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Paper Citation Record · LEDGER

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate

As of 18 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 3 inbound Pith citation observations for arXiv:2504.12259.

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

pith.paper-citation-record.v1
2504.12259 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:38:39.468676Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T19:09:45.710658Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T05:43:58.843048Z

Reference resolution

44 of 44 outbound references displayed

  • verified exact1
  • verified fuzzy13
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 67cd8e16-c5c8-4a42-9e24-ac7a5f9bbb1c · outbound

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

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 1

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source=pdf_text observed=2026-08-16T12:38:38.748487Z digest=sha256:9132e8425dff1362c4bbb1fde7fef3a9b46ee7f39aa5b7eb5b2096a048f54d91

Observation 7a583eab-3f3b-47c9-a0b5-9e4ebec0e2d9 · outbound

This paper cites Token merging for fast sta- ble diffusion.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Token merging for fast sta- ble diffusion

Reference 2

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source=pdf_text observed=2026-08-16T12:38:38.791819Z digest=sha256:05f54ffb7c32831a6d31a534f3c53b1f6bdfdb9f346e2c744d2a422b7074d71e

Observation c97de3da-80a2-48ff-b874-60402cff15b2 · outbound

This paper cites Token Merging: Your ViT But Faster.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Token Merging: Your ViT But Faster

Reference 3

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source=pdf_text observed=2026-08-16T12:38:38.830872Z digest=sha256:88b793376f2b4bac0f83bcbd27750d45f574c25cb6755b26361b5133b14867d3

Observation 9f5d3f8c-a654-436d-8470-613082278906 · outbound

This paper cites Video generation models as world simulators.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Video generation models as world simulators

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:38.835678Z digest=sha256:b18d39f00d895facb84eb1d8eadb9707ec20a1acc758108aab99e4776ef84e13

Observation dbf2cee0-16fb-4b5c-9229-17942f5b814a · outbound

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

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 5

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source=pdf_text observed=2026-08-16T12:38:38.840148Z digest=sha256:832d02c1d17067d3fa4841bb969035d842fe09e7d7572508c236b554e7f9e968

Observation 75316492-cc38-4818-8092-cb50947e5397 · outbound

This paper cites Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models

Reference 6

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source=pdf_text observed=2026-08-16T12:38:38.844726Z digest=sha256:953896648c54201354d94f2cc6daa568b9acca319cbc901887b46d8790b48ece

Observation a7c504ad-e4b8-49dc-9548-6bf0bbdf7e18 · outbound

This paper cites Structure and content-guided video synthesis with diffusion models.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Structure and content-guided video synthesis with diffusion models

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:38.849094Z digest=sha256:a42da2e12ea8fec8bb2dd73c533452e490fcfb6a980821ac70c1c24090132a16

Observation 88cf8276-278b-41c1-96f3-d09461cb4c9c · outbound

This paper cites Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning

Reference 8

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source=pdf_text observed=2026-08-16T12:38:38.852882Z digest=sha256:34f87e2919177cd7fedeb55b50d3731a70b1ab4a3d9b359a757a2f815f665890

Observation 9e5a09bc-9856-4832-85a0-660be567e736 · outbound

This paper cites Generative adversarial nets.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Generative adversarial nets

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:38.857297Z digest=sha256:2674e2796b4900456aaf281fea2b31e66bd11f6dbde12eff7c77771fc5c74aea

Observation 27af50fd-a0fd-4667-8052-5b3e31b2349d · outbound

This paper cites Denoising dif- fusion probabilistic models.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Denoising dif- fusion probabilistic models

Reference 10

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source=pdf_text observed=2026-08-16T12:38:38.861486Z digest=sha256:5d4abb8735a50e2c6ea57463b586f8cab4e87ae1c77ad5ec8eb788ccb07a2ccb

Observation 7d47c23c-83b9-424f-bf20-f2e6cb12243c · outbound

This paper cites Real-time intermediate flow estimation for video frame interpolation.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Real-time intermediate flow estimation for video frame interpolation

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:38.865295Z digest=sha256:28806057f08ecf7f54ec69ad0cb05605e1a28a4eab5cca2e95a623b04a4a37eb

Observation 6bf0d354-3709-4c34-b383-3d23d07a68dd · outbound

This paper cites Vbench: Comprehensive bench- mark suite for video generative models.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Vbench: Comprehensive bench- mark suite for video generative models

Reference 12

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source=pdf_text observed=2026-08-16T12:38:38.869788Z digest=sha256:87f9e5fac1c435b78e0e5c9e00b5e7c8f0825846efbb5911474de2d49ae35134

Observation 6ccd0851-215a-461b-8741-12fb7dc9efb5 · outbound

This paper cites Scale-adaptive feature aggregation for efficient space-time video super-resolution.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Scale-adaptive feature aggregation for efficient space-time video super-resolution

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:38.880698Z digest=sha256:ad95b6d2bfbaebd585c9f2d2ef55122c89ce65d58cf9dba562a942a6c7fb5ebf

Observation 84e3dfca-b5d0-48fd-a620-1d0c1b0c5e08 · outbound

This paper cites Adaptive Caching for Faster Video Generation with Diffusion Transformers.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Adaptive Caching for Faster Video Generation with Diffusion Transformers

Reference 14

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source=pdf_text observed=2026-08-16T12:38:38.929792Z digest=sha256:ebdf293a16d4c8e745a0f1e42981aae2099e9be3be72d9369ba0086d4dabb0c0

Observation 785a618b-2086-4314-bc37-98b826b1d4f2 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate A style-based generator architecture for generative adversarial networks

Reference 15

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source=pdf_text observed=2026-08-16T12:38:38.974248Z digest=sha256:2f75a2f06b1766f59defde38b2ed43432ea745ea1b5f133a1334878798b005f6

Observation 3c8ba51d-0e8b-4a9b-9e50-85b66e57f6ce · outbound

This paper cites Auto-encoding vari- ational bayes, 2013.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Auto-encoding vari- ational bayes, 2013

Reference 16

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source=pdf_text observed=2026-08-16T12:38:38.997313Z digest=sha256:087bee689332e5bd94a0df0ad73f419d4dc89161765cc61ee4e445cae353d5b4

Observation f37110b4-9d05-4312-8d6a-61ce26637afe · outbound

This paper cites HunyuanVideo: A Systematic Framework For Large Video Generative Models.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate HunyuanVideo: A Systematic Framework For Large Video Generative Models

Reference 17

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source=pdf_text observed=2026-08-16T12:38:39.001641Z digest=sha256:356447b2cad8ba1d0dcb741c1c0f4636e780dc46bb89dbf7484ca3cd19f5ac9b

Observation 79590cb1-e8a2-4854-b015-3cb4353b6a04 · outbound

This paper cites an unresolved cited work.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Unresolved cited work

Reference 18

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source=pdf_text observed=2026-08-16T12:38:39.006294Z digest=sha256:f24cc28d311ba4fcab0782e4612fea93b9e5556210424404a8cda0a86167177f

Observation 2716eb1c-0855-47ee-886a-ff772862207c · outbound

This paper cites Vidtome: Video token merging for zero-shot video editing.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Vidtome: Video token merging for zero-shot video editing

Reference 19

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source=pdf_text observed=2026-08-16T12:38:39.010561Z digest=sha256:f1697221d444c3f4c0fef868311d937cc824ddce8bb33fee4b9ef9b6894ca3ee

Observation 68635b21-b4e8-4a4e-9376-1d6ae49154c7 · outbound

This paper cites Open-Sora Plan: Open-Source Large Video Generation Model.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Open-Sora Plan: Open-Source Large Video Generation Model

Reference 20

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source=pdf_text observed=2026-08-16T12:38:39.014612Z digest=sha256:acb8a3204df02eb4715943816e40c2ffd821df732e6008d01f7bffc9f8e3cb2e

Observation 10665c07-2900-495a-881f-6ff016c30050 · outbound

This paper cites Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Reference 21

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source=pdf_text observed=2026-08-16T12:38:39.018662Z digest=sha256:d7f66d56ee5a13e515b68ac41211f291294150ee5e7198b4b917244548b8912d

Observation 9dc2b6ba-58e1-4460-8375-daadeb4d78f5 · outbound

This paper cites A study of subjective video quality at various frame rates.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate A study of subjective video quality at various frame rates

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:39.023565Z digest=sha256:f1e0d3bf426ab0fcedcdf4a5a46d87c00c0d43f97ae1fd2a701ee36c4caa0847

Observation 82445d38-1761-4133-bde1-621ce609ecfa · outbound

This paper cites Scalable diffusion models with transformers.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Scalable diffusion models with transformers

Reference 23

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source=pdf_text observed=2026-08-16T12:38:39.028007Z digest=sha256:ef041efaa787dd849e405ef3d74de71506061cf6b7cb5092b8950d6c6df92c00

Observation e2a5609d-5ee1-441b-bb39-181a98999b2f · outbound

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

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 24

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source=pdf_text observed=2026-08-16T12:38:39.032068Z digest=sha256:ae3f914a083ad26cab07af9323c81af8d96e3272c891da619742200e779b7d16

Observation 0dafea98-db47-46ad-84b8-e00dd28bc553 · outbound

This paper cites Sampson, Shikai Li, Simone Parmeggiani, Steve Fine, Tara Fowler, Vladan Petro- vic, and Yuming Du.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Sampson, Shikai Li, Simone Parmeggiani, Steve Fine, Tara Fowler, Vladan Petro- vic, and Yuming Du

Reference 25

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:39.035562Z digest=sha256:9103cd631d0bf618fbf62f8ef07676373f855a3e0c249628b9856def051e9ba5

Observation a09e068e-8a49-4998-bc65-122062ffb520 · outbound

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

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate High-resolution image synthesis with latent diffusion models

Reference 26

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source=pdf_text observed=2026-08-16T12:38:39.038604Z digest=sha256:4e50e9948df9ef1fc5d382de54c6c245b17e9d74761f9d9a097566db4e18fe0a

Observation 06616b2f-840c-43f8-8011-c7cb69a0ccc7 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Photorealistic text-to-image diffusion models with deep language understanding

Reference 27

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source=pdf_text observed=2026-08-16T12:38:39.042275Z digest=sha256:f0ba4585fe9b8070f94a5ab67276599ec5d98b42bb2a2801d5efc5aa9c4210a9

Observation 5f7cc21c-9ad1-4fff-b77a-3d4845c65bee · outbound

This paper cites MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models

Reference 28

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source=pdf_text observed=2026-08-16T12:38:39.045973Z digest=sha256:11b3d832c00f19e42a5fc72c2f75ec34dfcc55e707661ffe0d44cbe2e95d773e

Observation c03e854c-f3d5-4b08-9243-5dc3c098db7d · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Deep unsupervised learning using nonequilibrium thermodynamics

Reference 29

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source=pdf_text observed=2026-08-16T12:38:39.087866Z digest=sha256:0a6de6fbcbca1203d14abd474863aada004c8839ee574c4358bbfeaa105a2e8e

Observation 4d04a0cc-bd62-450f-b9fd-076a5a7a33ba · outbound

This paper cites Rate control for low-bit-rate video via variable-encoding frame rates.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Rate control for low-bit-rate video via variable-encoding frame rates

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:39.163124Z digest=sha256:b83f08340a3db517ffa9edc92aa49b0bc8a92526bf5973dd3691e046e05ffa6c

Observation 69b9cc37-7127-435d-b456-db656f0f3a31 · outbound

This paper cites AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration

Reference 31

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source=pdf_text observed=2026-08-16T12:38:39.206024Z digest=sha256:32e216f19cc522aa197ecfc0ca75f471552be588f20c6fbdd44e050a5791231f

Observation f992b9a2-9381-47fc-83e2-173ff9bd998b · outbound

This paper cites Mocogan: Decomposing motion and content for video generation.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Mocogan: Decomposing motion and content for video generation

Reference 32

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source=pdf_text observed=2026-08-16T12:38:39.211071Z digest=sha256:23a098253689e2cfd8f4843db1e05c7ecffb25ced00cad9858b0790e44cfde9f

Observation 966842cc-25c9-4cec-8c6e-a6a5dd271bc0 · outbound

This paper cites Attention is all you need.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Attention is all you need

Reference 33

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source=pdf_text observed=2026-08-16T12:38:39.215567Z digest=sha256:7fe3fc7523feef7a0cf50eae1c67205f29056bc1ef44ecd3dcefd399a841cb82

Observation 6bad5f5d-b81c-4bbc-9428-816508494dc1 · outbound

This paper cites Omnitokenizer: A joint image- video tokenizer for visual generation.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Omnitokenizer: A joint image- video tokenizer for visual generation

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:39.219635Z digest=sha256:b379b8e4be99f5232ef29a9cc2f8b3a909fab32ae0b17a12c3c183a598bf46de

Observation 3a00d3d3-bfe1-4b9d-8f4e-f45448a2d84f · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Image quality assessment: from error visibility to structural similarity

Reference 35

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source=pdf_text observed=2026-08-16T12:38:39.223858Z digest=sha256:19532d7b7bde9cec9fe1600d1b7d60e0ba6fa88e68c7038ed30972187ba05c00

Observation 18d6ff58-83ce-43d8-95bf-c94a328accb6 · outbound

This paper cites GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions

Reference 36

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source=pdf_text observed=2026-08-16T12:38:39.227730Z digest=sha256:e25f171f305437629b9eed66ecfa51beaef7fe26fd7ac43ce321a22ef53db5bd

Observation 68b81096-9330-402e-b293-286acb0fad74 · outbound

This paper cites Fast- vqa: Efficient end-to-end video quality assessment with frag- ment sampling.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Fast- vqa: Efficient end-to-end video quality assessment with frag- ment sampling

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-16T12:38:39.856295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:39.231929Z digest=sha256:81b070a48cc93fdafcb6a98c37e2229723333e8c39f885519815490e1fd11554

Observation 11aba5e0-8fb6-4d8a-83ff-7e7460eebaaf · outbound

This paper cites Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity

Reference 38

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no resolver link, observed 2026-08-16T12:38:39.235933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:38:39.235933Z digest=sha256:6e5a31259e2e4faa4de24d83a937b37a5d0bc5e445fc9657a0b10fb1439fdcd5

Observation 0aba210c-32b7-4284-a628-7169ef8c887f · outbound

This paper cites DLFR-VAE: Dynamic Latent Frame Rate VAE for Video Generation.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate DLFR-VAE: Dynamic Latent Frame Rate VAE for Video Generation

Reference 39

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verified exact
local_arxiv, observed 2026-08-16T12:38:39.532010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:39.273774Z digest=sha256:b9adb0528f747c607d2ef37365eb31bcd2703fe38b4cc776e7a5d5f4a651ff7c

Observation 47fe8059-200c-4356-832f-d2cbf4e315de · outbound

This paper cites Training-free and hardware-friendly acceleration for diffu- sion models via similarity-based token pruning.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Training-free and hardware-friendly acceleration for diffu- sion models via similarity-based token pruning

Reference 40

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verified fuzzy
raw_fallback, observed 2026-08-16T12:38:39.841175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:39.356121Z digest=sha256:a9648c890d2636474d87743926822f7f1cf2828b3f3eee08f3fd8080e385196e

Observation cc845b07-edcf-4ae0-82c9-a0d547cb2e9e · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate The unreasonable effectiveness of deep features as a perceptual metric

Reference 41

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unresolved
no resolver link, observed 2026-08-16T12:38:39.425758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:38:39.425758Z digest=sha256:5f21e1da0f5f82d4e2cd1c24ca14da0613a383d4429be9df16c211d67935c7f6

Observation 73421633-e3aa-43eb-88d4-2929c029b1d1 · outbound

This paper cites Cross- attention makes inference cumbersome in text-to-image dif- fusion models.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Cross- attention makes inference cumbersome in text-to-image dif- fusion models

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-16T12:38:39.737343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T12:38:39.458138Z digest=sha256:2b883f63c0ec5a6cf34babe91eea779fc2772c3928b8d8ee417b34a453eb5509

Observation b35e6825-cabb-467f-9470-c2af8496806b · outbound

This paper cites Real-Time Video Generation with Pyramid Attention Broadcast.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Real-Time Video Generation with Pyramid Attention Broadcast

Reference 43

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unresolved
no resolver link, observed 2026-08-16T12:38:39.465001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:38:39.465001Z digest=sha256:bb41079c3cc2e1c8a016c472a2083f8c8e3f16176012d4b202d30e97f02ce065

Observation 86f6fc95-ddf3-4dba-9692-427e16de919b · outbound

This paper cites Open-Sora: Democratizing Efficient Video Production for All.

VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate Open-Sora: Democratizing Efficient Video Production for All

Reference 44

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unresolved
no resolver link, observed 2026-08-16T12:38:39.468676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:38:39.468676Z digest=sha256:976e1a93fa35e9c98cda626c6051501b5f2235cf5cb80c26d068eaaadcecb62c

Pith citing papers

Observation a112b229-bdc1-4221-bdf2-9c76bfd30437 · inbound

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

Efficient Video Diffusion Models: Advancements and Challenges VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate

Reference 171

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verified exact
arxiv_id, observed 2026-05-10T09:03:25.957197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-10T08:28:29.706249Z digest=sha256:268ff83a5d32baf40d1a3856ce9936e813e5cdc3ec879e1bb32722480ae03f77

Observation e0fa2656-9670-4c6a-9efe-3be96fa5e0fb · inbound

Dynamic Video Generation: Shaping Video Generation Across Time and Space cites this paper.

Dynamic Video Generation: Shaping Video Generation Across Time and Space VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate

Reference 41

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verified exact
arxiv_id, observed 2026-05-21T05:43:58.844504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-21T05:42:41.925474Z digest=sha256:853dbc88a0f1ba74d5878c5a00622b46c19b91d327a17268cacd0b6107035f38

Observation 66ba069b-b811-46f8-b150-417ec937ff7f · inbound

Temporal Concentration from Rollout Errors: Implicit Preference Optimization for Text-to-Video Diffusion cites this paper.

Temporal Concentration from Rollout Errors: Implicit Preference Optimization for Text-to-Video Diffusion VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T19:09:45.710658Z digest=sha256:6ac19fb9a719247a44e74ef6bf5bc2cb01861f7214554f9b9ebfe9143d88fc8d