Dataset of Dense Image Captions.

One Diffusion to Generate Them All From Pixels to Prose: A Large Dataset of Dense Image Captions

Reference 56

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.788830Z digest=sha256:8fb22c9100fdc9273a600f5e9e56300486c043baa9efc524e8a6f58199902945

Observation cb59da87-ed01-4766-9dd5-f899a0f7d4fd · outbound

This paper cites Pseudoinverse-guided diffusion models for inverse problems.

One Diffusion to Generate Them All Pseudoinverse-guided diffusion models for inverse problems

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:56.103384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.794468Z digest=sha256:5a65f7a79dddd497e7819c9db7baef18a6d2051f2b29113a02564e5a196ea459

Observation 70a9bad7-1965-454c-b71d-d18a89d28acc · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

One Diffusion to Generate Them All Roformer: Enhanced transformer with rotary position embedding

Reference 58

Resolution
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no resolver link, observed 2026-08-12T13:19:54.799823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.799823Z digest=sha256:56c2d289da37743f3f2860717cadef212d3829824266865394081683595addf7

Observation 15a9d392-e6e7-4f2a-9e01-1714cfcb8dc9 · outbound

This paper cites LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation.

One Diffusion to Generate Them All LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation

Reference 59

Resolution
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no resolver link, observed 2026-08-12T13:19:54.806198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.806198Z digest=sha256:fde290b44a60608a22e955ce7e854202f5a0de956cd4a2c0c1e2d25bce0507d8

Observation 103e7e05-9a00-4fd9-b2a8-e9aedf6508f3 · outbound

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

One Diffusion to Generate Them All Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 60

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no resolver link, observed 2026-08-12T13:19:54.815311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.815311Z digest=sha256:b628339debb33d745a5377187a4115b9a0d0d41893abd8962e329c46add1735b

Observation 3ac51171-17d1-4830-95d3-0cc5fc835117 · outbound

This paper cites DIODE: A Dense Indoor and Outdoor DEpth Dataset.

One Diffusion to Generate Them All DIODE: A Dense Indoor and Outdoor DEpth Dataset

Reference 61

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no resolver link, observed 2026-08-12T13:19:54.820653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.820653Z digest=sha256:f77bbf3278b1e4397f31e7ce3d1ec01e7b601f76ff3efb690e48216d537cd9b0

Observation ca79ffef-8727-455d-9c76-31528f77a551 · outbound

This paper cites ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation.

One Diffusion to Generate Them All ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation

Reference 62

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T13:19:54.828581Z digest=sha256:c87b6a3e835ac4db932c14165e99aea1f4d32ed3c83346869e99f0f36baccbe1

Observation 85b626cc-99e5-4b15-9fe1-5994a663f2f1 · outbound

This paper cites InstantID: Zero-shot Identity-Preserving Generation in Seconds.

One Diffusion to Generate Them All InstantID: Zero-shot Identity-Preserving Generation in Seconds

Reference 63

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no resolver link, observed 2026-08-12T13:19:54.834417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.834417Z digest=sha256:7c99944f568efa4ff2ac2c327eda9202ee719674d78b895dd1c676ffef20eae5

Observation f87b029b-ac1c-4f13-90b0-744c43dc3c02 · outbound

This paper cites OmniGen: Unified Image Generation.

One Diffusion to Generate Them All OmniGen: Unified Image Generation

Reference 64

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source=pdf_text observed=2026-08-12T13:19:54.840129Z digest=sha256:2d50e36545c0affe2e647e2a510609af69b25524f620757af51853b5f034321b

Observation c9324b61-54f8-4a81-b5c8-2aa2f7d474e1 · outbound

This paper cites Versatile diffusion: Text, images and variations all in one diffusion model.

One Diffusion to Generate Them All Versatile diffusion: Text, images and variations all in one diffusion model

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:56.073661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.848210Z digest=sha256:fa5f91974acc2af3a164c1835d95183acb66106a7213d0c5364280f982f97b45

Observation 47d1f842-7ab3-4ca4-b92c-768cd800df5b · outbound

This paper cites Vit- pose: Simple vision transformer baselines for human pose estimation.

One Diffusion to Generate Them All Vit- pose: Simple vision transformer baselines for human pose estimation

Reference 66

Resolution
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raw_fallback, observed 2026-08-12T13:19:56.052184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.859600Z digest=sha256:af8bda8135b450b009f437b3e60444cdcb49927a22ef7bda493309f9d5540167

Observation cfe922f1-3aa7-4a71-ad87-35fcfc150a82 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

One Diffusion to Generate Them All Depth anything: Unleashing the power of large-scale unlabeled data

Reference 67

Resolution
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raw_fallback, observed 2026-08-12T13:19:56.027775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.865435Z digest=sha256:d655e56fa0da1f9a96572ad37df91f607520e4c1dd23aa153495c23590893a1c

Observation 27389c3f-e917-4d06-9459-9c745c18ce3a · outbound

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

One Diffusion to Generate Them All IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 68

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.871697Z digest=sha256:7aeae5b39a83abb8f0f00ade50b9082572d1a51e6efcb20e06885baeb1148fb3

Observation 11b94d22-6dc2-4fa3-a731-397ff86af781 · outbound

This paper cites DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data.

One Diffusion to Generate Them All DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data

Reference 69

Resolution
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no resolver link, observed 2026-08-12T13:19:54.878392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.878392Z digest=sha256:8b3e57e48bed375a22fd01c4f430830add8af2047a205c45cefc5c554a1ab411

Observation 0c0bdac3-cd4a-455b-a959-61fcf4a5752c · outbound

This paper cites Learning to recover 3d scene shape from a single image.

One Diffusion to Generate Them All Learning to recover 3d scene shape from a single image

Reference 70

Resolution
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raw_fallback, observed 2026-08-12T13:19:56.008811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.884429Z digest=sha256:f3c88bb983bb524959d0d28bf25bb2cb35fede817d6266d420f9e508bd5c5c3f

Observation 0f6f1f4e-f2c3-4ed2-93a3-dc932a65d79d · outbound

This paper cites Hierarchical normalization for robust monocular depth estimation.

One Diffusion to Generate Them All Hierarchical normalization for robust monocular depth estimation

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:55.991689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.890123Z digest=sha256:cb9b3a06de3e322d1a0d9417e32e85b0816be01620085aa6810a13a318962466

Observation adf79828-c8c2-4c9b-ad1d-96d457cca600 · outbound

This paper cites Cameras as Rays: Pose Estimation via Ray Diffusion.

One Diffusion to Generate Them All Cameras as Rays: Pose Estimation via Ray Diffusion

Reference 72

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.897435Z digest=sha256:541d9c0e1ab1e5d6023bcf25ee6ae8e073627ec96b8be9df86cbf7dc74c5ebcf

Observation 6cdaf5f5-a6d3-455d-bc7a-8aa7b073610f · outbound

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

One Diffusion to Generate Them All Adding conditional control to text-to-image diffusion models

Reference 73

Resolution
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no resolver link, observed 2026-08-12T13:19:54.903676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.903676Z digest=sha256:8390bf2e12e222919b671fa2221aa9d026dedf01c722e8ba167f22e46e6116c2

Observation fe72a61d-9e48-440b-88b2-f6e4e1c0ca65 · outbound

This paper cites Tedi: Temporally-entangled diffusion for long-term motion synthesis.

One Diffusion to Generate Them All Tedi: Temporally-entangled diffusion for long-term motion synthesis

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:55.958167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.909040Z digest=sha256:a444e2aa8249b3e7fabc45ef7fb15d5733341c3dd2be08c72545d8a4c69ad9ad

Observation 69e4b1f3-2bbc-4dd7-8f82-9172dc42f7bd · outbound

This paper cites Uni-controlnet: All-in-one control to text-to-image diffusion models.(may 2023), 2023.

One Diffusion to Generate Them All Uni-controlnet: All-in-one control to text-to-image diffusion models.(may 2023), 2023

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:55.939490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.914932Z digest=sha256:0efb23edab6d893088f0cf18adbd754432e569aa74169234a242ad0165d77148

Observation 20b2877a-0f8f-4455-b9ba-b846907652e9 · outbound

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

One Diffusion to Generate Them All Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-12T13:19:54.920474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.920474Z digest=sha256:2b74b61bb1f70539c5fef81aecd2e02c9b43967090a162fd89a42b390c7ec1c3

Observation ca1945b4-4be0-4ab8-86ad-1042adc92d8a · outbound

This paper cites Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT.

One Diffusion to Generate Them All Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-12T13:19:54.927420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:19:54.927420Z digest=sha256:e17a8cb9d86889f81306bf7f1b04a6ac148fff0b0902de3d963f6909161c9d3b

Observation 931768a6-86e0-4346-b4aa-b718e7096368 · outbound

This paper cites Camera Pose Estimation We evaluate our model on camera pose estimation using the Google Scanned Object dataset [13].

One Diffusion to Generate Them All Camera Pose Estimation We evaluate our model on camera pose estimation using the Google Scanned Object dataset [13]

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:55.897527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.938221Z digest=sha256:3dfc005e293bce79bb778bbb405bb8ca9c93cd0dedc728f5e8e1f5fd80aa79dc

Observation 6ab52017-3977-48cd-90ee-73be9d78bb4d · outbound

This paper cites OneDiffusion achieves strong performance compared to specialized editing and generation approaches without any fine-tuning.

One Diffusion to Generate Them All OneDiffusion achieves strong performance compared to specialized editing and generation approaches without any fine-tuning

Reference 80

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verified fuzzy
raw_fallback, observed 2026-08-12T13:19:55.875847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.944128Z digest=sha256:dee42800ba2039a9eca8e376fa370884af9d81409ad5006850088e0f9add2278

Observation 19a58f5e-7c8c-4470-8b6f-8c3e1d50604f · outbound

This paper cites We report ours results for ID Cus- tomization tasks in Figure 13 and Figure 14.

One Diffusion to Generate Them All We report ours results for ID Cus- tomization tasks in Figure 13 and Figure 14

Reference 81

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verified fuzzy
raw_fallback, observed 2026-08-12T13:19:55.855764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.950162Z digest=sha256:b56e336cf0c08e1ca3125e5895b735c5eb57a1e086354f91930ae370f97c4dc0

Observation dd596bf3-7b9c-4f22-a570-1f971a1d21b9 · outbound

This paper cites Our model works best for camera trajectory covering front views of a scene.

One Diffusion to Generate Them All Our model works best for camera trajectory covering front views of a scene

Reference 82

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verified fuzzy
raw_fallback, observed 2026-08-12T13:19:55.833261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.956115Z digest=sha256:c2565c28bf7396981af9dc133568d07f6875f1f172a2a62e0b96b0d167a52860

Observation 5f013656-541b-42cc-bd41-38e9c9db9296 · outbound

This paper cites photorealistic, masterpiece, highly detail, score 9, score 8 up.

One Diffusion to Generate Them All photorealistic, masterpiece, highly detail, score 9, score 8 up

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:55.810460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.961392Z digest=sha256:a0ff1658796be325e1bdb9b781f082b3d2656e438531bf1ebb773dba01f55111

Observation c5191367-a7e6-4387-abc2-3aef9ded3f16 · outbound

This paper cites Qualitative comparison between RayDiffusion and OneDiffusion on GSO dataset.

One Diffusion to Generate Them All Qualitative comparison between RayDiffusion and OneDiffusion on GSO dataset

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:19:55.917538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T13:19:54.933073Z digest=sha256:f084d9b4e4466cd0d5eda90eea892fcb756c00b897ba4d72affeef485df918c9

Pith citing papers

Observation d77006c9-cbac-4950-a176-42e521c99d85 · inbound

On Fairness of Unified Multimodal Large Language Model for Image Generation cites this paper.

On Fairness of Unified Multimodal Large Language Model for Image Generation One Diffusion to Generate Them All

Reference 11

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unresolved
no resolver link, observed 2026-08-09T04:52:40.582682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T04:52:40.582682Z digest=sha256:bb6a98c209e96051cec8f4acc66d7b206873a9ea475da590c8a4e1dd8f86c0cc

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

MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing cites this paper.

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

Reference 22

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unresolved
no resolver link, observed 2026-08-16T00:43:56.173090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:43:56.173090Z digest=sha256:00b6a6f1fd8d038a61bba077b59dedd8955bdf505417e7c570e58c7b56563c70

Observation b1704c97-8bcd-42c0-9e37-2153ce3b547e · inbound

Jodi: Unification of Visual Generation and Understanding via Joint Modeling cites this paper.

Jodi: Unification of Visual Generation and Understanding via Joint Modeling One Diffusion to Generate Them All

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:22.360463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:25:22.360463Z digest=sha256:7c0211656501e5c6714310ad5f99b09f5b6ec2af88af7669bd250a950bf0e1c1

Observation 9a2c58bb-b6da-496b-bf73-853b683b270b · inbound

Image Editing As Programs with Diffusion Models cites this paper.

Image Editing As Programs with Diffusion Models One Diffusion to Generate Them All

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T10:51:38.418361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:51:38.418361Z digest=sha256:74db5df13f3a79ccbe032dae211d9778048fcf20795ad0e2dd9da43914d1ae26

Observation 89f517dd-a670-4015-9a23-80da41c04228 · inbound

UNIC: Unified In-Context Video Editing cites this paper.

UNIC: Unified In-Context Video Editing One Diffusion to Generate Them All

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T10:51:43.074915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:51:43.074915Z digest=sha256:a52e2d9102ea92cd5d8285cd663dcd65b4ffff46527db8f7bbcd64e01612a8fd

Observation 07338fca-a62e-42de-8057-95f16254f379 · inbound

Trade-offs in Image Generation: How Do Different Dimensions Interact? cites this paper.

Trade-offs in Image Generation: How Do Different Dimensions Interact? One Diffusion to Generate Them All

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-06T12:08:01.733314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:08:01.733314Z digest=sha256:caf83c9b62af8583e3e3a55de21c84783302317a6f8001fc4cef622a37b41d0a

Observation b0a78eb8-1112-46a7-a7d0-ca133b882bf4 · inbound

Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering cites this paper.

Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering One Diffusion to Generate Them All

Reference 34

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verified exact
arxiv_id, observed 2026-05-18T22:21:53.153936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-18T22:17:55.678629Z digest=sha256:f9e3684ef20d2304180163e567af96af48629c0478021672eef9869da9ebd167

Observation 0054500e-248f-4325-ace5-83f26cb0d941 · 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 One Diffusion to Generate Them All

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-15T15:20:09.026211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T15:16:59.801347Z digest=sha256:36586a2c91f571de6444cd0d84223064e723445afdbfc6f2d6d1c392cab29d13

Observation beeb3046-9a50-4235-a214-3e9dbd923c0f · inbound

UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors cites this paper.

UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors One Diffusion to Generate Them All

Reference 59

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metadata mismatch
arxiv_id, observed 2026-05-11T15:26:07.624681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-09T20:05:21.723724Z digest=sha256:bbb02a2cc016e047415aec4be2f4bf7d9ae1ce0c2fb4f3978d05aa0aaba6e826