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Paper Citation Record · LEDGER

Gradient-Free Classifier Guidance for Diffusion Model Sampling

As of 17 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 5 inbound Pith citation observations for arXiv:2411.15393.

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

pith.paper-citation-record.v1
2411.15393 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:29:03.765830Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:26:21.935195Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T19:03:06.342366Z

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 6d3ccb71-2aa7-4cb6-93e2-eb8029d61de5 · outbound

This paper cites Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance

Reference 1

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Observation 6496f13a-d11d-4c72-83d3-bd094630343f · outbound

This paper cites Universal guidance for diffusion models.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Universal guidance for diffusion models

Reference 2

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Observation 02d0bc13-7da3-4ef0-8cd8-9945f7ab68dd · outbound

This paper cites Diffusion Posterior Sampling for General Noisy Inverse Problems.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Diffusion Posterior Sampling for General Noisy Inverse Problems

Reference 3

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Observation f3e57aa8-af0b-4839-bbae-399de779dfc3 · outbound

This paper cites Stable diffusion.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Stable diffusion

Reference 4

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Observation 9d662bc1-537a-4053-8ccf-3c5a101a9c40 · outbound

This paper cites ImageNet: A large-scale hierarchical im- age database.

Gradient-Free Classifier Guidance for Diffusion Model Sampling ImageNet: A large-scale hierarchical im- age database

Reference 5

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Observation 990c0f59-0023-44ba-af91-21b98d7ef198 · outbound

This paper cites Diffusion models beat GANs on image synthesis.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Diffusion models beat GANs on image synthesis

Reference 6

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Observation 64867d05-f45e-455f-baa3-55b666d8568e · outbound

This paper cites A smaller subset of 10 easily classified classes from imagenet, and a little more french.

Gradient-Free Classifier Guidance for Diffusion Model Sampling A smaller subset of 10 easily classified classes from imagenet, and a little more french

Reference 7

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Observation e71f462a-f1de-4354-a18d-fa56a2cfc4f6 · outbound

This paper cites Manifold Preserving Guided Diffusion.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Manifold Preserving Guided Diffusion

Reference 8

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Observation 3811ac98-bdf5-49a7-b331-e9418bf6ff66 · outbound

This paper cites GANs trained by a two time-scale update rule converge to a local nash equi- librium.

Gradient-Free Classifier Guidance for Diffusion Model Sampling GANs trained by a two time-scale update rule converge to a local nash equi- librium

Reference 9

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Observation cdf1823a-c587-4f64-8866-2d0201f1021e · outbound

This paper cites Classifier-free diffusion guidance.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Classifier-free diffusion guidance

Reference 10

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Observation 95cca4f9-163b-4746-8f78-fbbe7d1300fb · outbound

This paper cites Denoising diffu- sion probabilistic models.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Denoising diffu- sion probabilistic models

Reference 11

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Observation 5d1500f1-abb2-42fc-b830-56b85540d5a2 · outbound

This paper cites Video diffu- sion models.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Video diffu- sion models

Reference 12

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Observation 3eceb7b5-c6d1-4eb2-ad77-0ecdafe2e941 · outbound

This paper cites Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention

Reference 13

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Observation c7897030-bea5-404b-986d-f1f3e1a9787d · outbound

This paper cites Improving sample quality of diffusion models us- ing self-attention guidance.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Improving sample quality of diffusion models us- ing self-attention guidance

Reference 14

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Observation 8c16a5eb-2152-4948-83e2-342112a5e7f1 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Elucidating the design space of diffusion-based generative models

Reference 15

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Observation 5b0482cd-8df4-4a0a-9c3b-b91302950509 · outbound

This paper cites Guiding a Diffusion Model with a Bad Version of Itself.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Guiding a Diffusion Model with a Bad Version of Itself

Reference 16

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Observation 1459cb0d-7a02-4790-bf4b-54824ab3dfe0 · outbound

This paper cites Analyzing and improving the training dynamics of diffusion models.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Analyzing and improving the training dynamics of diffusion models

Reference 17

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Observation 27ef5477-4c16-48e5-83d4-cad3145c0f17 · outbound

This paper cites Diffwave: A versatile diffusion model for audio synthesis.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Diffwave: A versatile diffusion model for audio synthesis

Reference 18

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Observation 480e4f8a-6ecb-4ab2-9dd7-3ffa0518e6f5 · outbound

This paper cites Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models

Reference 19

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Observation 5ddc6005-21b1-49b4-b7bd-676ff6e944d5 · outbound

This paper cites Pseudo Numerical Methods for Diffusion Models on Manifolds.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Pseudo Numerical Methods for Diffusion Models on Manifolds

Reference 20

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Observation aefb4eb6-54b6-4133-8aa3-9040bfb169fd · outbound

This paper cites Diffusion probabilistic models for 3d point cloud generation.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Diffusion probabilistic models for 3d point cloud generation

Reference 21

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Observation 6d7398fc-9c85-4dfe-9159-0091c5de84ee · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Gradient-Free Classifier Guidance for Diffusion Model Sampling GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 22

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Observation 7a4d6372-0f0b-4cee-a96d-b1e4bb99c2ed · outbound

This paper cites Arbitrary style guid- ance for enhanced diffusion-based text-to-image generation.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Arbitrary style guid- ance for enhanced diffusion-based text-to-image generation

Reference 23

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Observation f1d45b83-f067-4a15-b0b3-75f3c5438a8a · outbound

This paper cites On aliased resizing and surprising subtleties in gan evaluation.

Gradient-Free Classifier Guidance for Diffusion Model Sampling On aliased resizing and surprising subtleties in gan evaluation

Reference 24

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Observation c87604cc-cfba-459a-9c18-e096cbada60b · outbound

This paper cites Dif- fusion motion: Generate text-guided 3d human motion by diffusion model.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Dif- fusion motion: Generate text-guided 3d human motion by diffusion model

Reference 25

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Observation a70dbc79-ae71-4b7c-9e8b-0d665deb7ad6 · outbound

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

Gradient-Free Classifier Guidance for Diffusion Model Sampling High-resolution image synthesis with latent diffusion models

Reference 26

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Observation 2347814b-2ac6-4a82-81dd-9cfd64bb9e46 · outbound

This paper cites Palette: Image-to-image diffusion mod- els.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Palette: Image-to-image diffusion mod- els

Reference 27

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Observation 476150fd-1804-4966-923b-78d162d91136 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Photorealistic text-to-image diffusion models with deep language understanding

Reference 28

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Observation ca603ad7-3a3e-490b-a9f7-669145396729 · outbound

This paper cites Denois- ing diffusion implicit models.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Denois- ing diffusion implicit models

Reference 29

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 0bbd5946-28ab-43af-8af7-e7ef78d7ec4b · outbound

This paper cites Loss-guided diffusion models for plug-and-play controllable generation.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Loss-guided diffusion models for plug-and-play controllable generation

Reference 30

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Observation 6359cdbc-adb1-402e-abeb-1e1d110d8682 · outbound

This paper cites Generative modeling by esti- mating gradients of the data distribution.Advances in Neural Information Processing Systems, 32, 2019.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Generative modeling by esti- mating gradients of the data distribution.Advances in Neural Information Processing Systems, 32, 2019

Reference 31

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Observation fbe3eaf8-191a-4a39-9ac7-ac6373cd0a7f · outbound

This paper cites Score-based generative modeling through stochastic differential equa- tions.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Score-based generative modeling through stochastic differential equa- tions

Reference 32

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Observation 1f238ba2-a682-4d72-b21f-92f538510e68 · outbound

This paper cites Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

Reference 33

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Observation d8959c08-1eb3-45fe-bb02-73ebe5d7a2d9 · outbound

This paper cites Geometric latent diffusion models for 3d molecule generation.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Geometric latent diffusion models for 3d molecule generation

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation f3bc2f4d-3854-45f0-8672-2cdf85e7fbef · outbound

This paper cites TFG: Unified Training-Free Guidance for Diffusion Models.

Gradient-Free Classifier Guidance for Diffusion Model Sampling TFG: Unified Training-Free Guidance for Diffusion Models

Reference 35

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source=pdf_text observed=2026-08-12T14:29:03.743225Z digest=sha256:49b32f836884ce8f17708f8b861020b848aecea3f9a7b82a35c0f63f1530f154

Observation c367bfe9-9fbf-43d4-989a-24c995f36b74 · outbound

This paper cites FreeDoM: Training-free energy-guided condi- tional diffusion model.

Gradient-Free Classifier Guidance for Diffusion Model Sampling FreeDoM: Training-free energy-guided condi- tional diffusion model

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-12T14:29:03.965740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T14:29:03.746935Z digest=sha256:f5586a8aa21794d815ab491c6435dcb10cfe2de04710f00fd664edd4dc57db74

Observation 26d316a7-fb94-4d65-80f4-1ec740ee1188 · outbound

This paper cites To further validate this choice, we also conducted a lossy compression test as in [24] to compare FID and FDDINOv2.

Gradient-Free Classifier Guidance for Diffusion Model Sampling To further validate this choice, we also conducted a lossy compression test as in [24] to compare FID and FDDINOv2

Reference 37

Resolution
malformed identifier
raw_fallback, observed 2026-08-12T14:29:03.953522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T14:29:03.750551Z digest=sha256:43b72af4e1c16a7b96204dd0eef31938b281e508271740eae932948b4dcd5860

Observation ef11a2a5-b995-4fe2-b023-2e481c40c535 · outbound

This paper cites Class-Conditional Generation: Pseudo Code For Algorithm 1, specifics like timestep t, noise schedule and sampling method are illustrated using DDIM as the example.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Class-Conditional Generation: Pseudo Code For Algorithm 1, specifics like timestep t, noise schedule and sampling method are illustrated using DDIM as the example

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:29:03.941254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T14:29:03.754875Z digest=sha256:78f38fdb0af201a084e356bd3a596abb8866fa6826ca571f85a8bb3953946052

Observation 71e717c3-33b9-4d89-ae13-7a22b1cf5097 · outbound

This paper cites Effects of Random Seed Variation The results presented in Table 1 of the main paper were gen- erated using the same random seed for image generation.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Effects of Random Seed Variation The results presented in Table 1 of the main paper were gen- erated using the same random seed for image generation

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:29:03.929013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T14:29:03.758927Z digest=sha256:a6f19819edc858384436d31bfbfe8c5250e0b5b4ac75d1e4f8cf6829e63f356c

Observation 602f31d9-f507-44d5-8b94-97b2c09d0e28 · outbound

This paper cites Additionally, we compare GFCG to the additive method GFCGATG+CFG, which achieves state- of-the-art performance in FD DINOv2 for EDM2-S.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Additionally, we compare GFCG to the additive method GFCGATG+CFG, which achieves state- of-the-art performance in FD DINOv2 for EDM2-S

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:29:03.916275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T14:29:03.762523Z digest=sha256:235247d29f21d6ed7076a5c0b1b59f89c37b95c199d9973dc21fb03ba65f585b

Observation 9a711e09-303b-4cdd-a058-562f1d3f99a5 · outbound

This paper cites Additionally, we also conducted experiments using another popular model, DeepFloyd IF model5 from Stability AI.

Gradient-Free Classifier Guidance for Diffusion Model Sampling Additionally, we also conducted experiments using another popular model, DeepFloyd IF model5 from Stability AI

Reference 41

Resolution
malformed identifier
raw_fallback, observed 2026-08-12T14:29:03.904201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T14:29:03.765830Z digest=sha256:2aad151dc1b90d322eff89deb3accd72a15451157fa9f836780a62c2acd568cf

Pith citing papers

Observation d8ad7a8a-2035-47e1-a24e-1cd36abbdeeb · inbound

Visual Generation Without Guidance cites this paper.

Visual Generation Without Guidance Gradient-Free Classifier Guidance for Diffusion Model Sampling

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:21.935195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T14:26:21.935195Z digest=sha256:bb8ba1767b5a759d690ae30c8204fbdaf5bd542e97b406fddb59bee1ba1b7070

Observation cc81948b-6b77-4080-9da6-08d07737230f · inbound

UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control cites this paper.

UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control Gradient-Free Classifier Guidance for Diffusion Model Sampling

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-08T18:14:31.551294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:14:31.551294Z digest=sha256:30ee05d5a96056360916c849ba3217cf39d93ff805adeee1bb949f547be4c82e

Observation 771bb458-2025-4a9c-91c0-332fcd6556a8 · inbound

Contrastive Flow Matching cites this paper.

Contrastive Flow Matching Gradient-Free Classifier Guidance for Diffusion Model Sampling

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T10:31:22.944250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:31:22.944250Z digest=sha256:745fd7246e02c9f81238ac3cd4bf3c73670008b129cd510bfdedee649bc443a9

Observation d3083a3b-af32-4957-aee2-1ef382ba4d43 · inbound

DiffIER: Optimizing Diffusion Models with Iterative Error Reduction cites this paper.

DiffIER: Optimizing Diffusion Models with Iterative Error Reduction Gradient-Free Classifier Guidance for Diffusion Model Sampling

Reference 16

Resolution
metadata mismatch
local_arxiv, observed 2026-08-05T19:03:06.420342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-05T19:03:05.622039Z digest=sha256:f6309c8b6e7957f58791fa0dbcffddd6e26bdff8acc8bbb2081d6cfae31be4fc

Observation 34eda9ab-37e5-4bdc-b17c-aa1c7bb73388 · inbound

Conditional Diffusion Guidance under Hard Constraint: A Stochastic Analysis Approach cites this paper.

Conditional Diffusion Guidance under Hard Constraint: A Stochastic Analysis Approach Gradient-Free Classifier Guidance for Diffusion Model Sampling

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-03T04:22:31.689426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T04:22:31.689426Z digest=sha256:e725ae8fb8f6ff0bedb201340a579706dfb39afeae757ef15c8de58fe631913e