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Paper Citation Record · LEDGER

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing

As of 9 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.05934.

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

pith.paper-citation-record.v1
2506.05934 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:19:08.184540Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

  • verified exact1
  • verified fuzzy19
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation db692c20-a3c8-4852-9deb-37af60d62c3f · outbound

This paper cites Blended diffusion for text-driven editing of natural images.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Blended diffusion for text-driven editing of natural images

Reference 1

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source=pdf_text observed=2026-08-07T10:19:03.478920Z digest=sha256:dd3073ef33d58f5de94c7440cd74fd91d1ca51055d45abdb791dcda1599d4b77

Observation 832a6ef4-b1f8-436b-9b7c-8c0540a3a82d · outbound

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

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2

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source=pdf_text observed=2026-08-07T10:19:03.643392Z digest=sha256:52c4fe80b364df2c91d56a9a54a0d3f2740301ec62ccaa928371174747fef02e

Observation e8ed28c1-cc6d-416e-9053-1ac65b93a051 · outbound

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

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing In- structpix2pix: Learning to follow image editing instructions

Reference 3

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:03.855180Z digest=sha256:e4aae5cd02c181d218090e5d186cce2f37b982192e805550e55141332f386c61

Observation 1c99b18f-c93a-4fc2-a372-240b67f1a025 · outbound

This paper cites Video generation models as world simu- lators.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Video generation models as world simu- lators

Reference 4

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source=pdf_text observed=2026-08-07T10:19:04.029028Z digest=sha256:6b79ad5b09d38cbc3fe83144cc959071e4cb9b13862faa89dbe53c0c531d4c0f

Observation bf34d4f6-dba7-4ab0-b8d8-ee609ba01233 · outbound

This paper cites Masactrl: Tuning-free mu- tual self-attention control for consistent image synthesis and editing.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Masactrl: Tuning-free mu- tual self-attention control for consistent image synthesis and editing

Reference 5

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source=pdf_text observed=2026-08-07T10:19:04.180450Z digest=sha256:e570493b99c1196339bd921d14f46f71b26edb004baaedb11bb2cda7a1cfa6c6

Observation 5232c960-e01e-4fa2-af64-2d7e254d1e09 · outbound

This paper cites Diffusion mod- els beat gans on image synthesis.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Diffusion mod- els beat gans on image synthesis

Reference 6

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source=pdf_text observed=2026-08-07T10:19:04.368543Z digest=sha256:fc6f3d76f403debef95ddfb52d1c17f4a8e1df7eca3826e0ef779400772524f0

Observation 496630e3-991c-49ed-923a-aab74dce2da1 · outbound

This paper cites TokenFlow: Consistent Diffusion Features for Consistent Video Editing.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing TokenFlow: Consistent Diffusion Features for Consistent Video Editing

Reference 7

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source=pdf_text observed=2026-08-07T10:19:04.506663Z digest=sha256:e2e58693f7dda737b87b171231901fbd89230ca353aed2b1a819e9b7be7f2ddd

Observation 6b853151-fbfe-4104-9911-6b12fa6eab31 · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 8

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source=pdf_text observed=2026-08-07T10:19:04.637891Z digest=sha256:1065989b5e83c9d02e95d07bd0c3ccfabbc9a7c64ed6db01b6bf4167295f9639

Observation ed7b6033-a2ee-48e8-b8ea-1319dc9cad69 · outbound

This paper cites Denoising diffu- sion probabilistic models.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Denoising diffu- sion probabilistic models

Reference 9

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source=pdf_text observed=2026-08-07T10:19:04.790362Z digest=sha256:96d12adb9345618d6f8451db655ea730a849abb6251f65d84f480593c6b6af1b

Observation 92184380-9a3e-43b7-9b83-91f6786f9bde · outbound

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

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Imagen Video: High Definition Video Generation with Diffusion Models

Reference 10

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source=pdf_text observed=2026-08-07T10:19:04.949846Z digest=sha256:fb3758b85ac5d35db629222ad0486a84e9e08515d7d7957cdc39fdeb13f51bf1

Observation 91f1a1dc-e477-4bf3-8a1e-ae32cce936ee · outbound

This paper cites Video dif- fusion models.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Video dif- fusion models

Reference 11

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source=pdf_text observed=2026-08-07T10:19:05.049316Z digest=sha256:b4ca1565636c815845c38f4101356deb5c0797f3d2b6f8b0e8cf50bb6853110b

Observation 951529a9-b87d-438a-8509-17cfd083a5cd · outbound

This paper cites Video dif- fusion models.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Video dif- fusion models

Reference 12

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source=pdf_text observed=2026-08-07T10:19:05.236022Z digest=sha256:82c41c93e35c376efd81b1afba8fe2aedbd676da7507dcf7bd62d6463c7a5954

Observation 2c9a2b91-8962-44be-8842-63183fa8d461 · outbound

This paper cites Imagic: Text-based real image editing with diffusion models.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Imagic: Text-based real image editing with diffusion models

Reference 13

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source=pdf_text observed=2026-08-07T10:19:05.356618Z digest=sha256:1f481e18d369f0eca61264d4afd868c077bfc42278b95ee8d49067956d77b144

Observation 343c1462-f542-48ab-a8b4-992900d1f20d · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 14

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source=pdf_text observed=2026-08-07T10:19:05.476134Z digest=sha256:a833fc9dfaa18915ff61f4bd032f944a16d6907815e0af044957707cc4103cff

Observation 9c13f280-c937-467e-b9f7-28fe2b187b1e · outbound

This paper cites Video-p2p: Video editing with cross-attention control.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Video-p2p: Video editing with cross-attention control

Reference 15

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source=pdf_text observed=2026-08-07T10:19:05.622236Z digest=sha256:5eff8d4e613018ee3c25e8bd9efd48410269236870bb2a00c53c7365e937302d

Observation a76149a9-8926-448b-9a87-8880257ea169 · outbound

This paper cites Latte: Latent Diffusion Transformer for Video Generation.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Latte: Latent Diffusion Transformer for Video Generation

Reference 16

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source=pdf_text observed=2026-08-07T10:19:05.798152Z digest=sha256:2e38df8056931ad663b4c4c344f59b1b8270829cb67c1e37c82ea30f6f03ab16

Observation 0e1c0273-7406-420d-beef-7a67493a5b1e · outbound

This paper cites Sdedit: Guided image synthesis and editing with stochastic differential equa- tions.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Sdedit: Guided image synthesis and editing with stochastic differential equa- tions

Reference 17

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source=pdf_text observed=2026-08-07T10:19:05.946367Z digest=sha256:45a26e199379fcd3cb7ca05b5ad9d66d54eb0162a9f7be5b39d1a9183bc7a007

Observation 134e72a0-fd6b-49d2-9905-1031ebadd6e1 · outbound

This paper cites Null-text inversion for editing real images using guided diffusion models.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Null-text inversion for editing real images using guided diffusion models

Reference 18

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source=pdf_text observed=2026-08-07T10:19:06.054702Z digest=sha256:b1a0689b6efc2031d4f63247293a04adb65925aee73e8675dc512834eabd5f0c

Observation 580e706b-153f-44e9-a01b-e5d81297e334 · outbound

This paper cites Dreamix: Video Diffusion Models are General Video Editors.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Dreamix: Video Diffusion Models are General Video Editors

Reference 19

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source=pdf_text observed=2026-08-07T10:19:06.151039Z digest=sha256:46e8d1840e6a5f45ec6bb5b0c0b0fb537ad74066100024b4010660162c7b1449

Observation 5f2b93ed-92f1-45f6-9742-4daaa3750809 · outbound

This paper cites Zero-shot image-to-image translation.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Zero-shot image-to-image translation

Reference 20

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:06.257761Z digest=sha256:34190b7b721e42ea3e983268a4b998a1a255ad7f625739d91fb879039f9e8362

Observation c54bbec5-e7f6-4c3d-a340-74ea357175c1 · outbound

This paper cites Scalable diffusion models with transformers.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Scalable diffusion models with transformers

Reference 21

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:06.377616Z digest=sha256:2047cea9655b44ae24d549bcc9a44510f3f6e2b3c10ab0b1cb51d63a22c01068

Observation 46bf6358-31b4-4db6-9d55-61b0a42d908b · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing The 2017 DAVIS Challenge on Video Object Segmentation

Reference 22

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source=pdf_text observed=2026-08-07T10:19:06.504730Z digest=sha256:d12ee3b354418df68bd1b53fec565259822b2154dbaa39f7ec35a5acff217b4e

Observation b12d2c16-ac26-4d31-9549-e0029e48b6a4 · outbound

This paper cites Fatezero: Fus- ing attentions for zero-shot text-based video editing.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Fatezero: Fus- ing attentions for zero-shot text-based video editing

Reference 23

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:06.588722Z digest=sha256:2c7462055cd1ac713af3966b19ea674042e9370baf6caab18fe6e3768d0367fe

Observation d26d0e87-8c74-436f-9770-f07054680081 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Learn- ing transferable visual models from natural language super- vision

Reference 24

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:06.683218Z digest=sha256:4c9e9bcf9919279af35bd0c9cf2e5c89d6c47911086df1b4208f0e4b4e6e275b

Observation 7b2ed3be-d619-4506-8e21-380b0f6e197c · outbound

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

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing High-resolution image syn- thesis with latent diffusion models

Reference 25

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source=pdf_text observed=2026-08-07T10:19:06.769852Z digest=sha256:b759a428d0efb1748d705d353a6f0b1934db1d6fa7f7e1868e899591c086b656

Observation 5a0c3715-3de1-4a30-8b63-aff7f867382b · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing U-net: Convolutional networks for biomedical image segmentation

Reference 26

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source=pdf_text observed=2026-08-07T10:19:06.869319Z digest=sha256:c314622ff072d4422d2829705c2727cddba716c05cce117b27bd0542a2a9e333

Observation 1d28bcca-6a64-4db8-8967-056959b88607 · outbound

This paper cites Edit-a-video: Single video editing with object-aware consistency.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Edit-a-video: Single video editing with object-aware consistency

Reference 27

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source=pdf_text observed=2026-08-07T10:19:06.966198Z digest=sha256:efedc4da94ea9a50fc03375b9c8db34655fc61917b34f826bae02bc519f92b4e

Observation 85521acf-c599-40fd-895b-232d0898e7ba · outbound

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

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 28

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source=pdf_text observed=2026-08-07T10:19:07.077392Z digest=sha256:4617e12831a7102886b41d047fd6bbcba45d05c17ba10a1916ba9f85b150f48d

Observation 93d29866-473f-437d-aaa7-719da92ed9d5 · outbound

This paper cites Denois- ing diffusion implicit models.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Denois- ing diffusion implicit models

Reference 29

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:07.140497Z digest=sha256:2c338e0b342550e1da63feb4f5e41b92fff58bf8aa92b420b23fc3ae10830e60

Observation f9698051-03a2-4865-ae22-fd4e0eff5a3d · outbound

This paper cites Guide-and-Rescale: Self-Guidance Mechanism for Effective Tuning-Free Real Image Editing.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Guide-and-Rescale: Self-Guidance Mechanism for Effective Tuning-Free Real Image Editing

Reference 30

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local_arxiv, observed 2026-08-07T10:19:08.463865Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:07.247237Z digest=sha256:af66eb2464ddcfcd6e5a47ceea796a8a9850c2167c438fb048667a456339d7e7

Observation c1237889-b656-4685-a507-6bb920794a2b · outbound

This paper cites Plug-and-play diffusion features for text-driven image-to-image translation.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Plug-and-play diffusion features for text-driven image-to-image translation

Reference 31

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source=pdf_text observed=2026-08-07T10:19:07.351249Z digest=sha256:d48a56afb53595839555bcf46ee6d03f65d732bf1455a9d111a450939bd7a29f

Observation 0493e568-c121-479d-86ea-12339d3461f1 · outbound

This paper cites Phenaki: Variable length video generation from open domain textual descriptions.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Phenaki: Variable length video generation from open domain textual descriptions

Reference 32

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raw_fallback, observed 2026-08-07T10:19:09.612868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:07.464678Z digest=sha256:f80bfe0df9c9f06bc3c105c8599aa81d05bdc369393a70a032e1ab35a056e9db

Observation 8020d1fc-c6dd-46ea-97b5-a46faefbec06 · outbound

This paper cites Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models

Reference 33

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source=pdf_text observed=2026-08-07T10:19:07.565705Z digest=sha256:cb504438dc273c2ef8d5e4a515ae05c75087a7cb928f8ad0e758ea5d94645484

Observation ee46637e-616d-4ba3-8947-8e9f8dee71fe · outbound

This paper cites Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

Reference 34

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:07.663134Z digest=sha256:46442cae1707a83432f769617984af8c8c67b21728bf077da8e7023f04681d20

Observation 0f40527c-d616-455c-bb7e-283c77d3f925 · outbound

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

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 35

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source=pdf_text observed=2026-08-07T10:19:07.787108Z digest=sha256:068e2f13f181205ae9eace87a99136427e0f8fff557df82edd4ca210d3533990

Observation 2b12b871-0d75-4c28-85ee-ca45797e5152 · outbound

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

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing The unreasonable effectiveness of deep features as a perceptual metric

Reference 36

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no resolver link, observed 2026-08-07T10:19:07.881118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:07.881118Z digest=sha256:5029e41f6810dcaaf91006be5e4e90a4a11b2a20f1628f4ba52523838df553d6

Observation 40d88a7c-bedf-4f33-bd9c-e3482708fe9f · outbound

This paper cites Open-sora: Democratizing efficient video production for all.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Open-sora: Democratizing efficient video production for all

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:19:09.191248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:07.960797Z digest=sha256:d90e63ca9c3f524d76a5f7f25821ddafa05bc697b1e4faad67a299d01e7ef865

Observation 338153ab-2662-4847-ab01-51a95173f9b1 · outbound

This paper cites Detecting twenty-thousand classes using image-level supervision.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing Detecting twenty-thousand classes using image-level supervision

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:19:08.996401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:08.097733Z digest=sha256:99021775d6f8d333cf96b3dd159e6f3f25ab252f06fb6e422978ca612dc8f2f2

Observation 85a95c9a-9186-4d28-ac40-a7aee9e33d20 · outbound

This paper cites taxi”+“autumn.

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing taxi”+“autumn

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:19:08.721804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:08.184540Z digest=sha256:e7fd7629f7bd87a5b842e27fd0d70d1717dc20f4a2a10e718a3bf1a767680c56

Pith citing papers

No inbound Pith citation observations are available.