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Paper Citation Record · LEDGER

FastInit: Fast Noise Initialization for Temporally Consistent Video Generation

As of 8 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 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

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=sha256:635767c824d2fa0618d0c66df5547efc6161f101e79138beca8c632b727614f6

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=sha256:2dc637b487e59afe694e14517d33441844a907ec4e0df8697a440d72f3abff25

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:e7e5003ad406bded7ed8ddeaf6b387b711e0c6130f962114bd3a4f1f738d03be

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=sha256:5d0534c5af91ef1bb1c1a80a51ffdda02d4bd699412c3cd71a7eda23b1a45cf1

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:e3f77ef4a92ca2bf8f849399a07bf580758f7880fcabccacd948f0f9891101b6

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=sha256:9d62611380195e59d5392e5904e4362d9b398c454324ad7a43fabdc593c20de3

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

source=pdf_text observed=2026-08-06T23:49:53.736141Z digest=sha256:12185a629f46d8cfb7ced9797f760b526cb82b578ebb872c8bbb964fb337646e

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=sha256:86aabaab88c5c64338a743d5399f7a2a0e814e608ab2cf81da89bca94cb06338

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=sha256:559ee1f010e4f31b2c654cdeef8db74f3469bbb1f5d835c5a5a9ac9034c8ac68

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=sha256:24514e8762fd38db6c99e82aba8acad0f72062aeff4a7a57c2f819c36fb515da

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:d31f1bf23a9f535e563a9f547c663ba695deed5edbe0b674c7a9bc36d490cd34

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:d343492ab4aacc276fb9d267836cb56d23b634aeff6e55823c46b5f945a4cfc8

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=sha256:96c9e2414a2f35192973c34a61764670a131bccfcbe19ccde73552323a4747d3

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=sha256:64258f61ff3ab8569aa261d9159107080592a11a9f4782e48d77e182830fd88a

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

source=pdf_text observed=2026-08-06T23:49:54.408665Z digest=sha256:c9c179d05066652d39e8f5d22c67572f882d73fd982f9711b27a358052a171ba

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:d7f6bed730a119f271f76b94f80565390f94e14ba5dffecae9da15037c456a9c

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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source=pdf_text observed=2026-08-06T23:49:54.638320Z digest=sha256:caa1aa06ce7972cb7cc9fd38eee8eb05e262c3aee08a10f7879e7f066eabf700

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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source=pdf_text observed=2026-08-06T23:49:54.744981Z digest=sha256:4e8b0d50fa8852ccb09010918b841c7cd5f44c4472fcf3d07100233ba2ff1c86

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=sha256:6d0e4950dbcddfca381fdafea33f0ee542b1de44f0b697d52d9deb70d9011e04

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:e5bf7ccc9180f8073c6e68d844e96d62f3587e21a8ef44979f391a16e57266e3

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=sha256:0150e09dc405d6e32f12ef69f04938e537b7552f9b4bd99b61361d7aaa4fa770

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=sha256:260102981d99e235ab79dcab782beda49827058d4b0a53d624dcbaaa00206939

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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source=pdf_text observed=2026-08-06T23:49:55.259457Z digest=sha256:3a72a1257f1173a93603733a03e2ae7f8e30aebd5d3b55179a2ad0d6b11a2a89

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=sha256:1ce6a319d0466762373d72b2d4b2de4b74b9e4fb0f1186d4e3a33cfcd3f20c75

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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source=pdf_text observed=2026-08-06T23:49:55.460639Z digest=sha256:72ad46d38603f104f48715c8203dca2eb55fd70506f249a4c9b384afbda268b6

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=sha256:6bf8d2b4e48924dc2517520753a0ae59d4189c4f7c1c947cc5ec62114ce250fb

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=sha256:7e8fa9f4cf4dc9ef3998a54b780336b38b1cd21ebf6b2397c223b39b4554d144

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=sha256:0ac7fbc28fa1f0bdc298f5a0e1bf64cb2cf73f83afde8e9307c75ea5c2a8fd83

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

source=pdf_text observed=2026-08-06T23:49:55.887061Z digest=sha256:37714decacdc5cd39d702612820d378c17811770872a52fb2321038a52de3b72

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=sha256:3895c057e405424696aace47ed46d8ed06f7346fc87a73ad97b0a2b5c27e8373

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

source=pdf_text observed=2026-08-06T23:49:56.095704Z digest=sha256:13097227dd0ef9f1f49cbda6db51d1a5ad1962595541c4227c70d074dfbbd757

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

source=pdf_text observed=2026-08-06T23:49:56.218916Z digest=sha256:8171eb17ab25a6fd9b7aa1dd5d0d341013591b77eedc854125536772f7484f7e

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=sha256:8ddd6a4c6321c7ac7c9db615b51543bc5a33134485f45c7f9ce6dac6aca3b6a8

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

source=pdf_text observed=2026-08-06T23:49:56.447287Z digest=sha256:eee1a0888026aa17fc1bcbca8bb1574afb47f8fa73dd6a2fe84850698d4c0698

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

source=pdf_text observed=2026-08-06T23:49:56.551359Z digest=sha256:ff771cdc77ed1fec7ada1d5b219681e544f8dfb0f8a03c3fbdec4a242cbaa50e

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

source=pdf_text observed=2026-08-06T23:49:56.678048Z digest=sha256:44dfd94a2ec2e812606cd5f5dad0ca7b298e821e1604c672e9d14f08bb17195e

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

source=pdf_text observed=2026-08-06T23:49:56.773847Z digest=sha256:90f2f721502c5fb1840a4ff34b360457f5871437dfdf07c1488e49dfd245c164

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=sha256:85b36267a34247e71baf3e8d395ab815e0fc69069a96fec7ecae73368f8f96c1

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

source=pdf_text observed=2026-08-06T23:49:57.057143Z digest=sha256:43602cf0b6a38ccafadbe109e113ccc3014c6b8beeb8b59f5e27cadbe29dba42

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=sha256:1049def47e3eacb305de84dca30c504c6ea5a4ae9ce2cc6a50e99aa241c8fbeb

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

source=pdf_text observed=2026-08-06T23:49:57.354245Z digest=sha256:94d7d6ee86216d99be5e06d6d26938ebfad7f56ba928969575200fdfbeafed83

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:bd8584d9534a862c8ce36412133aae3fb61b2ffbdb90721f8cdcaa2ecd985b3a

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

source=pdf_text observed=2026-06-29T04:13:18.570081Z digest=sha256:db646ef4fbbce59640d201f848b8c2748d48cb338c3b646b2a76527cf6643350