Paper Citation Record · LEDGER

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation

As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2506.09376.

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

pith.paper-citation-record.v1
2506.09376 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:55:40.743098Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-14T20:09:07.959955Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T20:09:26.116298Z

Reference resolution

31 of 31 outbound references displayed

verified exact1
verified fuzzy5
unresolved22
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 22f7f860-29e0-4532-a526-eb238ef33868 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Classifier-Free Diffusion Guidance

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:37.419643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26f6b1b4-cc56-445b-a6c6-8e199632f492 · outbound

This paper cites Plug-and-Play Diffusion Distillation.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Plug-and-Play Diffusion Distillation

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T04:55:41.786371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3da6a85-006a-45e0-a752-55e5d47bc221 · outbound

This paper cites FouriScale: A Frequency Perspective on Training-Free High-Resolution Image Synthesis.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation FouriScale: A Frequency Perspective on Training-Free High-Resolution Image Synthesis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:37.749567Z

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Observation d99ed275-809b-470a-a7b5-4e574f3815bc · outbound

This paper cites Distilling Diffusion Models into Conditional GANs.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Distilling Diffusion Models into Conditional GANs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:37.923495Z

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Observation 401ea604-c5b7-4412-a884-81f4ca865330 · outbound

This paper cites Diffusion Model Compression for Image-to-Image Translation.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Diffusion Model Compression for Image-to-Image Translation

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T04:55:41.618104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 53ab0928-9459-4405-bb52-87bed35c0746 · outbound

This paper cites The Role of ImageNet Classes in Fr\'echet Inception Distance.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation The Role of ImageNet Classes in Fr\'echet Inception Distance

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:38.244730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26d16f3c-678f-4e8b-919c-a5e136df6033 · outbound

This paper cites Spectrum Translation for Refinement of Image Generation (STIG) Based on Contrastive Learning and Spectral Filter Profile.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Spectrum Translation for Refinement of Image Generation (STIG) Based on Contrastive Learning and Spectral Filter Profile

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T04:55:41.254074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 17014c97-357a-44e7-ab63-021834a77e1a · outbound

This paper cites LCM-LoRA: A Universal Stable-Diffusion Acceleration Module.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation LCM-LoRA: A Universal Stable-Diffusion Acceleration Module

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:38.690082Z

Source-reported events for the cited work

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Observation 2d7bc424-0e22-4dc4-8779-0a98073b0a6c · outbound

This paper cites DeepCache: Accelerating Diffusion Models for Free.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation DeepCache: Accelerating Diffusion Models for Free

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:38.891443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68fd5e22-ffe7-4348-99f1-bc9ddd68531a · outbound

This paper cites Generative Modelling With Inverse Heat Dissipation.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Generative Modelling With Inverse Heat Dissipation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:39.059225Z

Source-reported events for the cited work

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Observation 623458ab-66d6-4098-b35d-24eb9c1f850e · outbound

This paper cites Adversarial Diffusion Distillation.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Adversarial Diffusion Distillation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:39.136521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 794e49ff-e5b5-4f1a-9889-30652133ff85 · outbound

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

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:39.303189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42669976-a86c-483c-8341-c0238a109b1e · outbound

This paper cites Improved Techniques for Training Consistency Models.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Improved Techniques for Training Consistency Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:39.385676Z

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Unavailable: canonical work link unavailable.

Observation dbf7dd94-6d77-4c6d-9ce2-156443bd8d4a · outbound

This paper cites Multi-student Diffusion Distillation for Better One-step Generators.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Multi-student Diffusion Distillation for Better One-step Generators

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:39.470694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa823d91-fd7c-4a61-9b8d-d4b660eb9c14 · outbound

This paper cites SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:39.571460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7555eb0c-a308-4638-a428-265a5c0c88f5 · outbound

This paper cites Frequency Compensated Diffusion Model for Real-scene Dehazing.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Frequency Compensated Diffusion Model for Real-scene Dehazing

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:39.649309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31e3e269-4a91-40a1-81ac-f0c18826e331 · outbound

This paper cites UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:39.744061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4f75f32-9a00-40a3-a5ba-b19ae2e9caef · outbound

This paper cites Accelerating Diffusion Sampling with Optimized Time Steps.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Accelerating Diffusion Sampling with Optimized Time Steps

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:39.813119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61f5d5c3-6f2e-47ff-951a-fde856ab7dc4 · outbound

This paper cites Improved Distribution Matching Distillation for Fast Image Synthesis.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Improved Distribution Matching Distillation for Fast Image Synthesis

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:39.919187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04d56776-3663-4b0e-ab30-78ae54db1c0d · outbound

This paper cites Dynamic Diffusion Transformer.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Dynamic Diffusion Transformer

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:40.066725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e3111ef-5f29-4e87-89ab-b965315c1690 · outbound

This paper cites Fast ODE-based Sampling for Diffusion Models in Around 5 Steps.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Fast ODE-based Sampling for Diffusion Models in Around 5 Steps

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:55:40.936783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b91ce165-a3e4-4fb4-a09a-8269c4a9bf61 · outbound

This paper cites Accelerating Diffusion Transformers with Token-wise Feature Caching.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Accelerating Diffusion Transformers with Token-wise Feature Caching

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:40.343946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18e0812a-c421-46c9-b75e-a49210f4f59e · outbound

This paper cites Accelerating Diffusion InferenceMany recent works tried to accelerate the inference process of diffusion models, often focusing on the redundancy inherent in these models.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Accelerating Diffusion InferenceMany recent works tried to accelerate the inference process of diffusion models, often focusing on the redundancy inherent in these models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:55:42.434431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 68a54b05-003b-469b-a63c-4d28bbb09c2c · outbound

This paper cites Distillation models with GAN were introduced recently.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Distillation models with GAN were introduced recently

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:55:42.299986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 362ff8af-8860-4411-b10b-efad7041ccac · outbound

This paper cites CLIP-FID Results Potential data leakage in FID when using a discriminator pre-trained on ImageNet has been a concern (Kynk ¨a¨anniemi et al., 2023).

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation CLIP-FID Results Potential data leakage in FID when using a discriminator pre-trained on ImageNet has been a concern (Kynk ¨a¨anniemi et al., 2023)

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:55:42.122067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 388ccecb-81dc-4343-95a1-c63a4f242bb1 · outbound

This paper cites Dhariwal, P.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Dhariwal, P

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:37.011114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99c9ccb3-8d4b-45cf-890c-d79c60b76e77 · outbound

This paper cites Diffusion models have achieved great success in image generation (Dhariwal & Nichol, 2021; Nichol et al., 2022; Ramesh et al., 2022; Saharia et al.,.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Diffusion models have achieved great success in image generation (Dhariwal & Nichol, 2021; Nichol et al., 2022; Ramesh et al., 2022; Saharia et al.,

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:55:42.554376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9de90514-be45-4107-86b5-f74cbfab20cd · outbound

This paper cites Accelerating vision diffusion transformers with skip branches, 2024a.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Accelerating vision diffusion transformers with skip branches, 2024a

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:36.900613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11f13bff-5ff0-4b8c-bbfb-0d664d3e1500 · outbound

This paper cites Esser, P., Rombach, R., and Ommer, B.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Esser, P., Rombach, R., and Ommer, B

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:55:42.704169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 95f03254-6a3a-46bb-b814-89dc9868031b · outbound

This paper cites Consistency Models Made Easy.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Consistency Models Made Easy

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:37.252847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa7caf60-f3e8-4798-9ebe-4570d6ba6bcb · outbound

This paper cites Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models.

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T04:55:38.531698Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 00efa476-9839-4477-9421-65ef44777e30 · inbound

Learning to Optimize Radiotherapy Plans via Fluence Maps Diffusion Model Generation and LSTM-based Optimization cites this paper.

Learning to Optimize Radiotherapy Plans via Fluence Maps Diffusion Model Generation and LSTM-based Optimization Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:09:26.119837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.