Paper Citation Record · LEDGER

Elucidating the Preconditioning in Consistency Distillation

As of 10 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2502.02922.

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

pith.paper-citation-record.v1
2502.02922 v3

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:42:54.436844Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

24 of 24 outbound references displayed

verified exact1
verified fuzzy7
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3f1a83bc-06f2-4c58-bf62-a40c1d175703 · outbound

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

Elucidating the Preconditioning in Consistency Distillation Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.348203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebe5e8e7-cd1f-417e-bd97-bf1db5a418ec · outbound

This paper cites For CIFAR-10 (unconditional), we train the model with a batch size of 256 for 200K iterations, which takes 5 days on 4 GPU cards.

Elucidating the Preconditioning in Consistency Distillation For CIFAR-10 (unconditional), we train the model with a batch size of 256 for 200K iterations, which takes 5 days on 4 GPU cards

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:42:54.773261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3a98f536-e7e9-43b1-ba37-c5d38a084ecd · outbound

This paper cites an unresolved cited work.

Elucidating the Preconditioning in Consistency Distillation Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-09T10:42:54.760002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f1f0222d-103d-49f9-bacd-564ec3ffabba · outbound

This paper cites SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models.

Elucidating the Preconditioning in Consistency Distillation SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-09T10:42:54.715686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e809624f-5f48-42e6-9d15-6cf7a267a91d · outbound

This paper cites For CIFAR-10 and FFHQ 64 ×64, we select N = 18and the maximum number of sampling steps as 17, i.e., not restricting the range of jumping from t to s.

Elucidating the Preconditioning in Consistency Distillation For CIFAR-10 and FFHQ 64 ×64, we select N = 18and the maximum number of sampling steps as 17, i.e., not restricting the range of jumping from t to s

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:42:54.786267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cb02c049-3141-4623-a289-15a2b3e1442b · outbound

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

Elucidating the Preconditioning in Consistency Distillation Learning multiple layers of features from tiny images

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:42:54.820788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 544373e1-b9ee-4261-b796-f15bf151f908 · outbound

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

Elucidating the Preconditioning in Consistency Distillation Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.387671Z

Source-reported events for the cited work

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Observation e2e02997-6bec-4372-a0f4-a798eed20598 · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

Elucidating the Preconditioning in Consistency Distillation Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.391535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c40f2c3e-835d-48d6-86fa-c4cefa141c68 · outbound

This paper cites On distillation of guided diffusion models.

Elucidating the Preconditioning in Consistency Distillation On distillation of guided diffusion models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:42:54.808879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 72270ef0-3771-4fa2-9f35-daf1ee34683e · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Elucidating the Preconditioning in Consistency Distillation Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.398771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1edeeb7-39ca-4653-bb06-6c1ad422727d · outbound

This paper cites Adversarial Diffusion Distillation.

Elucidating the Preconditioning in Consistency Distillation Adversarial Diffusion Distillation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.402741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 677cd170-9d33-4b20-8b2c-6139a4edeef2 · outbound

This paper cites Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation.

Elucidating the Preconditioning in Consistency Distillation Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.406306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6254b186-9aa9-48d2-ac46-68767c45f1d6 · outbound

This paper cites Sageatten- tion2: Efficient attention with thorough outlier smoothing and per-thread int4 quantization.

Elucidating the Preconditioning in Consistency Distillation Sageatten- tion2: Efficient attention with thorough outlier smoothing and per-thread int4 quantization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.417657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a045542d-42d1-4d03-9484-bc0ddf45c13a · outbound

This paper cites Sageattention: Accurate 8-bit attention for plug-and-play inference acceleration.

Elucidating the Preconditioning in Consistency Distillation Sageattention: Accurate 8-bit attention for plug-and-play inference acceleration

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.421488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88648f27-6542-4e13-bc89-8616473e0cbf · outbound

This paper cites Bidirectional Consistency Models.

Elucidating the Preconditioning in Consistency Distillation Bidirectional Consistency Models

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.384007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26c2d227-fc21-4546-9559-94d22772a8b0 · outbound

This paper cites VideoLCM: Video Latent Consistency Model.

Elucidating the Preconditioning in Consistency Distillation VideoLCM: Video Latent Consistency Model

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.413857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 084190aa-6b2b-4847-94df-d69ed9eff100 · outbound

This paper cites Photorealistic Video Generation with Diffusion Models.

Elucidating the Preconditioning in Consistency Distillation Photorealistic Video Generation with Diffusion Models

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.372159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c717f23-01ef-49d6-afe3-344f3d9bc161 · outbound

This paper cites Denoising diffusion implicit models.

Elucidating the Preconditioning in Consistency Distillation Denoising diffusion implicit models

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:42:54.797416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e63b98cd-701b-41a4-ab0c-2b5dc6cfcc73 · outbound

This paper cites Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling.

Elucidating the Preconditioning in Consistency Distillation Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.425027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4312522-0341-4388-b3d7-6a11df626e1a · outbound

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

Elucidating the Preconditioning in Consistency Distillation Imagen Video: High Definition Video Generation with Diffusion Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.376030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 524a2f72-68b9-46dc-8a60-2b137d02c270 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

Elucidating the Preconditioning in Consistency Distillation Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.363968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a361dd6-f0ce-436e-b9dc-e17e148355e5 · outbound

This paper cites ImageNet: A large-scale hier- archical image database.

Elucidating the Preconditioning in Consistency Distillation ImageNet: A large-scale hier- archical image database

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:42:54.831764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 56876d57-211f-4ee7-bd30-c04700dcca8a · outbound

This paper cites Weiss, Mohammad Norouzi, and William Chan.

Elucidating the Preconditioning in Consistency Distillation Weiss, Mohammad Norouzi, and William Chan

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:42:54.843498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d9c4e51a-10a0-4905-8836-b8f06583cc82 · outbound

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

Elucidating the Preconditioning in Consistency Distillation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T10:42:54.352660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.