Paper Citation Record · LEDGER

Physics Informed Distillation for Diffusion Models

As of 14 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 2 inbound Pith citation observations for arXiv:2411.08378.

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

pith.paper-citation-record.v1
2411.08378 v1

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measured 53 of 53 reference resolution

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measured 55 of 55 standing notices

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

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External citation measurements

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Outbound references

Observation e914b282-6871-4610-ba8e-33e1fbf238a3 · outbound

This paper cites Label-efficient semantic segmentation with diffusion models.

Physics Informed Distillation for Diffusion Models Label-efficient semantic segmentation with diffusion models

Reference 1

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Observation 60dfe328-d935-4944-9f8f-19ee9c863965 · outbound

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

Physics Informed Distillation for Diffusion Models Large scale GAN training for high fidelity natural image synthesis

Reference 2

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Observation 315ed7c3-9968-46b8-b8af-817bc67b9921 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Physics Informed Distillation for Diffusion Models Emerging properties in self-supervised vision transformers

Reference 3

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Observation 4caa3655-8b79-4ccc-83be-9a648277b181 · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Physics Informed Distillation for Diffusion Models Improved Baselines with Momentum Contrastive Learning

Reference 4

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Observation bec02347-d786-4117-aa5f-2fcf9fa41898 · outbound

This paper cites Can-pinn: A fast physics-informed neural network based on coupled-automatic--numerical differentiation method.

Physics Informed Distillation for Diffusion Models Can-pinn: A fast physics-informed neural network based on coupled-automatic--numerical differentiation method

Reference 5

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Observation 4e994d51-5402-4f35-9cf3-9d86716bbc0b · outbound

This paper cites Scientific machine learning through physics--informed neural networks: where we are and what’s next.

Physics Informed Distillation for Diffusion Models Scientific machine learning through physics--informed neural networks: where we are and what’s next

Reference 6

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Observation f358b367-f0c5-49dc-8036-94c10a7a0f43 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Physics Informed Distillation for Diffusion Models Imagenet: A large-scale hierarchical image database

Reference 7

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Observation abdf4625-1082-4a8c-8e3f-e9e09d2cc5ed · outbound

This paper cites Diffusion models beat gans on image synthesis.

Physics Informed Distillation for Diffusion Models Diffusion models beat gans on image synthesis

Reference 8

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Observation 69c1149a-54f1-4765-b33d-bba1d0e37b91 · outbound

This paper cites One-step diffusion distillation via deep equilibrium models.

Physics Informed Distillation for Diffusion Models One-step diffusion distillation via deep equilibrium models

Reference 9

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Observation f51e015b-e3e6-4667-8928-e53f5aebb775 · outbound

This paper cites Generative adversarial networks.

Physics Informed Distillation for Diffusion Models Generative adversarial networks

Reference 10

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Observation 3537a961-d3ff-4830-b902-6fa4a2a85701 · outbound

This paper cites Lecture 4: Picard-lindelöf theorem.

Physics Informed Distillation for Diffusion Models Lecture 4: Picard-lindelöf theorem

Reference 11

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Observation 14f10cc0-7767-46ee-ad54-143a2f77624c · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

Physics Informed Distillation for Diffusion Models Bootstrap your own latent-a new approach to self-supervised learning

Reference 12

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Observation 18d9b141-622b-4c33-a869-58a6714518ef · outbound

This paper cites Boot: Data-free distillation of denoising diffusion models with bootstrapping.

Physics Informed Distillation for Diffusion Models Boot: Data-free distillation of denoising diffusion models with bootstrapping

Reference 13

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Observation bcc162c5-b540-49ff-9431-1565a1f0cc7f · outbound

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

Physics Informed Distillation for Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 14

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Observation 2c994590-6709-46d6-b0a2-bd435cdfe298 · outbound

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

Physics Informed Distillation for Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 15

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Observation 8c0d2bd3-4671-45dd-bb89-3326b293ea2d · outbound

This paper cites Denoising diffusion probabilistic models.

Physics Informed Distillation for Diffusion Models Denoising diffusion probabilistic models

Reference 16

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Observation 118d1570-849c-48cb-8b1e-4b9c0d34a9bc · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Physics Informed Distillation for Diffusion Models Multilayer feedforward networks are universal approximators

Reference 17

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Observation c4666d0f-7b6f-4fcf-b37c-0969ff1b843e · outbound

This paper cites Gotta Go Fast When Generating Data with Score-Based Models.

Physics Informed Distillation for Diffusion Models Gotta Go Fast When Generating Data with Score-Based Models

Reference 18

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Observation 1e138cf5-8b8e-4222-b023-2748f82ceb50 · outbound

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

Physics Informed Distillation for Diffusion Models Elucidating the design space of diffusion-based generative models

Reference 19

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Observation e1dcbe01-1a4e-449a-abfd-02437c1a4f66 · outbound

This paper cites Consistency trajectory models: Learning probability flow ODE trajectory of diffusion.

Physics Informed Distillation for Diffusion Models Consistency trajectory models: Learning probability flow ODE trajectory of diffusion

Reference 20

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Observation fa2315a2-d802-452e-ac62-b873dd79255b · outbound

This paper cites Auto-Encoding Variational Bayes.

Physics Informed Distillation for Diffusion Models Auto-Encoding Variational Bayes

Reference 21

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Observation dec5c985-77f4-48cb-8200-c50e9fef729e · outbound

This paper cites Glow: Generative flow with invertible 1x1 convolutions.

Physics Informed Distillation for Diffusion Models Glow: Generative flow with invertible 1x1 convolutions

Reference 22

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Observation 258521d9-9afb-46a3-b870-9ad0f4e9e87e · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick.

Physics Informed Distillation for Diffusion Models Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick

Reference 23

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Observation 1a6a8c6b-125d-4409-b44d-51c57106ce7b · outbound

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

Physics Informed Distillation for Diffusion Models Learning multiple layers of features from tiny images

Reference 24

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This paper cites Artificial neural networks for solving ordinary and partial differential equations.

Physics Informed Distillation for Diffusion Models Artificial neural networks for solving ordinary and partial differential equations

Reference 25

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Observation bd4f1243-ae9a-468b-975d-225753dd98dd · outbound

This paper cites Srdiff: Single image super-resolution with diffusion probabilistic models.

Physics Informed Distillation for Diffusion Models Srdiff: Single image super-resolution with diffusion probabilistic models

Reference 26

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Observation 8a90990a-9045-4534-a698-b68aec05c335 · outbound

This paper cites On the variance of the adaptive learning rate and beyond.

Physics Informed Distillation for Diffusion Models On the variance of the adaptive learning rate and beyond

Reference 27

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Observation 8f1d774c-88e4-4524-be07-ccc20392e09e · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

Physics Informed Distillation for Diffusion Models Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 28

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Observation 20a86ac7-5319-49a6-8b15-1d174d8219bf · outbound

This paper cites DPM -solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps.

Physics Informed Distillation for Diffusion Models DPM -solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps

Reference 29

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Observation 9d202104-37bd-4ec8-ad17-bd4dcbf2ff0f · outbound

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

Physics Informed Distillation for Diffusion Models Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

Reference 30

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This paper cites Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models.

Physics Informed Distillation for Diffusion Models Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models

Reference 31

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Observation e8f035b0-89f0-4370-97ac-57c9f325b6a6 · outbound

This paper cites GLIDE : Towards photorealistic image generation and editing with text-guided diffusion models.

Physics Informed Distillation for Diffusion Models GLIDE : Towards photorealistic image generation and editing with text-guided diffusion models

Reference 32

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Observation 09f1bd6d-7cfc-43fa-8022-3851dfaa3ec8 · outbound

This paper cites CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution.

Physics Informed Distillation for Diffusion Models CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution

Reference 33

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

Physics Informed Distillation for Diffusion Models Pytorch: An imperative style, high-performance deep learning library

Reference 34

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Observation 3cb19df9-0835-4761-ab44-47c9de53eb72 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

Physics Informed Distillation for Diffusion Models Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 35

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Observation 6fb66942-eee1-4909-b35b-133cd35be5b4 · outbound

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

Physics Informed Distillation for Diffusion Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 36

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

Physics Informed Distillation for Diffusion Models Photorealistic text-to-image diffusion models with deep language understanding

Reference 37

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This paper cites Image super-resolution via iterative refinement.

Physics Informed Distillation for Diffusion Models Image super-resolution via iterative refinement

Reference 38

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This paper cites Progressive distillation for fast sampling of diffusion models.

Physics Informed Distillation for Diffusion Models Progressive distillation for fast sampling of diffusion models

Reference 39

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This paper cites Improved techniques for training gans.

Physics Informed Distillation for Diffusion Models Improved techniques for training gans

Reference 40

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Observation ed1a6d06-8d85-4edf-964f-592339414ddb · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Physics Informed Distillation for Diffusion Models Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 41

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Physics Informed Distillation for Diffusion Models Deep unsupervised learning using nonequilibrium thermodynamics

Reference 42

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This paper cites Denoising diffusion implicit models.

Physics Informed Distillation for Diffusion Models Denoising diffusion implicit models

Reference 43

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

Physics Informed Distillation for Diffusion Models Score-based generative modeling through stochastic differential equations

Reference 44

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Observation 6ac8fad3-108f-44fa-a73f-09208749dc38 · outbound

This paper cites Consistency models.

Physics Informed Distillation for Diffusion Models Consistency models

Reference 45

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This paper cites On the theory of the brownian motion.

Physics Informed Distillation for Diffusion Models On the theory of the brownian motion

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:45:48.545733Z

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This paper cites On the theory of the brownian motion ii.

Physics Informed Distillation for Diffusion Models On the theory of the brownian motion ii

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:45:48.536717Z

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Observation 64fc72e8-163f-4542-96af-b12178f03c36 · outbound

This paper cites Deblurring via stochastic refinement.

Physics Informed Distillation for Diffusion Models Deblurring via stochastic refinement

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:45:48.528087Z

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Observation d6678a76-8ec3-4d8f-ba7b-06f98b975cb3 · outbound

This paper cites Diffusion models for implicit image segmentation ensembles.

Physics Informed Distillation for Diffusion Models Diffusion models for implicit image segmentation ensembles

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:45:48.519667Z

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Observation ce365b66-c942-424b-9bb4-5942703434c4 · outbound

This paper cites Lipschitz Singularities in Diffusion Models.

Physics Informed Distillation for Diffusion Models Lipschitz Singularities in Diffusion Models

Reference 50

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This paper cites Fast sampling of diffusion models with exponential integrator.

Physics Informed Distillation for Diffusion Models Fast sampling of diffusion models with exponential integrator

Reference 51

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This paper cites Fast sampling of diffusion models via operator learning.

Physics Informed Distillation for Diffusion Models Fast sampling of diffusion models via operator learning

Reference 52

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Observation adbc955b-63b3-4402-a545-c49bbb837c1c · outbound

This paper cites write newline.

Physics Informed Distillation for Diffusion Models write newline

Reference 53

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Pith citing papers

Observation fb641a62-cdf4-4ed2-9470-34c0b7125752 · inbound

Parallel Decoding Distillation for Fast Image and Video Generation cites this paper.

Parallel Decoding Distillation for Fast Image and Video Generation Physics Informed Distillation for Diffusion Models

Reference 72

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Observation 5b0eab64-5348-45ec-921c-d4ddfaabeb35 · inbound

Amortized Moment Matching for Visual Generation cites this paper.

Amortized Moment Matching for Visual Generation Physics Informed Distillation for Diffusion Models

Reference 74

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