Paper Citation Record · LEDGER

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling

As of 13 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2506.08796.

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

pith.paper-citation-record.v1
2506.08796 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:11:23.846336Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:54:34.268284Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

55 of 55 outbound references displayed

verified exact0
verified fuzzy28
unresolved27
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 89616f2e-598d-48db-b853-0f142601a95f · outbound

This paper cites Neural flow diffusion models: Learnable forward process for improved diffusion modelling.Advances in Neural Information Processing Systems, 37:73952–73985, 2024.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Neural flow diffusion models: Learnable forward process for improved diffusion modelling.Advances in Neural Information Processing Systems, 37:73952–73985, 2024

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.604324Z

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Observation 5f5a7f37-3e98-4cfe-9d6c-69adb8c17909 · outbound

This paper cites Fluxspace: Disentangled semantic editing in rectified flow transformers, 2024.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Fluxspace: Disentangled semantic editing in rectified flow transformers, 2024

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.590170Z

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Observation e6c9fa45-0d98-4777-aa84-3182a4d071a3 · outbound

This paper cites Flow matching in latent space, 2023.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Flow matching in latent space, 2023

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.575989Z

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Observation 1e49eadd-6fbb-44ff-8097-b63c0f9c6c11 · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Scaling rectified flow transformers for high-resolution image synthesis

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.561712Z

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Observation a1a3e640-954d-4711-a7d7-143ab091047b · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis, 2024.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Scaling rectified flow transformers for high-resolution image synthesis, 2024

Reference 5

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Observation 4d5bf8db-a58a-4cb3-972d-c0c5cfc65b29 · outbound

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Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:11:24.538012Z

Source-reported events for the cited work

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Observation 834c72c0-1c59-4943-81c3-cad066d8989b · outbound

This paper cites Seeds: Exponential sde solvers for fast high-quality sampling from diffusion models.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Seeds: Exponential sde solvers for fast high-quality sampling from diffusion models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.522756Z

Source-reported events for the cited work

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Observation 85b5bf1a-2c68-4956-9e49-663f0008438a · outbound

This paper cites an unresolved cited work.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:11:24.507803Z

Source-reported events for the cited work

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Observation d24f68cc-f8e8-4b47-96c0-53715757e89c · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 9

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Source-reported events for the cited work

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Observation f77008ec-b1ec-432a-9075-68a4ccf7e5ae · outbound

This paper cites Progressive growing of gans for improved quality, stability, and variation.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Progressive growing of gans for improved quality, stability, and variation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.483612Z

Source-reported events for the cited work

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Observation 7c1fc18e-f2be-4cbd-b1c8-2aeab8879c87 · outbound

This paper cites Denoising Diffusion Restoration Models.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Denoising Diffusion Restoration Models

Reference 11

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Observation 9e795c1d-ad42-45f5-8b4e-66385d4f15a6 · outbound

This paper cites Learning multiple layers of features from tiny images.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Learning multiple layers of features from tiny images

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.469679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ee3a2b87-a384-4eaa-a741-c67119ba86ae · outbound

This paper cites Flux.https://github.com/black-forest-labs/flux, 2024.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Flux.https://github.com/black-forest-labs/flux, 2024

Reference 13

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Observation 1b025ab9-fc4f-4082-9aa1-6aacd9561139 · outbound

This paper cites Improving the training of rectified flows.Advances in Neural Information Processing Systems, 37:63082–63109, 2024.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Improving the training of rectified flows.Advances in Neural Information Processing Systems, 37:63082–63109, 2024

Reference 14

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Source-reported events for the cited work

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Observation a9982825-2175-47c6-9516-c13884f262fe · outbound

This paper cites Distrifusion: Distributed parallel inference for high-resolution diffusion models.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Distrifusion: Distributed parallel inference for high-resolution diffusion models

Reference 15

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Observation 15a4dd1b-914f-496e-b937-f7e16d60ea1b · outbound

This paper cites Faster diffusion: Rethinking the role of the encoder for diffusion model inference.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Faster diffusion: Rethinking the role of the encoder for diffusion model inference

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.424661Z

Source-reported events for the cited work

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Observation 9ebe2114-334c-42b0-ac95-61f35730573a · outbound

This paper cites OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows

Reference 17

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Source-reported events for the cited work

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Observation bf038fbf-b04f-413b-a917-aefea90d56f1 · outbound

This paper cites Snapfusion: Text-to-image diffusion model on mobile devices within two seconds.Advances in Neural Information Processing Systems, 36:20662–20678, 2023.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Snapfusion: Text-to-image diffusion model on mobile devices within two seconds.Advances in Neural Information Processing Systems, 36:20662–20678, 2023

Reference 18

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Observation 369de069-21a0-4893-be42-84c2d5243c55 · outbound

This paper cites Flow matching for generative modeling.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Flow matching for generative modeling

Reference 19

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Observation 2567896d-a7d5-49b9-b798-c2b4f0d886e2 · outbound

This paper cites Pseudo numerical methods for diffusion models on manifolds, 2022.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Pseudo numerical methods for diffusion models on manifolds, 2022

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.390712Z

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Observation d9ae3da3-29a1-4709-8806-3a1c6825bddf · outbound

This paper cites Rfwave: Multi-band rectified flow for audio waveform reconstruction, 2024.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Rfwave: Multi-band rectified flow for audio waveform reconstruction, 2024

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.376580Z

Source-reported events for the cited work

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Observation 47d32364-029c-4b1e-86f7-07f20d56ae07 · outbound

This paper cites Rectified flow: A marginal preserving approach to optimal transport, 2022.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Rectified flow: A marginal preserving approach to optimal transport, 2022

Reference 22

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Observation 2d2adf6c-a866-4ed4-9741-149d07f9b822 · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 23

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Source-reported events for the cited work

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Observation ec57d495-ffc4-4ec4-b2df-5a5556b0d9d3 · outbound

This paper cites Instaflow: One step is enough for high-quality diffusion-based text-to-image generation.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Instaflow: One step is enough for high-quality diffusion-based text-to-image generation

Reference 24

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Source-reported events for the cited work

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Observation c97fb4c6-0ad9-42c1-becc-0a7c34fd66c4 · outbound

This paper cites Decoupled Weight Decay Regularization.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Decoupled Weight Decay Regularization

Reference 25

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Observation edde770a-29af-4785-bf4a-48f577ffb642 · outbound

This paper cites Flowdiffuser: Advancing optical flow estimation with diffusion models.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Flowdiffuser: Advancing optical flow estimation with diffusion models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.332478Z

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Observation a57abba6-ccab-4d03-9eda-dce845e9353f · outbound

This paper cites Deepcache: Accelerating diffusion models for free.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Deepcache: Accelerating diffusion models for free

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.318719Z

Source-reported events for the cited work

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Observation b9a527c0-c939-40d2-a9fb-5393560b32ea · outbound

This paper cites Safe-sd: Safe and traceable stable diffusion with text prompt trigger for invisible generative watermarking.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Safe-sd: Safe and traceable stable diffusion with text prompt trigger for invisible generative watermarking

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.304268Z

Source-reported events for the cited work

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Observation 81833386-4f50-431e-9b16-f36ecfd91532 · outbound

This paper cites Adapedit: Spatio-temporal guided adaptive edit- ing algorithm for text-based continuity-sensitive image editing.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Adapedit: Spatio-temporal guided adaptive edit- ing algorithm for text-based continuity-sensitive image editing

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.288617Z

Source-reported events for the cited work

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Observation 8d2c3c4e-61d4-4deb-89cd-02b0a1c6d7dc · outbound

This paper cites Lmd: faster image reconstruction with latent masking diffusion.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Lmd: faster image reconstruction with latent masking diffusion

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.273913Z

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Observation d395b500-696a-4c4b-90be-3fbb9895e34d · outbound

This paper cites Efficient diffusion models: A comprehensive survey from principles to practices.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Efficient diffusion models: A comprehensive survey from principles to practices.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.259721Z

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Observation c87db13d-eff5-437c-a85e-3b3c340961fb · outbound

This paper cites Neural residual diffusion models for deep scalable vision generation.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Neural residual diffusion models for deep scalable vision generation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.245519Z

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Observation 041a6859-9730-482a-ad8a-0a28552bc7d7 · outbound

This paper cites On distillation of guided diffusion models.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling On distillation of guided diffusion models

Reference 33

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Observation 3dd72b44-a97c-4c61-8da9-dc194c795432 · outbound

This paper cites Improved denoising diffusion probabilistic models.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Improved denoising diffusion probabilistic models

Reference 34

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Observation df412493-08cb-43e8-9578-e73bf11d5284 · outbound

This paper cites Posterior-mean rectified flow: Towards minimum mse photo-realistic image restoration, 2025.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Posterior-mean rectified flow: Towards minimum mse photo-realistic image restoration, 2025

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.206987Z

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Observation 72dbd207-63bc-49ca-9f88-9820a09aa632 · outbound

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Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Unresolved cited work

Reference 36

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Observation 76f73e91-0aef-47e6-a12c-45cb188dd0ff · outbound

This paper cites Progressive distillation for fast sampling of diffusion models, 2022.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Progressive distillation for fast sampling of diffusion models, 2022

Reference 37

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Observation 74fd5730-85ea-44dd-a030-ff2623a71386 · outbound

This paper cites Adversarial diffusion distillation.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Adversarial diffusion distillation

Reference 38

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Observation 450b5b3e-c338-413a-9cd8-eede0a9c6b7b · outbound

This paper cites pytorch-fid: FID Score for PyTorch.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling pytorch-fid: FID Score for PyTorch

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.165196Z

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Observation d3451736-e306-482e-a0fc-234bf7ca80c8 · outbound

This paper cites Denoising Diffusion Implicit Models.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Denoising Diffusion Implicit Models

Reference 40

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Observation d3750c51-1bbe-4209-aa43-19e82ed22498 · outbound

This paper cites Consistency models.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Consistency models

Reference 41

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Observation e32fef4b-5cf3-464e-8595-ae7ba549f06a · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.Advances in neural information processing systems, 32, 2019.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Generative modeling by estimating gradients of the data distribution.Advances in neural information processing systems, 32, 2019

Reference 42

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Observation 2fec302b-bda8-4583-b64b-5ccfcbe31118 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Score-Based Generative Modeling through Stochastic Differential Equations

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T05:11:23.794001Z

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Observation dcd930f4-864a-4892-84eb-744791956cc4 · outbound

This paper cites Rectified diffusion: Straightness is not your need in rectified flow, 2024.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Rectified diffusion: Straightness is not your need in rectified flow, 2024

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.129688Z

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Observation f98c5121-73a9-49c6-9be2-274011ef835f · outbound

This paper cites Taming rectified flow for inversion and editing, 2024.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Taming rectified flow for inversion and editing, 2024

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.115732Z

Source-reported events for the cited work

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Observation 242994c7-bf65-45a1-83d1-46affb09d52d · outbound

This paper cites Frieren: Efficient video-to-audio generation with rectified flow matching.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Frieren: Efficient video-to-audio generation with rectified flow matching

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.100805Z

Source-reported events for the cited work

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Observation c88eb2be-6e2d-4662-bbc2-29360710e2ec · outbound

This paper cites Ufogen: You forward once large scale text-to-image generation via diffusion gans.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Ufogen: You forward once large scale text-to-image generation via diffusion gans

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.086574Z

Source-reported events for the cited work

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Observation eff88fd4-7a3a-4fe9-ab64-5866491075df · outbound

This paper cites PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator

Reference 48

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Observation 082e0d45-95ca-4195-af35-701ad04d2320 · outbound

This paper cites Improving training efficiency of diffusion models via multi-stage framework and tailored multi-decoder architecture.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Improving training efficiency of diffusion models via multi-stage framework and tailored multi-decoder architecture

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.072732Z

Source-reported events for the cited work

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Observation d8ab1d4e-4a88-4ef4-a02b-c6fd8b7c371c · outbound

This paper cites Mobilediffusion: Instant text-to-image generation on mobile devices.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Mobilediffusion: Instant text-to-image generation on mobile devices

Reference 50

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This paper cites Flowie: Efficient image enhancement via rectified flow.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Flowie: Efficient image enhancement via rectified flow

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:24.041094Z

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Observation 1e34f98b-109d-497b-85c1-fb288f670826 · outbound

This paper cites an unresolved cited work.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:11:24.025718Z

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Observation 16a2869b-d5e1-488e-a96d-b20908266fa0 · outbound

This paper cites an unresolved cited work.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:11:24.010880Z

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Observation f37e3326-4cad-40be-8ced-72199bb04eec · outbound

This paper cites an unresolved cited work.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:11:23.996955Z

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Observation ce6c8b0a-e3f7-4a67-bf27-25a013fda001 · outbound

This paper cites During training, images are normalized to have zero mean and unit variance.

Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling During training, images are normalized to have zero mean and unit variance

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:23.981826Z

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Pith citing papers

Observation 5960989f-e7ff-4ba3-a402-d879a844ca2d · inbound

Deep Neural Networks Inspired by Differential Equations cites this paper.

Deep Neural Networks Inspired by Differential Equations Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling

Reference 167

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