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Paper Citation Record · LEDGER

Accelerating Video Diffusion Models via Distribution Matching

As of 13 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 3 inbound Pith citation observations for arXiv:2412.05899.

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

pith.paper-citation-record.v1
2412.05899 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:17:52.720444Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T09:28:00.597160Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T09:32:16.742169Z

Reference resolution

38 of 38 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved37
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

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Outbound references

Observation 00ba17f1-02ea-49b0-a750-75b230db7426 · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Accelerating Video Diffusion Models via Distribution Matching Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2

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source=pdf_text observed=2026-08-11T20:17:52.569364Z digest=sha256:51b7f4a9c8857db03b4f8386b1b1a63a11b1a57687a1da74f74da0441ed3c5ee

Observation 99b70d1f-08b6-4f48-9473-b1957323020d · outbound

This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

Accelerating Video Diffusion Models via Distribution Matching CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 6

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source=pdf_text observed=2026-08-11T20:17:52.587933Z digest=sha256:e348a42fda41a6516d23178d5040512da53de640979db3bf936b10bf9427305c

Observation 165f577a-0526-4bed-815e-c2b037285702 · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

Accelerating Video Diffusion Models via Distribution Matching Imagen Video: High Definition Video Generation with Diffusion Models

Reference 7

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source=pdf_text observed=2026-08-11T20:17:52.592293Z digest=sha256:ba84bfb05f27e1890eba685466fc9e82532decef5a19c37e110ca5a1f074fe74

Observation fd9a3ce0-92c2-4d66-8039-12a71439e3b2 · outbound

This paper cites Auto-Encoding Variational Bayes.

Accelerating Video Diffusion Models via Distribution Matching Auto-Encoding Variational Bayes

Reference 9

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source=pdf_text observed=2026-08-11T20:17:52.600892Z digest=sha256:57e173b1bbf4d6413381decd0612256b7ce59da6be8602f1bcf4ca25ddca19e0

Observation a1d2cba3-1372-4fc1-b384-2dfab8141452 · outbound

This paper cites DiffWave: A Versatile Diffusion Model for Audio Synthesis.

Accelerating Video Diffusion Models via Distribution Matching DiffWave: A Versatile Diffusion Model for Audio Synthesis

Reference 11

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source=pdf_text observed=2026-08-11T20:17:52.609046Z digest=sha256:d570afc55c4f8fcd8b52e8c702c5fb58fb1e2acdd58ad4355c4784bc27060222

Observation 9cc7045f-8204-4314-bff3-e01eeb5b52e5 · outbound

This paper cites T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback.

Accelerating Video Diffusion Models via Distribution Matching T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback

Reference 12

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source=pdf_text observed=2026-08-11T20:17:52.613147Z digest=sha256:9273ec74c1e136526951e876a3ce347dcca817ac3c63db861f02f6c46559fc93

Observation 1c2d17f6-f75d-4bfb-8c5e-97193f61a611 · outbound

This paper cites AnimateDiff-Lightning: Cross-Model Diffusion Distillation.

Accelerating Video Diffusion Models via Distribution Matching AnimateDiff-Lightning: Cross-Model Diffusion Distillation

Reference 13

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source=pdf_text observed=2026-08-11T20:17:52.616564Z digest=sha256:c4e174c07967f710393b948986d4b7a350593f135f75cf2352d392044827c594

Observation 67f420c4-124b-4da0-950f-6f991788a89c · outbound

This paper cites SDXL-Lightning: Progressive Adversarial Diffusion Distillation.

Accelerating Video Diffusion Models via Distribution Matching SDXL-Lightning: Progressive Adversarial Diffusion Distillation

Reference 14

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source=pdf_text observed=2026-08-11T20:17:52.619824Z digest=sha256:97c478d7db6f8757c1eea348e986efe08fae99b9d58034594c2e57769a3742a7

Observation abca3990-1a5e-4f94-b291-b250589e03d8 · outbound

This paper cites Rectified Flow: A Marginal Preserving Approach to Optimal Transport.

Accelerating Video Diffusion Models via Distribution Matching Rectified Flow: A Marginal Preserving Approach to Optimal Transport

Reference 15

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source=pdf_text observed=2026-08-11T20:17:52.624155Z digest=sha256:1db316148ba7ec3576fc1f0b448b0175314c8287770620f8c679534aea30bd02

Observation 0d1cc96f-343e-461b-b48d-222ec1540f44 · outbound

This paper cites Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed.

Accelerating Video Diffusion Models via Distribution Matching Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

Reference 17

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source=pdf_text observed=2026-08-11T20:17:52.632916Z digest=sha256:d05801005a416a32163338bd896cef847ab52ed7084f2a625b6e0217f06c3356

Observation c1c86cb6-3403-4336-97ad-11b0075f55f3 · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

Accelerating Video Diffusion Models via Distribution Matching Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 18

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source=pdf_text observed=2026-08-11T20:17:52.637688Z digest=sha256:309d247b87aba3bb57d4c6809abb7f0fc4a7b6e38d04b01ccbfbbcf9ee015060

Observation 892dc76a-5cd4-4f07-a4eb-883c4d4f0727 · outbound

This paper cites OSV: One Step is Enough for High-Quality Image to Video Generation.

Accelerating Video Diffusion Models via Distribution Matching OSV: One Step is Enough for High-Quality Image to Video Generation

Reference 19

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source=pdf_text observed=2026-08-11T20:17:52.642322Z digest=sha256:7f3bb29df04a4f8a2da1ed68e53b4a02095f90681181cac84abf4a81783f10fb

Observation bf3e4f3a-0959-4be6-b286-5c982c35e617 · outbound

This paper cites OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation.

Accelerating Video Diffusion Models via Distribution Matching OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation

Reference 20

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Observation 8b957deb-7705-404c-8059-42a66cb75333 · outbound

This paper cites Open-sora-plan.

Accelerating Video Diffusion Models via Distribution Matching Open-sora-plan

Reference 21

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source=pdf_text observed=2026-08-11T20:17:52.651491Z digest=sha256:ae916bcbd817b3a2c8634fb8adb0aa281b888ec4c52f4052c7de19ae83b59069

Observation 55170175-2ff6-4491-9e4f-b8c9597f0fac · outbound

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

Accelerating Video Diffusion Models via Distribution Matching SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 22

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source=pdf_text observed=2026-08-11T20:17:52.655287Z digest=sha256:dd9e30e0fa56afd4dfbe010a4a2969d5e231c41106794f3c7e22809500300eb4

Observation 14cd3c75-0101-4e00-bbc6-1024d2dc026c · outbound

This paper cites DreamFusion: Text-to-3D using 2D Diffusion.

Accelerating Video Diffusion Models via Distribution Matching DreamFusion: Text-to-3D using 2D Diffusion

Reference 23

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source=pdf_text observed=2026-08-11T20:17:52.659634Z digest=sha256:ed8b4f47d30b9b8ea953d2b3e06d1fa8a670f535d158dd4f6e6d98478471bdca

Observation 0323e8a7-b640-4bfb-b366-a1429fc68191 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Accelerating Video Diffusion Models via Distribution Matching Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 24

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source=pdf_text observed=2026-08-11T20:17:52.664112Z digest=sha256:ff0e711cb3659982a1d352585c104fe1f1b251667f3946f24745634d895df068

Observation ebc67f10-f04a-4565-800f-e9b06e36a698 · outbound

This paper cites Make-A-Video: Text-to-Video Generation without Text-Video Data.

Accelerating Video Diffusion Models via Distribution Matching Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 26

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source=pdf_text observed=2026-08-11T20:17:52.670713Z digest=sha256:596f25be8db1cafdec4e17a02370f56f9c83beccc90ecee786f038ee48a26630

Observation 3003471f-85c9-4394-af42-5fec51985426 · outbound

This paper cites Towards Accurate Generative Models of Video: A New Metric & Challenges.

Accelerating Video Diffusion Models via Distribution Matching Towards Accurate Generative Models of Video: A New Metric & Challenges

Reference 28

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source=pdf_text observed=2026-08-11T20:17:52.677853Z digest=sha256:23eefe11e714ff4ab746a480aa21d56f4a379daae456ee76c4a0ef4d3aa2f7e6

Observation fe1bd1c6-43fb-466d-846f-a7c0ab5720e8 · outbound

This paper cites LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models.

Accelerating Video Diffusion Models via Distribution Matching LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models

Reference 30

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source=pdf_text observed=2026-08-11T20:17:52.686270Z digest=sha256:fe2fcf0d674344889151caa6ea2c9de1ad5dca4c84c16f1bf88b13f0139ac6f5

Observation a17098ee-e2bb-460e-96da-408f81852423 · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

Accelerating Video Diffusion Models via Distribution Matching CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 31

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source=pdf_text observed=2026-08-11T20:17:52.690238Z digest=sha256:050597bda86554f00a7e8f6e496e3d0e3d32fce27582d6b95b6e91c6c299b090

Observation e2822956-26e9-4ef9-b4ac-8c30c0fe3fb8 · outbound

This paper cites SE(3) diffusion model with application to protein backbone generation.

Accelerating Video Diffusion Models via Distribution Matching SE(3) diffusion model with application to protein backbone generation

Reference 32

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source=pdf_text observed=2026-08-11T20:17:52.694287Z digest=sha256:a12b9c18a6b76e2fff0149e783dadd7bd79e317e3b5a33cc2b892ffbbd1585e2

Observation 9298ae2e-65c6-4720-ac8c-60c61805a41a · outbound

This paper cites Improved Distribution Matching Distillation for Fast Image Synthesis.

Accelerating Video Diffusion Models via Distribution Matching Improved Distribution Matching Distillation for Fast Image Synthesis

Reference 33

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source=pdf_text observed=2026-08-11T20:17:52.698987Z digest=sha256:1e47209be7b48a4d4f01de53f466b8f1a70139571947d3d73a8e7f0407411fcd

Observation 4afc7c36-2533-4f1a-b610-eb1483b10d9d · outbound

This paper cites Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation.

Accelerating Video Diffusion Models via Distribution Matching Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation

Reference 34

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source=pdf_text observed=2026-08-11T20:17:52.703606Z digest=sha256:eea27ff76c8abf26a1fd39faf214874e3c51f3d95589b290598aafbc8b81e95b

Observation 4e8b2b34-974c-4447-a9a0-da6155950680 · outbound

This paper cites Fast Sampling of Diffusion Models with Exponential Integrator.

Accelerating Video Diffusion Models via Distribution Matching Fast Sampling of Diffusion Models with Exponential Integrator

Reference 35

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source=pdf_text observed=2026-08-11T20:17:52.707980Z digest=sha256:b68999e2447f56d522750d89b8653eb3794e595767c948da8ec133ebeb888f0f

Observation 054f2700-8e02-4ad1-b8c7-07915b1f2285 · outbound

This paper cites I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models.

Accelerating Video Diffusion Models via Distribution Matching I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models

Reference 36

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source=pdf_text observed=2026-08-11T20:17:52.712254Z digest=sha256:733bc870b7def839f7ee40e418cbae6916f1a359755c5f62bb1abb124a01f8e8

Observation 152781fe-1807-4f93-aa15-a6473e5e4d17 · outbound

This paper cites SF-V: Single Forward Video Generation Model.

Accelerating Video Diffusion Models via Distribution Matching SF-V: Single Forward Video Generation Model

Reference 37

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source=pdf_text observed=2026-08-11T20:17:52.716615Z digest=sha256:3f66fde75f6a7721069bbcae6e07867a794e2af2a4e855a3ba4efadfc0e1a922

Observation 1736984f-f17f-4538-bffb-f6464c5e5c00 · outbound

This paper cites Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation.

Accelerating Video Diffusion Models via Distribution Matching Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation

Reference 38

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source=pdf_text observed=2026-08-11T20:17:52.720444Z digest=sha256:f7a3ade0d258aa698346b54f3b49a51261e8e7882ed592ef1c3e5f3466187396

Observation 494e74dc-c3ab-4519-b4a6-8ab9852f8d19 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Accelerating Video Diffusion Models via Distribution Matching Progressive Distillation for Fast Sampling of Diffusion Models

Reference 1992

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source=pdf_text observed=2026-08-11T20:17:52.667878Z digest=sha256:aa04a9b3dad5f97ed2d1e0c160baf567c398245a120926b3689b5c1ed50b5c69

Observation f2d89756-59c5-4924-9460-b3e83d5c95f1 · outbound

This paper cites Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation.

Accelerating Video Diffusion Models via Distribution Matching Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation

Reference 2013

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source=pdf_text observed=2026-08-11T20:17:52.604806Z digest=sha256:4d7d0791a80a5b06d2f2a14437ec9adc828fc77eaf154ab06cc874130442ac2d

Observation 29fd787a-61f4-44c6-b3ca-e77d1e6a389b · outbound

This paper cites Denoising Diffusion Implicit Models.

Accelerating Video Diffusion Models via Distribution Matching Denoising Diffusion Implicit Models

Reference 2015

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source=pdf_text observed=2026-08-11T20:17:52.674027Z digest=sha256:8939bf152137cc3308cc5ef6ff0682a61cfd91af2752c72ffca70de387e3f6f5

Observation baa9419f-94f1-4fa3-a2cc-e0b0ffece0b5 · outbound

This paper cites AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data.

Accelerating Video Diffusion Models via Distribution Matching AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data

Reference 2018

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source=pdf_text observed=2026-08-11T20:17:52.681971Z digest=sha256:9cd2f15bc70b6efc345934a580827adea71fa205d448d36fc1b4940ff3336fc7

Observation de0a4b24-b68d-47f2-a068-64c35b0d4d13 · outbound

This paper cites Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion.

Accelerating Video Diffusion Models via Distribution Matching Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion

Reference 2019

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source=pdf_text observed=2026-08-11T20:17:52.597172Z digest=sha256:a2253c1b507b15115a70ba5a1d97ba52cdfc5e1ecc692bab126aa1f892f2d26f

Observation 29ec8558-161c-496a-afaf-acf6be504b7c · outbound

This paper cites an unresolved cited work.

Accelerating Video Diffusion Models via Distribution Matching Unresolved cited work

Reference 2020

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source=pdf_text observed=2026-08-11T20:17:52.578504Z digest=sha256:1cf23daa6260b47f7fc8ed6580045dbe96451e328d7428c678dc29994f35de01

Observation 075cdb30-ac06-4452-b787-82ae6457fa05 · outbound

This paper cites Density estimation using Real NVP.

Accelerating Video Diffusion Models via Distribution Matching Density estimation using Real NVP

Reference 2021

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source=pdf_text observed=2026-08-11T20:17:52.573832Z digest=sha256:15a97b1a4720dc673b37bb598cf4251420ed50cf205666935d5f9205242ca9d2

Observation 3f34be5c-f475-408a-9160-31c204d32e39 · outbound

This paper cites InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation.

Accelerating Video Diffusion Models via Distribution Matching InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation

Reference 2022

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source=pdf_text observed=2026-08-11T20:17:52.628143Z digest=sha256:dd22cbb84c5ac3c5b9f9f36b6c3e13e900846c8c06c52b51a53f3f698b04e483

Observation f9e2acc2-1985-4bc0-add9-163bd0f3a5a5 · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning.

Accelerating Video Diffusion Models via Distribution Matching AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 2023

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source=pdf_text observed=2026-08-11T20:17:52.582750Z digest=sha256:8c4f5b0e330fb6ffad50469c11e6b53a53a18de7b599af180dee6a27902e35d2

Observation f97b9e80-af35-4a27-91d3-8d8d4232dd74 · outbound

This paper cites Towards Principled Methods for Training Generative Adversarial Networks.

Accelerating Video Diffusion Models via Distribution Matching Towards Principled Methods for Training Generative Adversarial Networks

Reference 2024

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unresolved
no resolver link, observed 2026-08-11T20:17:52.564536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:17:52.564536Z digest=sha256:6bbc937f51c87f546b6b51cd1c9aa096da28588551bf49a16bcb2b515c29ef87

Pith citing papers

Observation 8e30cff3-32f0-4b77-aaf1-f2f31e7dc61c · inbound

ReSim: Reliable World Simulation for Autonomous Driving cites this paper.

ReSim: Reliable World Simulation for Autonomous Driving Accelerating Video Diffusion Models via Distribution Matching

Reference 119

Resolution
verified exact
arxiv_id, observed 2026-05-19T09:32:16.745638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-19T09:28:00.597160Z digest=sha256:b9f986f86dabcc3fc07a02d7528aa1222b918a60cd5c06a0551ed9ff6fa4d4ab

Observation 75e53e38-b6e0-4dd5-87b0-e9042a296272 · inbound

Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms cites this paper.

Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms Accelerating Video Diffusion Models via Distribution Matching

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-05-14T01:38:35.957678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-14T01:35:14.878069Z digest=sha256:4a200068e42ca239d9cac9437e98989468b1555ef94c001ba90438cbd35cce32

Observation 51cede1c-2868-488b-a9b5-f9c53624b4a6 · inbound

Efficient Video Diffusion Models: Advancements and Challenges cites this paper.

Efficient Video Diffusion Models: Advancements and Challenges Accelerating Video Diffusion Models via Distribution Matching

Reference 203

Resolution
verified exact
arxiv_id, observed 2026-05-10T09:03:26.297060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-10T08:28:29.706249Z digest=sha256:6692e2df1813b1aeb64fd9049ef10e15d9b92ba00605f2d45090f7eeb7682191