Paper Citation Record · LEDGER

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory

As of 23 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2411.17472.

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

pith.paper-citation-record.v1
2411.17472 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:26:57.085177Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:16:58.456615Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T11:16:59.212160Z

Reference resolution

46 of 46 outbound references displayed

verified exact3
verified fuzzy26
unresolved17
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6697357c-7582-4ab2-b951-f56769bae28d · outbound

This paper cites Karanam, Kshitijh Joseph, Ak- shara Saxena, Karan Goswami, and Balaji Vasan Srinivasan.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Karanam, Kshitijh Joseph, Ak- shara Saxena, Karan Goswami, and Balaji Vasan Srinivasan

Reference 1

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Observation 7609d9c8-8aa4-4907-a675-8e5a7a6565f1 · outbound

This paper cites Blended diffusion for text-driven editing of nat- ural images.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Blended diffusion for text-driven editing of nat- ural images

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.791681Z

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Observation ef177c57-a127-4e50-bd2b-2d701d6f5b45 · outbound

This paper cites Large scale GAN training for high fidelity natural image synthesis.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Large scale GAN training for high fidelity natural image synthesis

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.776890Z

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Observation 385c3b53-dde5-4185-866f-428e888726b9 · outbound

This paper cites In- structpix2pix: Learning to follow image editing instructions.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory In- structpix2pix: Learning to follow image editing instructions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.762836Z

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Observation 14f29814-f985-498c-9b01-93da12bf9992 · outbound

This paper cites Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-12T13:26:57.387644Z

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Observation 81c95d1f-7584-430d-afd9-9bc974365c84 · outbound

This paper cites Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.749092Z

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Observation a4704e4f-80a5-4b69-89e4-7fdcc58e4301 · outbound

This paper cites Wide stochastic networks: Gaussian limit and pac-bayesian training.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Wide stochastic networks: Gaussian limit and pac-bayesian training

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.734407Z

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Observation b52c5a45-3230-4365-aa7a-32dea796eadf · outbound

This paper cites Diffusion models beat gans on image synthesis.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Diffusion models beat gans on image synthesis

Reference 8

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Source-reported events for the cited work

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Observation eac2062e-ffee-439f-93f7-e383d6f13dae · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.708629Z

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Observation cf989b6a-7d29-4f3c-9e7e-94ab14e2a588 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Taming transformers for high-resolution image synthesis

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.693474Z

Source-reported events for the cited work

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Observation a75ff9ee-fb96-45be-9a58-1f9c38621a8f · outbound

This paper cites Training- free structured diffusion guidance for compositional text-to- image synthesis.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Training- free structured diffusion guidance for compositional text-to- image synthesis

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.678380Z

Source-reported events for the cited work

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Observation 1f3a98ab-941a-4056-b3b7-9aab564bcc3c · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 12

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Observation 13aad9d1-07ef-4c24-ac15-d520e9a31e91 · outbound

This paper cites Generative adversarial nets.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Generative adversarial nets

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.663105Z

Source-reported events for the cited work

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Observation acf3261e-c4fb-4400-886b-98d6b785d80d · outbound

This paper cites Prompt-to-prompt im- age editing with cross-attention control.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Prompt-to-prompt im- age editing with cross-attention control

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.648554Z

Source-reported events for the cited work

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Observation 20ac7ba3-dec4-4299-86b9-a3d8e4630e23 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Classifier-Free Diffusion Guidance

Reference 15

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Source-reported events for the cited work

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Observation 7de0f43d-8d61-4a9b-8d0b-06e9ed7f9356 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Denoising dif- fusion probabilistic models

Reference 16

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Source-reported events for the cited work

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Observation beee7a66-cf7a-4e72-b3e1-73d1083e78e5 · outbound

This paper cites spaCy 2: Natural lan- guage understanding with Bloom embeddings, convolutional neural networks and incremental parsing.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory spaCy 2: Natural lan- guage understanding with Bloom embeddings, convolutional neural networks and incremental parsing

Reference 17

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Source-reported events for the cited work

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Observation d7ec5626-2b0c-4730-800f-7f41f3a805a3 · outbound

This paper cites Denoising Diffusion Restoration Models.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Denoising Diffusion Restoration Models

Reference 18

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Source-reported events for the cited work

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Observation 0eec0809-4b09-486d-8ac9-25edf8fb6025 · outbound

This paper cites DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation

Reference 19

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Observation 1960beac-b345-482f-bd41-ed9464328e8a · outbound

This paper cites Kingma, Tim Salimans, Ben Poole, and Jonathan Ho.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Kingma, Tim Salimans, Ben Poole, and Jonathan Ho

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.614355Z

Source-reported events for the cited work

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Observation 1fa29a35-1790-48cc-b1ab-99b6dc19281f · outbound

This paper cites Dichotomize and generalize: Pac-bayesian bi- nary activated deep neural networks.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Dichotomize and generalize: Pac-bayesian bi- nary activated deep neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.599553Z

Source-reported events for the cited work

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Observation 07b5db2a-6e5f-404e-9b31-9600873819d8 · outbound

This paper cites Divide and bind: Improving long-term compositionality in text-to-image synthesis.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Divide and bind: Improving long-term compositionality in text-to-image synthesis

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.584404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e84aa992-11ef-485b-9659-d34e52c01252 · outbound

This paper cites GLIGEN: Open-Set Grounded Text-to-Image Generation.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory GLIGEN: Open-Set Grounded Text-to-Image Generation

Reference 23

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Observation 8a12a55a-8f39-4fc7-a360-3a5af28d16ce · outbound

This paper cites Tenenbaum.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Tenenbaum

Reference 24

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Source-reported events for the cited work

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Observation 585b7279-13d9-4f57-968b-59d639ddc34f · outbound

This paper cites Some PAC-Bayesian theorems.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Some PAC-Bayesian theorems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.555946Z

Source-reported events for the cited work

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Observation ffc97d68-fd91-45ac-bdaf-f40ff0c1edf5 · outbound

This paper cites SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 26

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Observation 54cdbf6a-fdb7-4808-8357-574c59e636d5 · outbound

This paper cites Improved Denoising Diffusion Probabilistic Models.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Improved Denoising Diffusion Probabilistic Models

Reference 28

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Source-reported events for the cited work

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Observation a26452f6-a9ad-4f6f-b849-cb1d4eb2902f · outbound

This paper cites Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-12T13:26:57.260286Z

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Observation 476dc9e3-f396-4357-a157-93438616d3dc · outbound

This paper cites Image Transformer.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Image Transformer

Reference 30

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Observation 9b0ae256-8fda-4cbf-8080-f77d531d9cc5 · outbound

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

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Learning transferable visual models from natural language supervision

Reference 31

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Observation c8bdd71a-0a25-4e6b-91e4-5e0fcf6bce0a · outbound

This paper cites Linguistic bind- ing in diffusion models: Enhancing attribute correspondence through attention map alignment.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Linguistic bind- ing in diffusion models: Enhancing attribute correspondence through attention map alignment

Reference 32

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Observation a0fc1955-2fca-4028-9deb-d948ca6434ab · outbound

This paper cites Generative ad- versarial text to image synthesis.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Generative ad- versarial text to image synthesis

Reference 33

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Observation e020787f-98c3-4ff2-8cfa-037d25ec1ede · outbound

This paper cites Geometry-free view synthesis: Transformers and no 3d pri- ors.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Geometry-free view synthesis: Transformers and no 3d pri- ors

Reference 34

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Observation b70576f4-6eda-489e-9050-c67e8ea9dd62 · outbound

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

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory High-resolution image syn- thesis with latent diffusion models

Reference 35

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Observation af3c8ade-eff8-48f2-90c5-09578037c256 · outbound

This paper cites Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Reference 36

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Observation 7de605f0-6f20-4abc-a3d7-71a188f594f8 · outbound

This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 37

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Observation b80900f8-7670-411d-ad7d-541f242c309e · outbound

This paper cites Score-based generative mod- eling through stochastic differential equations.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Score-based generative mod- eling through stochastic differential equations

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.466017Z

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Observation 4133124d-1501-44bd-a8ea-33fccb5e8f9b · outbound

This paper cites Nor- malized flat minima: Exploring scale invariant definition of flat minima for neural networks using pac-bayesian analy- sis.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Nor- malized flat minima: Exploring scale invariant definition of flat minima for neural networks using pac-bayesian analy- sis

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.451602Z

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Observation 149d29a3-28ae-4718-9eab-e1a2654d9118 · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.436673Z

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Observation fc16b50d-86f8-4d05-be3f-66e0d431a318 · outbound

This paper cites "Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory "Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood

Reference 41

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Observation f65fa40b-8d7a-47f3-b0c3-1af945be9107 · outbound

This paper cites Object- conditioned energy-based model for attention map alignment in text-to-image diffusion models.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Object- conditioned energy-based model for attention map alignment in text-to-image diffusion models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.421482Z

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Observation d5a4655e-e6c6-49ec-9a86-8bfe2da8fb6c · outbound

This paper cites Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching

Reference 43

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Observation 4c444458-e36f-4ffd-9ad3-6aa2ff90a9dd · outbound

This paper cites Object- conditioned energy-based attention map alignment in text-to- image diffusion models.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Object- conditioned energy-based attention map alignment in text-to- image diffusion models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:26:57.405975Z

Source-reported events for the cited work

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Observation 30b748af-d789-4e4d-8e16-098aa25e3313 · outbound

This paper cites Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-12T13:26:57.163207Z

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Observation 644470cd-8cfe-4117-b7a3-b5b9fec24ae9 · outbound

This paper cites Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach

Reference 46

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Source-reported events for the cited work

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Observation c16294af-8637-4ed0-abb1-93e685b0117d · outbound

This paper cites Think Twice Before You Act: Improving Inverse Problem Solving With MCMC.

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory Think Twice Before You Act: Improving Inverse Problem Solving With MCMC

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T13:26:57.085177Z

Source-reported events for the cited work

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Pith citing papers

Observation 1c7dbae7-a9d1-43f0-8dbb-97f325f1a675 · inbound

Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models cites this paper.

Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-12T11:16:59.219706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.