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Paper Citation Record · LEDGER

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation

As of 18 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2506.16119.

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

pith.paper-citation-record.v1
2506.16119 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:49:57.535396Z

measured 43 of 43 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T04:13:18.570081Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T17:05:51.054099Z

Reference resolution

42 of 42 outbound references displayed

  • verified exact2
  • verified fuzzy14
  • unresolved26
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a34d4aac-7f57-4650-ace5-9716684ff403 · outbound

This paper cites A Noise is Worth Diffusion Guidance.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation A Noise is Worth Diffusion Guidance

Reference 1

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source=pdf_text observed=2026-08-06T23:49:53.244659Z digest=sha256:4d775e6a0c96ce5c020a0a17c7f7633721309850a04558b995a881559515f66e

Observation fb105ac8-fac1-48b8-b5f5-c6326ce1dfb7 · outbound

This paper cites Frozen in time: A joint video and image encoder for end-to-end retrieval.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Frozen in time: A joint video and image encoder for end-to-end retrieval

Reference 2

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source=pdf_text observed=2026-08-06T23:49:53.313544Z digest=sha256:13f669316f6bb03fcb25040959a53d9880927d86a81450469f5857639954ecbe

Observation a6c3ec74-a015-42f8-9241-a2e75cc0d662 · outbound

This paper cites Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models

Reference 3

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source=pdf_text observed=2026-08-06T23:49:53.402851Z digest=sha256:7dacecc0611ee935d42f44cb7341892f09a7bc2837dc47fd16607468a526c84f

Observation 31c46ab7-a2af-4544-8122-2f1125a562af · outbound

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

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 4

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source=pdf_text observed=2026-08-06T23:49:53.469289Z digest=sha256:32a5f017dd5e9d6c112f31fe1a4f48f8a2cb2a69a43244acdbefab10a5e6a3f7

Observation 78cc1ab4-4035-482e-a410-322141d4d12a · outbound

This paper cites Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models.ACM transactions on Graphics (TOG), 42(4):1–10, 2023.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models.ACM transactions on Graphics (TOG), 42(4):1–10, 2023

Reference 5

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source=pdf_text observed=2026-08-06T23:49:53.562565Z digest=sha256:d88faf1e2ba5e786db4546f6d6b340c0264916242108623830a72dfa26071c99

Observation 29414514-d100-4412-b662-a29605d36342 · outbound

This paper cites Videocrafter2: Overcoming data limitations for high-quality video diffusion models.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Videocrafter2: Overcoming data limitations for high-quality video diffusion models

Reference 6

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source=pdf_text observed=2026-08-06T23:49:53.629334Z digest=sha256:56ea9d222c2e18ab861098a2c19b1d9936c78549f927cd1d37613f93bf39f4a7

Observation 0b488557-280d-4e56-ba3f-3875c3f91bc3 · outbound

This paper cites Preserve your own correlation: A noise prior for video diffusion models.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Preserve your own correlation: A noise prior for video 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=2026-08-06T23:49:53.736141Z digest=sha256:807d04ac831bc2362762bccba6befa4249921ca4ca13b84853ab22206640307c

Observation ce9715c3-0107-4f1b-a735-3f4c4b389046 · outbound

This paper cites Factorizing text-to-video generation by explicit image conditioning.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Factorizing text-to-video generation by explicit image conditioning

Reference 8

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source=pdf_text observed=2026-08-06T23:49:53.805072Z digest=sha256:6241de897d4d9a8c273b0d410c1a6660b5a9618a8615685f7319ae99426bfbfc

Observation 0621ffaa-19cd-45e1-8177-721cf0d0ad3d · outbound

This paper cites Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation

Reference 9

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source=pdf_text observed=2026-08-06T23:49:53.894676Z digest=sha256:efe2364f4d9e5020a54fa2634f917e9527622345ad79877e64f888f93ef819d7

Observation 65886361-74b1-4c56-8cb7-893bbe273eef · outbound

This paper cites I4VGen: Image as Free Stepping Stone for Text-to-Video Generation.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation I4VGen: Image as Free Stepping Stone for Text-to-Video Generation

Reference 10

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source=pdf_text observed=2026-08-06T23:49:53.980357Z digest=sha256:1c13631fc4546c49192ceaf7d5cb8215a05a6c1a4cf6f68bb171aea647c37efa

Observation 9cfb0793-fc71-4e34-80cd-5e1700e5b4ae · outbound

This paper cites Initno: Boosting text-to-image diffusion models via initial noise optimization.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Initno: Boosting text-to-image diffusion models via initial noise optimization

Reference 11

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source=pdf_text observed=2026-08-06T23:49:54.053043Z digest=sha256:f084b8572c02ca4f3af6e94adb327676daaa0ae1a81f2e2be9a8f8d408efd533

Observation 8c92772f-ea80-4da5-9f1f-7df5927ed089 · outbound

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

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 12

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source=pdf_text observed=2026-08-06T23:49:54.124164Z digest=sha256:47233cd4805c101e27d18341f7c5b780ca02de8865e2a6fc624c2b31d14f9e1d

Observation c53a8221-b6de-44a2-bcd7-cb9c4a628ef6 · outbound

This paper cites Kingma, Ben Poole, Mohammad Norouzi, David J.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Kingma, Ben Poole, Mohammad Norouzi, David J

Reference 13

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source=pdf_text observed=2026-08-06T23:49:54.237821Z digest=sha256:ec89c612397cf260bcf2ee72984fa41f8faa046cf8fe9d8010fee2556c242e66

Observation fb662e59-2bda-476d-92e3-8c74afa7a197 · outbound

This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 14

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source=pdf_text observed=2026-08-06T23:49:54.320583Z digest=sha256:33962af5d8abce31862f4b75c4183e7a448ff13ce10ef019c40b914f015b844d

Observation 8d45d331-12fd-4ac6-a5eb-7e16504e113e · outbound

This paper cites Hunyuanvideo: A systematic framework for large video generative models, 2025.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Hunyuanvideo: A systematic framework for large video generative models, 2025

Reference 15

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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=2026-08-06T23:49:54.408665Z digest=sha256:1fc88dca356c75f0b88249d057c4f04fbcb69b179863956c2f4eb9270dcb4ef6

Observation 83fb48dd-eb6e-4b34-870c-797d5b720a6e · outbound

This paper cites Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization

Reference 16

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source=pdf_text observed=2026-08-06T23:49:54.533224Z digest=sha256:40ba77ad99d2c2a61c31e76b3f532996b37ab19c8a93af56e064137dbe2cdab3

Observation 8fdd355c-72dc-4b4b-b113-f0e524470fc8 · outbound

This paper cites Uniformer: Unified transformer for efficient spatiotemporal representation learning, 2022.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Uniformer: Unified transformer for efficient spatiotemporal representation learning, 2022

Reference 17

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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=2026-08-06T23:49:54.638320Z digest=sha256:7af6eba8549f9fa037d1bcfa3d7c73a7f0fdd65bacc5e42e493737491a33a9ae

Observation d1587bd7-11cf-42c0-898d-a5b2322bd77f · outbound

This paper cites All seeds are not equal: Enhancing compositional text-to-image generation with reliable random seeds, 2025.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation All seeds are not equal: Enhancing compositional text-to-image generation with reliable random seeds, 2025

Reference 18

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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=2026-08-06T23:49:54.744981Z digest=sha256:25f913c6d99f2d5f749a86caf3dbc84eb3f76900e20d9e89390cd0ebe08dbc80

Observation eec1184e-4ae9-4e96-a288-b699537949bc · outbound

This paper cites Tuning-Free Noise Rectification for High Fidelity Image-to-Video Generation.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Tuning-Free Noise Rectification for High Fidelity Image-to-Video Generation

Reference 19

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source=pdf_text observed=2026-08-06T23:49:54.836834Z digest=sha256:e7b79396f897ee2bc5662459f6d152ba451aca7b73c1e9411d9c46bb4d96d08e

Observation c3005abc-3d4e-4cb4-babc-c2cdb6eb6eee · outbound

This paper cites FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention

Reference 20

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source=pdf_text observed=2026-08-06T23:49:54.958599Z digest=sha256:a77632cb08dc340990da6f8bac9217fb438aae903719da115763ce2049106157

Observation 077e67df-9275-485d-8091-d6228cf03924 · outbound

This paper cites Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps

Reference 21

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source=pdf_text observed=2026-08-06T23:49:55.062864Z digest=sha256:5afc8517a639752c9489e603fde0c19e64207b5dbec8af4ecdf9f636fdee2050

Observation 5fc40e7c-2f57-4aec-9fb0-7e81091ee6a5 · outbound

This paper cites Sdedit: Guided image synthesis and editing with stochastic differential equations, 2022.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Sdedit: Guided image synthesis and editing with stochastic differential equations, 2022

Reference 22

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source=pdf_text observed=2026-08-06T23:49:55.140582Z digest=sha256:2159e0c9d7a5ce19658dc32e5bd15054618e46aa5e6f9ccc5bd31f6bbad68b2d

Observation 3fc57036-1531-4e40-aed8-5a856dad101a · outbound

This paper cites Freenoise: Tuning-free longer video diffusion via noise rescheduling, 2023.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Freenoise: Tuning-free longer video diffusion via noise rescheduling, 2023

Reference 23

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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=2026-08-06T23:49:55.259457Z digest=sha256:8a29457458802c8eaebcbf746f34149e72decdd3f2031b45e8c92c951cdfd029

Observation b5ea5c53-6e0c-4dab-a5a4-623df694599d · outbound

This paper cites IV-Mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation IV-Mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis

Reference 24

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source=pdf_text observed=2026-08-06T23:49:55.364242Z digest=sha256:0c96c874b904acf0b0d12b646feb53be96310d83f678a1f587c98fecf7ce80f5

Observation 1c6340a5-691e-4ba5-8ebb-fd9fd1df431e · outbound

This paper cites Make-a-video: Text-to-video generation without text-video data, 2022.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Make-a-video: Text-to-video generation without text-video data, 2022

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=2026-08-06T23:49:55.460639Z digest=sha256:01d45c7429f29a6c967e29fd47fe7e3ca37818852499e9510d334cd1d5f7b54e

Observation cb65b5ee-2537-4c7c-8ff5-0a77127c6711 · outbound

This paper cites Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024

Reference 26

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source=pdf_text observed=2026-08-06T23:49:55.554719Z digest=sha256:2d57457d1188d24435fd490ff1f96cb9b5bc624ab86a523fa900fce86080d9b7

Observation 1e9a0c98-db0e-47c9-9508-f02496115424 · outbound

This paper cites End-to-end diffusion latent optimization improves classifier guidance.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation End-to-end diffusion latent optimization improves classifier guidance

Reference 27

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source=pdf_text observed=2026-08-06T23:49:55.640524Z digest=sha256:7ab5e8b9bad7b609ce4b7f9bb8633a58d8ebe26bcb7daa953ac93d1d75e119fb

Observation 4b9311d5-dc17-48a9-a7c0-08b0b14c1eee · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Wan: Open and Advanced Large-Scale Video Generative Models

Reference 28

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source=pdf_text observed=2026-08-06T23:49:55.790454Z digest=sha256:99830470abdab71e3b5f32bcf3b5c6308448eae307ef88eb68deee4a0c839fcc

Observation 1ab687fb-478b-41d7-b7e2-6d3affccdb88 · outbound

This paper cites Modelscope text-to-video technical report, 2023.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Modelscope text-to-video technical report, 2023

Reference 29

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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=2026-08-06T23:49:55.887061Z digest=sha256:71a55acc4d2e9e5b5bf8015dd4f979f0ba522d63da830e8136059618a87f1683

Observation ef908e50-f7e0-484a-8c3f-86474e520615 · outbound

This paper cites The Silent Assistant: NoiseQuery as Implicit Guidance for Goal-Driven Image Generation.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation The Silent Assistant: NoiseQuery as Implicit Guidance for Goal-Driven Image Generation

Reference 30

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source=pdf_text observed=2026-08-06T23:49:55.996195Z digest=sha256:205ee2b322fc7401f955c2dc48907d6503c054e6ce02c378ab49d4891830c62a

Observation 7e91a4b0-2bb5-4231-b848-c0ff9ff419eb · outbound

This paper cites CoNo: Consistency Noise Injection for Tuning-free Long Video Diffusion.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation CoNo: Consistency Noise Injection for Tuning-free Long Video Diffusion

Reference 31

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local_arxiv, observed 2026-08-06T23:49:57.822728Z

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=2026-08-06T23:49:56.095704Z digest=sha256:c0dfe55fb1b9267d2aa8084f3f6a3b01550284ffae696e4b453541e3643d0df7

Observation a20582f8-a34f-49b0-977d-ca17d0146926 · outbound

This paper cites Freeinit: Bridging initialization gap in video diffusion models.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Freeinit: Bridging initialization gap in video diffusion models

Reference 32

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raw_fallback, observed 2026-08-06T23:49:59.773468Z

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=2026-08-06T23:49:56.218916Z digest=sha256:215a0342fb279898fce015e4dcbca619b4337273ec99e09b490a76474fc80146

Observation 106dee4f-43ac-4cab-ac54-a2ba5863b465 · outbound

This paper cites Msr-vtt: A large video description dataset for bridging video and language.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Msr-vtt: A large video description dataset for bridging video and language

Reference 33

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source=pdf_text observed=2026-08-06T23:49:56.346355Z digest=sha256:f7aa19da8fb536cfb520bbcb43ebcb594cb246861a69c1f9a0e9fd8067233ef8

Observation 7a76d926-4f12-4089-ba2d-effefb0eb159 · outbound

This paper cites Good seed makes a good crop: Discovering secret seeds in text-to-image diffusion models, 2025.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Good seed makes a good crop: Discovering secret seeds in text-to-image diffusion models, 2025

Reference 34

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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=2026-08-06T23:49:56.447287Z digest=sha256:dee3cf92c1bf93ee33b8a6141d7590e8358f72f7ca82d5285e290c5a053e35ad

Observation 73a9e6c5-0ace-4929-9e68-898b56e95d6c · outbound

This paper cites Noise calibration: Plug-and-play content-preserving video enhancement using pre-trained video diffusion models.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Noise calibration: Plug-and-play content-preserving video enhancement using pre-trained video diffusion models

Reference 35

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raw_fallback, observed 2026-08-06T23:49:59.291490Z

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=2026-08-06T23:49:56.551359Z digest=sha256:ed3e42e5c1dbd845ea1f000a6ffb32ea4031d8b1792472cc68fa307ab3de2ad4

Observation c2899ba0-2d90-4977-b540-cb5f7afa730c · outbound

This paper cites Cogvideox: Text-to-video diffusion models with an expert transformer, 2025.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Cogvideox: Text-to-video diffusion models with an expert transformer, 2025

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:49:59.003487Z

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=2026-08-06T23:49:56.678048Z digest=sha256:fa5538e08e2ee7ee693572cc098bd858ba71030390e0de7847231a04bc673e3d

Observation 320b184c-c187-467f-bc5e-2828d1287253 · outbound

This paper cites Chronomagic-bench: A benchmark for metamorphic evaluation of text-to-time-lapse video generation, 2024.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Chronomagic-bench: A benchmark for metamorphic evaluation of text-to-time-lapse video generation, 2024

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:49:58.688011Z

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=2026-08-06T23:49:56.773847Z digest=sha256:d87ff53e39fbc8d95636b85aae920d3b8dedeba6ca12c8051b68827cba493705

Observation 2eda3fcb-60a8-4e65-9b19-27de78ea0204 · outbound

This paper cites RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T23:49:56.913424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:49:56.913424Z digest=sha256:80f24d84b53b74d12cd4f86bb2289add8628169c2149476c4eb1bc609a93ba29

Observation 1b60286d-9adf-45ee-96c2-0f64a632da60 · outbound

This paper cites Vbench-2.0: Advancing video generation benchmark suite for intrinsic faithfulness, 2025.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Vbench-2.0: Advancing video generation benchmark suite for intrinsic faithfulness, 2025

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:49:58.582880Z

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=2026-08-06T23:49:57.057143Z digest=sha256:011c86437c80105655377cac333ae1924c8dce0d97d09e90c58515eb533b1e52

Observation 247eb103-fdfc-4e42-ae05-acd55711d78f · outbound

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

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Open-Sora: Democratizing Efficient Video Production for All

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T23:49:57.182687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:49:57.182687Z digest=sha256:152dfc5203dcc336695ec1a27a20a765b556519f775df5d9beb8edc1938a87a0

Observation d319efbb-4f36-4d87-8deb-71297a111cff · outbound

This paper cites Magicvideo: Efficient video generation with latent diffusion models, 2023.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Magicvideo: Efficient video generation with latent diffusion models, 2023

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:49:58.410581Z

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=2026-08-06T23:49:57.354245Z digest=sha256:5b028d65dca5d1e9fe1190016b506fb4a5f975d4a42b9b979cc3f1be0ac00361

Observation dcfd47d6-ffb3-4a10-a234-f68e170161d6 · outbound

This paper cites Golden Noise for Diffusion Models: A Learning Framework.

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation Golden Noise for Diffusion Models: A Learning Framework

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T23:49:57.535396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:49:57.535396Z digest=sha256:25a0c2930cd3275cb380105732aab1f67aea7b923eacbf3b4fb784f3bd291254

Pith citing papers

Observation 14134386-af52-42b3-9221-3b21a4f21a43 · inbound

Exposure Bias Can Alleviate Itself via Directional and Frequency Rectification in Flow Matching cites this paper.

Exposure Bias Can Alleviate Itself via Directional and Frequency Rectification in Flow Matching FastInit: Fast Noise Initialization for Temporally Consistent Video Generation

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-01T17:05:51.055478Z

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=2026-06-29T04:13:18.570081Z digest=sha256:e2023cee2c2710cfb442989cb16828237368490bb94b369037f6530be1ee9a9a