Paper Citation Record · LEDGER

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models

As of 22 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2412.15341.

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

pith.paper-citation-record.v1
2412.15341 v2

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

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measured 66 of 66 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:29:39.873392Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T12:29:40.428282Z

Reference resolution

65 of 65 outbound references displayed

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External citation measurements

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Outbound references

Observation 0bb8fde5-4599-4ff4-b56d-b5fa7292568a · outbound

This paper cites Nudenet: An ensemble of neural nets for nudity detection and censoring, 2020.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Nudenet: An ensemble of neural nets for nudity detection and censoring, 2020

Reference 1

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Observation fa329448-2f55-49e3-814c-a74375d706d4 · outbound

This paper cites ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning

Reference 2

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This paper cites A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations

Reference 3

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Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Unresolved cited work

Reference 4

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Observation c0bae73e-21bf-4c92-bd10-ea233a9270da · outbound

This paper cites Salun: Empowering machine unlearn- ing via gradient-based weight saliency in both image classi- fication and generation.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Salun: Empowering machine unlearn- ing via gradient-based weight saliency in both image classi- fication and generation

Reference 5

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Observation 89661325-323f-4c27-b796-b2ca377a97e4 · outbound

This paper cites Structural pruning for diffusion models, 2023.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Structural pruning for diffusion models, 2023

Reference 6

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Observation 5c282425-5d25-4d7e-bce4-8c29a268dbfa · outbound

This paper cites Forward and reverse gradient-based hyper- parameter optimization.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Forward and reverse gradient-based hyper- parameter optimization

Reference 7

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Observation af56c0f4-69f2-48fd-9b79-8791d24e9b3d · outbound

This paper cites Bilevel programming for hyperparameter optimization and meta-learning.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Bilevel programming for hyperparameter optimization and meta-learning

Reference 8

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Observation f0ee1d3a-8958-4759-83e6-ea4ec6cd2ca1 · outbound

This paper cites Erasing concepts from diffusion models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Erasing concepts from diffusion models

Reference 9

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Observation 5ff5dce6-5955-478c-bc8f-fa5b700b74e2 · outbound

This paper cites Unified concept editing in dif- fusion models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Unified concept editing in dif- fusion models

Reference 10

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Observation b3d2c6a9-2bfe-47a4-87c5-af9900d18234 · outbound

This paper cites Mixture of efficient diffusion ex- perts through automatic interval and sub-network selection.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Mixture of efficient diffusion ex- perts through automatic interval and sub-network selection

Reference 11

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Observation 6d6f4d6b-3533-4b8e-948b-f276a9e57e2d · outbound

This paper cites Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models

Reference 12

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Observation 88d621fb-16e8-4f26-a6d5-dc573c733778 · outbound

This paper cites Approximation methods for bilevel programming, 2018.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Approximation methods for bilevel programming, 2018

Reference 13

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Observation fc7564ef-f0b2-4c7f-abe7-aeb67da83324 · outbound

This paper cites Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss

Reference 14

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Observation 84fb8fbc-78fc-4769-af77-d63e1a6d13bf · outbound

This paper cites Selective amnesia: A continual learning approach to forgetting in deep generative models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Selective amnesia: A continual learning approach to forgetting in deep generative models

Reference 15

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Observation 21fe09f3-2356-4541-9850-c45bfd031497 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 16

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Observation e90b749b-4cd7-445e-a758-323e4a1acdb4 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Distilling the Knowledge in a Neural Network

Reference 17

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Observation 8579bc84-62ac-44c3-aa45-ad78e78cd572 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Classifier-Free Diffusion Guidance

Reference 18

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Observation b933873d-8d7c-4220-a81a-2ac091987b5c · outbound

This paper cites Denoising diffu- sion probabilistic models, 2020.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Denoising diffu- sion probabilistic models, 2020

Reference 19

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Observation 9ac2cd39-bc67-4015-b7a8-4788952f0a0c · outbound

This paper cites Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers

Reference 20

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This paper cites Elucidating the design space of diffusion-based generative models, 2022.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Elucidating the design space of diffusion-based generative models, 2022

Reference 21

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Observation 3b3b4f84-5799-47f5-8fdd-91dad6bdf9f8 · outbound

This paper cites BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion

Reference 22

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Observation 37ac31de-1e91-4d5c-ae0a-49378160cd0a · outbound

This paper cites Ablating con- cepts in text-to-image diffusion models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Ablating con- cepts in text-to-image diffusion models

Reference 23

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Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Unresolved cited work

Reference 24

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Observation 3651ea00-130e-44ca-99d6-75823e9e328c · outbound

This paper cites KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis

Reference 25

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Observation b00dc62c-052e-4cd6-a704-5cb2c1a2f42a · outbound

This paper cites Pruning filters for efficient convnets, 2017.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Pruning filters for efficient convnets, 2017

Reference 26

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Observation 49a99859-5dbb-47fd-94b4-2602fc404253 · outbound

This paper cites Q-Diffusion: Quantizing Diffusion Models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Q-Diffusion: Quantizing Diffusion Models

Reference 27

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This paper cites Snap- 9 fusion: Text-to-image diffusion model on mobile devices within two seconds, 2023.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Snap- 9 fusion: Text-to-image diffusion model on mobile devices within two seconds, 2023

Reference 28

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Observation c01eb3e6-6ca8-4e08-9aa3-a0dfcb35d5c8 · outbound

This paper cites Microsoft coco: Common objects in context, 2014.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Microsoft coco: Common objects in context, 2014

Reference 29

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Observation 3f9ce9c2-934e-4929-bde8-839542bda2e9 · outbound

This paper cites Bome! bilevel optimization made easy: A simple first- order approach.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Bome! bilevel optimization made easy: A simple first- order approach

Reference 30

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

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Pseudo numerical methods for diffusion models on manifolds

Reference 31

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Observation ac6398e1-4b3b-4fcc-90b0-d7f45c386d89 · outbound

This paper cites Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-loop and Hessian-free Solution Strategy.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-loop and Hessian-free Solution Strategy

Reference 32

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Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Implicit Concept Removal of Diffusion Models

Reference 33

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Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Decoupled weight decay regularization

Reference 34

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Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models First-order penalty methods for bilevel optimization

Reference 35

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Observation f06cf4fc-cc2e-4bd7-99cd-a8e9a7275f6e · outbound

This paper cites Edit- ing implicit assumptions in text-to-image diffusion models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Edit- ing implicit assumptions in text-to-image diffusion models

Reference 36

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Observation 800d2898-88cb-4f99-b682-6dc4cc440fcb · outbound

This paper cites SDXL: improving latent diffusion models for high-resolution image synthesis, 2024.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models SDXL: improving latent diffusion models for high-resolution image synthesis, 2024

Reference 37

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This paper cites Learning transferable visual models from natural language supervision.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Learning transferable visual models from natural language supervision

Reference 38

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Observation ed74653d-98e2-4fd5-90e6-be2cdddf3c1e · outbound

This paper cites Red-Teaming the Stable Diffusion Safety Filter.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Red-Teaming the Stable Diffusion Safety Filter

Reference 39

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This paper cites High-resolution image syn- thesis with latent diffusion models, 2022.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models High-resolution image syn- thesis with latent diffusion models, 2022

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:02.554849Z

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Observation 72f7e47d-53f9-4b65-940b-825588be6614 · outbound

This paper cites Fitnets: Hints for thin deep nets, 2015.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Fitnets: Hints for thin deep nets, 2015

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:02.536729Z

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Observation 241b4f94-64e6-414e-a830-2a6ea919d864 · outbound

This paper cites Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J

Reference 42

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Observation 8e712efb-8b41-4f51-81d1-3dc0f036cbb8 · outbound

This paper cites Safe latent diffusion: Mitigating inappro- priate degeneration in diffusion models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Safe latent diffusion: Mitigating inappro- priate degeneration in diffusion models

Reference 43

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Observation cf087e03-2d29-4dbb-b130-730066075baf · outbound

This paper cites Safe latent diffusion: Mitigating inap- propriate degeneration in diffusion models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Safe latent diffusion: Mitigating inap- propriate degeneration in diffusion models

Reference 44

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Observation cce390c0-57b6-4523-ad99-7465122e393f · outbound

This paper cites On penalty-based bilevel gradi- ent descent method.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models On penalty-based bilevel gradi- ent descent method

Reference 45

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Observation 5e81b12d-70d2-43f9-97b5-2d2bd09464b2 · outbound

This paper cites Weiss, Niru Mah- eswaranathan, and Surya Ganguli.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Weiss, Niru Mah- eswaranathan, and Surya Ganguli

Reference 46

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Observation 1ac77f51-a1bd-4a2d-aa23-fa3df9908aec · outbound

This paper cites Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models

Reference 47

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This paper cites Measuring Style Similarity in Diffusion Models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Measuring Style Similarity in Diffusion Models

Reference 48

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Observation 5817fbe1-d331-4207-9ee5-5e0a0d0f2b40 · outbound

This paper cites Generative modeling by es- timating gradients of the data distribution, 2019.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Generative modeling by es- timating gradients of the data distribution, 2019

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:02.409957Z

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Observation 43bd9c86-290a-4190-8b35-cc3c8ac9656b · outbound

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

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 50

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Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:36:02.391891Z

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Observation 6cef0855-4e06-4f25-b0ef-075f1610b598 · outbound

This paper cites Erasing Undesirable Influence in Diffusion Models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Erasing Undesirable Influence in Diffusion Models

Reference 52

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Observation ec56cf4b-1f97-4b9c-be94-779154d611b4 · outbound

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Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Pruning for Robust Concept Erasing in Diffusion Models

Reference 53

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Observation 27cab729-6e19-42a4-be45-5af8038f1120 · outbound

This paper cites Diffusion probabilistic model made slim, 2023.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Diffusion probabilistic model made slim, 2023

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:02.369753Z

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Observation 07fc47e2-d08b-4075-9426-dd497bbf5144 · outbound

This paper cites Mma-diffusion: Multimodal attack 10 on diffusion models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Mma-diffusion: Multimodal attack 10 on diffusion models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:02.329187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1bb62335-3718-44f1-a6c3-540c7bdc84ae · outbound

This paper cites LAPTOP-Diff: Layer Pruning and Normalized Distillation for Compressing Diffusion Models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models LAPTOP-Diff: Layer Pruning and Normalized Distillation for Compressing Diffusion Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T11:36:01.633785Z

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Observation d0f0408d-19b5-47b0-8f9e-706832667b9c · outbound

This paper cites Forget-me-not: Learning to forget in text-to-image diffusion models, 2024.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Forget-me-not: Learning to forget in text-to-image diffusion models, 2024

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:02.310703Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 904097b1-9140-4fb6-a338-4139e133a02d · outbound

This paper cites Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T11:36:01.643476Z

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Observation 42902d14-77e3-43ae-90ca-03b3dde39159 · outbound

This paper cites To gen- erate or not? safety-driven unlearned diffusion models are still easy to generate unsafe images.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models To gen- erate or not? safety-driven unlearned diffusion models are still easy to generate unsafe images

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:02.279537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 625074df-0567-4a27-9f90-e5e9a71e2ff4 · outbound

This paper cites Separable Multi-Concept Erasure from Diffusion Models.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Separable Multi-Concept Erasure from Diffusion Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T11:36:01.655803Z

Source-reported events for the cited work

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Observation fe152559-38d2-42e3-bda5-a6e219becd8e · outbound

This paper cites MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T11:36:01.660739Z

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Observation 5d9634f0-d3c8-44f1-8e7e-bcaf8da36343 · outbound

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Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:36:02.257227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 82c7fd6e-a88f-4a25-837e-d5c272125ccf · outbound

This paper cites an unresolved cited work.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:36:02.238670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9b33b41b-9e6c-4fbc-90d0-0cb0ab019dea · outbound

This paper cites To prevent all codes from collapsing into a single one, the router module employs optimal transport during the pruning phase.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models To prevent all codes from collapsing into a single one, the router module employs optimal transport during the pruning phase

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:02.220560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b695d10b-25b2-4534-9a24-b5bca11e9c80 · outbound

This paper cites Figure Prompts Samples in Fig.

Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models Figure Prompts Samples in Fig

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:02.202418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation c2d9b3d1-932c-4fb6-b871-c58c0e0535f5 · inbound

Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts cites this paper.

Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models

Reference 74

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.