Paper Citation Record · LEDGER

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2506.19741.

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

pith.paper-citation-record.v1
2506.19741 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:32:56.349831Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

43 of 43 outbound references displayed

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verified fuzzy11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 70de24b4-e8d4-4d38-804e-6353836d4930 · outbound

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

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 1

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Observation 78984f27-68ee-449e-a915-a4975fccea45 · outbound

This paper cites Deep unsuper- vised learning using nonequilibrium thermodynamics.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Deep unsuper- vised learning using nonequilibrium thermodynamics

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.185121Z

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Observation f9ed782e-85b9-48dc-877f-ac254ff4fa77 · outbound

This paper cites Diff- instruct: A universal approach for transferring knowledge from pre-trained diffusion models.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Diff- instruct: A universal approach for transferring knowledge from pre-trained diffusion models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:56.848991Z

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Observation 6601b5b7-92fe-4348-b660-c85794ccee54 · outbound

This paper cites You only sample once: Taming one-step text-to-image synthesis by self-cooperative diffusion gans, 2024.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls You only sample once: Taming one-step text-to-image synthesis by self-cooperative diffusion gans, 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:56.835054Z

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Observation 3a536a7f-b695-4844-aef0-67f588f78fd0 · outbound

This paper cites One-step Diffusion with Distribution Matching Distillation.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls One-step Diffusion with Distribution Matching Distillation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.198552Z

Source-reported events for the cited work

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Observation 483c90fd-b608-426a-a083-42b2369d4441 · outbound

This paper cites Consistency models, 2023.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Consistency models, 2023

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.203014Z

Source-reported events for the cited work

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Observation bfddafbc-59d2-4751-b6a3-0259d890b905 · outbound

This paper cites Inductive Moment Matching.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Inductive Moment Matching

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.207568Z

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Observation f3cffb77-a948-4bd9-b9a7-089d66f12bd7 · outbound

This paper cites Sdxs: Real-time one-step latent diffusion models with image conditions.arxiv, 2024.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Sdxs: Real-time one-step latent diffusion models with image conditions.arxiv, 2024

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:56.813416Z

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Observation dd67453e-a715-4260-96cb-ce985a652c81 · outbound

This paper cites Adding Additional Control to One-Step Diffusion with Joint Distribution Matching.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Adding Additional Control to One-Step Diffusion with Joint Distribution Matching

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:32:56.523841Z

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Observation 3954d3b9-f6c6-4fe0-a69a-1909befc07b8 · outbound

This paper cites T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.220118Z

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Observation 998ea275-e9b7-4f3a-8381-8fb7acdf0a97 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Classifier-Free Diffusion Guidance

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.223872Z

Source-reported events for the cited work

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Observation f56a20b2-cd74-448e-b111-502ac15ee693 · outbound

This paper cites Universal guidance for diffusion models.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Universal guidance for diffusion models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.227725Z

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Observation a48d787c-f1d7-45eb-b5b3-b5623f4c5f65 · outbound

This paper cites Elucidating The Design Space of Classifier-Guided Diffusion Generation.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Elucidating The Design Space of Classifier-Guided Diffusion Generation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.231768Z

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Observation 46245065-a848-45a6-b693-3fc82d3b1292 · outbound

This paper cites Reward-Instruct: A Reward-Centric Approach to Fast Photo-Realistic Image Generation.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Reward-Instruct: A Reward-Centric Approach to Fast Photo-Realistic Image Generation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.235464Z

Source-reported events for the cited work

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Observation 76cadc90-7f32-49a4-a023-a8729f982faf · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Adding conditional control to text-to-image diffusion models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:56.791949Z

Source-reported events for the cited work

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Observation 806c2ccd-fc6d-483f-9d9e-31e8e4121ffd · outbound

This paper cites A kernel two-sample test.The Journal of Machine Learning Research, 13(1):723–773, 2012.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls A kernel two-sample test.The Journal of Machine Learning Research, 13(1):723–773, 2012

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.242780Z

Source-reported events for the cited work

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Observation acf30efd-a9a0-4d3f-bfbb-37fc64e79f08 · outbound

This paper cites Integral probability metrics and their generating classes of functions.Advances in applied probability, 29(2):429–443, 1997.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Integral probability metrics and their generating classes of functions.Advances in applied probability, 29(2):429–443, 1997

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.246734Z

Source-reported events for the cited work

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Observation 41fddc56-6428-478a-8e64-b5fc9adf19b4 · outbound

This paper cites DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.250742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc944a0f-320b-486c-ba60-efc5686de891 · outbound

This paper cites Unipc: A unified predictor- corrector framework for fast sampling of diffusion models.Advances in Neural Information Processing Systems, 36:49842–49869, 2023.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Unipc: A unified predictor- corrector framework for fast sampling of diffusion models.Advances in Neural Information Processing Systems, 36:49842–49869, 2023

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:56.763322Z

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Observation e630c97a-0ac0-43f1-8208-19c17fa0db26 · outbound

This paper cites Accelerating diffusion sampling with optimized time steps.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Accelerating diffusion sampling with optimized time steps

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.258885Z

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Observation 01c1b2a1-54d9-4e27-89a2-5080234a1662 · outbound

This paper cites Freeu: Free lunch in diffusion u-net.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Freeu: Free lunch in diffusion u-net

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.262806Z

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Observation bc178045-f746-43c9-b3a5-2f0d6497f234 · outbound

This paper cites The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.266913Z

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Observation 0baeef7e-c44a-45c8-8f31-35145e956b96 · outbound

This paper cites Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.271158Z

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Observation 55b1f3f6-9ef4-4559-8311-28366dac4c6f · outbound

This paper cites On distillation of guided diffusion models.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls On distillation of guided diffusion models

Reference 24

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Observation 3455375a-6dae-43a5-bd52-59767ddf6b7f · outbound

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

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Progressive distillation for fast sampling of diffusion models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.279035Z

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Observation abcd84ab-bb56-441f-9eaa-145a764095fe · outbound

This paper cites Consistency models.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Consistency models

Reference 26

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Observation 67db3043-5bd6-4c36-ab05-aac267174c16 · outbound

This paper cites Improved techniques for training consistency models.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Improved techniques for training consistency models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.286805Z

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Observation d4059fdf-3536-4416-a141-754920ec4a8c · outbound

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

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator

Reference 28

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Observation e5875c01-5f30-4434-9140-1bd45e6ebe33 · outbound

This paper cites Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:56.700817Z

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Observation 2c09db3d-5da4-4228-989f-899e3dbacacb · outbound

This paper cites CCM: Real-time controllable visual content creation using text-to-image consistency models.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls CCM: Real-time controllable visual content creation using text-to-image consistency models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:56.687734Z

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Observation caf47c79-2286-48df-bf2a-b38f9de6d6f6 · outbound

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Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Unresolved cited work

Reference 31

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Observation f354b04f-c167-4f26-a50a-66b3b30f7398 · outbound

This paper cites Learning few-step diffusion models by trajectory distribution matching, 2025.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Learning few-step diffusion models by trajectory distribution matching, 2025

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:56.666101Z

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Observation cf6ed992-4643-41f3-a8be-c58c00e28daa · outbound

This paper cites InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation

Reference 33

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Observation 036bd254-c36d-414e-9e57-251686e02f71 · outbound

This paper cites Constrained Learning with Non-Convex Losses.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Constrained Learning with Non-Convex Losses

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:32:56.388013Z

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Observation 64763f9e-fc6e-483c-96a2-e4cb0ab5bc9e · outbound

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

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls High- resolution image synthesis with latent diffusion models

Reference 35

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Observation 95a58839-a45c-4a8c-bc57-30bb1bf2f7b7 · outbound

This paper cites A computational approach to edge detection.PAMI, 1986.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls A computational approach to edge detection.PAMI, 1986

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:56.644367Z

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Observation 1db5db44-d9ef-4d39-b069-d4d331c82cea · outbound

This paper cites Holistically-nested edge detection.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Holistically-nested edge detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:56.631132Z

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Observation 9829f5c6-ada4-46db-9268-6ac0cf6e796d · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017

Reference 38

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Observation f84ae825-36be-4ef9-a4e0-fc5e42ff2cf6 · outbound

This paper cites Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:56.335158Z

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Observation 32f865ad-8a3e-45e0-9801-7b9c2dcf58d8 · outbound

This paper cites Microsoft coco: Common objects in context.

Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Microsoft coco: Common objects in context

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:32:56.602330Z

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Observation 570c73ee-64e9-4490-b9c9-433d7922d6db · outbound

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Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:32:56.589363Z

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Observation e63aace3-5492-4c19-9b16-5b351d018527 · outbound

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Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:32:56.576394Z

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Observation 5ca575a4-7c38-437d-b999-794fc1128d5e · outbound

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Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls Unresolved cited work

Reference 128

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:32:56.563790Z

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