Paper Citation Record · LEDGER

DiC: Rethinking Conv3x3 Designs in Diffusion Models

As of 12 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2501.00603.

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

pith.paper-citation-record.v1
2501.00603 v2

Coverage vector

measured 48 of 48 reference resolution

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measured 48 of 48 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 04eba614-4960-48fa-bf32-624255e3d775 · outbound

This paper cites All are worth words: A vit backbone for diffusion models.

DiC: Rethinking Conv3x3 Designs in Diffusion Models All are worth words: A vit backbone for diffusion models

Reference 1

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Observation 66b97528-01bc-49bf-bb00-a6f11f5b3ca8 · outbound

This paper cites Pixart- α: Fast training of diffusion transformer for photorealistic text-to-image synthe- sis, 2023.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Pixart- α: Fast training of diffusion transformer for photorealistic text-to-image synthe- sis, 2023

Reference 2

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Observation b8efa337-f45a-4f86-996a-e56587d34ea4 · outbound

This paper cites PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation.

DiC: Rethinking Conv3x3 Designs in Diffusion Models PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Reference 3

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Observation 8206e668-dd7e-49b8-b633-cfab18ff93e4 · outbound

This paper cites Pixart- δ: Fast and controllable image generation with latent consistency models,.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Pixart- δ: Fast and controllable image generation with latent consistency models,

Reference 4

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Observation e3895d6a-575d-4893-977a-e15a9efbb9bf · outbound

This paper cites VisionLLaMA: A Unified LLaMA Backbone for Vision Tasks.

DiC: Rethinking Conv3x3 Designs in Diffusion Models VisionLLaMA: A Unified LLaMA Backbone for Vision Tasks

Reference 5

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Observation c58cb231-b2e8-43fa-ae5d-410a86e151ff · outbound

This paper cites Kaplan, and En- rico Shippole.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Kaplan, and En- rico Shippole

Reference 6

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Observation 5b7f9519-1f1b-466c-8a4b-b7003b9174c3 · outbound

This paper cites Deformable Convolutional Networks.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Deformable Convolutional Networks

Reference 7

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Observation c3ccb4c4-68c0-4a0d-ac06-096c2aec084d · outbound

This paper cites FlashAttention-2: Faster attention with better paral- lelism and work partitioning.

DiC: Rethinking Conv3x3 Designs in Diffusion Models FlashAttention-2: Faster attention with better paral- lelism and work partitioning

Reference 8

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Observation d1270cbe-4142-4882-b925-4b0b7caceeb7 · outbound

This paper cites Fu, Stefano Ermon, Atri Rudra, and Christopher Ré.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Fu, Stefano Ermon, Atri Rudra, and Christopher Ré

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.619807Z

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Observation 73a77eae-f0bf-4f2e-aa6f-355676bc21b6 · outbound

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

DiC: Rethinking Conv3x3 Designs in Diffusion Models Imagenet: A large-scale hierarchical image database

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.602909Z

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Observation 02b5d7b5-e7a8-48c3-9799-8637f6381f7d · outbound

This paper cites Diffusion models beat gans on image synthesis.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Diffusion models beat gans on image synthesis

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.585815Z

Source-reported events for the cited work

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Observation 0414385e-bdfc-4888-9ebd-10f630bfff8d · outbound

This paper cites Repvgg: Making vgg-style convnets great again.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Repvgg: Making vgg-style convnets great again

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.568110Z

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Observation 9e12288f-7b2e-426e-b57c-38a438d29160 · outbound

This paper cites Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs

Reference 13

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Observation e8b39502-b6d8-4d11-bcc7-8a33a505ed09 · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Scaling rectified flow transformers for high-resolution image synthesis

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.550275Z

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Observation 8877c30d-b4e8-4a62-9f8d-f2913a5d4f32 · outbound

This paper cites Wavelet Convolutions for Large Receptive Fields.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Wavelet Convolutions for Large Receptive Fields

Reference 15

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Observation 7b8a31e9-3998-474b-82c4-8652a773a038 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 16

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Observation 30d3ee82-07a7-4cc2-8248-a91bf92c6422 · outbound

This paper cites DiffiT: Diffusion Vision Transformers for Image Generation.

DiC: Rethinking Conv3x3 Designs in Diffusion Models DiffiT: Diffusion Vision Transformers for Image Generation

Reference 17

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Observation eba52f6c-9aa6-49c8-afb5-5c31ef393254 · outbound

This paper cites Deep Residual Learning for Image Recognition.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Deep Residual Learning for Image Recognition

Reference 18

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Source-reported events for the cited work

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Observation b9cc352d-4aca-48ae-a0d5-353514b1c855 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Denoising Diffusion Probabilistic Models

Reference 19

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Observation a8b330e5-6e34-4edf-9069-6471ab4f3335 · outbound

This paper cites simple diffusion: End-to-end diffusion for high resolution images.

DiC: Rethinking Conv3x3 Designs in Diffusion Models simple diffusion: End-to-end diffusion for high resolution images

Reference 20

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Observation eea21f28-f328-4b55-816f-3ff71b07b4d0 · outbound

This paper cites Scalable adaptive computation for iterative generation, 2023.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Scalable adaptive computation for iterative generation, 2023

Reference 21

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Observation e9bdbcb2-c420-4253-a9e5-7a68dac4a815 · outbound

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

DiC: Rethinking Conv3x3 Designs in Diffusion Models Analyzing and improving the training dynamics of diffusion models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.497527Z

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Observation c282cf4c-db49-4ff5-a741-5e4f2341c6f4 · outbound

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DiC: Rethinking Conv3x3 Designs in Diffusion Models Unresolved cited work

Reference 23

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Observation cf4846ad-00c4-4960-a6c2-20987a724405 · outbound

This paper cites Open-sora-plan, 2024.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Open-sora-plan, 2024

Reference 24

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Observation 730961ee-ba9d-457b-8e45-892466574a0d · outbound

This paper cites Flux, 2024.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Flux, 2024

Reference 25

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Observation 7666fb6b-65ee-4c0d-aa5e-1496b512959c · outbound

This paper cites Fast Algorithms for Convolutional Neural Networks.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Fast Algorithms for Convolutional Neural Networks

Reference 26

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Observation 98046c4e-4e9a-4afa-8cbf-d4abc493faeb · outbound

This paper cites Torchprofile, 2024.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Torchprofile, 2024

Reference 27

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Observation 4d8d0a76-6fa5-46e3-9586-494313a66a05 · outbound

This paper cites A convnet for the 2020s.

DiC: Rethinking Conv3x3 Designs in Diffusion Models A convnet for the 2020s

Reference 28

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Observation 1dfe2f4e-2048-496e-9f94-51d31051f776 · outbound

This paper cites SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.

DiC: Rethinking Conv3x3 Designs in Diffusion Models SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 29

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Observation f22437b0-a631-4bf7-af29-e4d99f9b93c0 · outbound

This paper cites Scalable diffusion models with transformers.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Scalable diffusion models with transformers

Reference 30

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Observation 308d558b-7fae-4a88-a301-ea6f114f8b22 · outbound

This paper cites Searching for Activation Functions.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Searching for Activation Functions

Reference 31

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Observation 1ffa7fe1-96bb-4f10-b777-6f09446b7636 · outbound

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

DiC: Rethinking Conv3x3 Designs in Diffusion Models High-resolution image synthesis with latent diffusion models

Reference 32

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Observation 4c851ce1-0640-4c19-a3a5-87ec45d34fd2 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

DiC: Rethinking Conv3x3 Designs in Diffusion Models U-net: Convolutional networks for biomedical image segmentation

Reference 33

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Observation 0496ea94-25bc-47c4-8ea4-16595ae41412 · outbound

This paper cites Very deep convo- lutional networks for large-scale image recognition.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Very deep convo- lutional networks for large-scale image recognition

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.339540Z

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Observation 4ecd8fdf-24e6-4ac2-85d6-e3c973e599ec · outbound

This paper cites Todo: To- ken downsampling for efficient generation of high-resolution images, 2024.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Todo: To- ken downsampling for efficient generation of high-resolution images, 2024

Reference 35

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Observation a76ff508-ee4d-41c3-b7e5-a99a453fde7c · outbound

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

DiC: Rethinking Conv3x3 Designs in Diffusion Models Score-based generative modeling through stochastic differential equations

Reference 36

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This paper cites Dim: Diffusion mamba for efficient high-resolution image synthesis, 2024.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Dim: Diffusion mamba for efficient high-resolution image synthesis, 2024

Reference 37

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Observation 14a36922-58a4-4c5b-a0c3-ea395b140723 · outbound

This paper cites U-dits: Downsample tokens in u-shaped diffusion transformers, 2024.

DiC: Rethinking Conv3x3 Designs in Diffusion Models U-dits: Downsample tokens in u-shaped diffusion transformers, 2024

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.274802Z

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Observation f81847df-0182-42b2-a6b0-6eb445d0ae91 · outbound

This paper cites U-repa: Aligning diffusion u-nets to vits, 2025.

DiC: Rethinking Conv3x3 Designs in Diffusion Models U-repa: Aligning diffusion u-nets to vits, 2025

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.258435Z

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Observation e9c1c523-ea80-46b7-9410-f490b8f4f3b1 · outbound

This paper cites Attention Is All You Need.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Attention Is All You Need

Reference 40

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This paper cites DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems.

DiC: Rethinking Conv3x3 Designs in Diffusion Models DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

Reference 41

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This paper cites Internimage: Exploring large-scale vision foundation models with deformable convo- lutions, 2023.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Internimage: Exploring large-scale vision foundation models with deformable convo- lutions, 2023

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.240068Z

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Observation d758b1e5-0410-4312-94e4-c86a90b64e01 · outbound

This paper cites Sana: Efficient high-resolution image synthesis with linear diffusion transformer, 2024.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Sana: Efficient high-resolution image synthesis with linear diffusion transformer, 2024

Reference 43

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This paper cites Enhancing vi- sion transformer: Amplifying non-linearity in feedforward network module.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Enhancing vi- sion transformer: Amplifying non-linearity in feedforward network module

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.202234Z

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DiC: Rethinking Conv3x3 Designs in Diffusion Models Unresolved cited work

Reference 45

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Observation ca9ee680-2297-478a-9bb8-4f542b8a6ef7 · outbound

This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 46

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This paper cites Open-sora: Democratizing efficient video production for all, 2024.

DiC: Rethinking Conv3x3 Designs in Diffusion Models Open-sora: Democratizing efficient video production for all, 2024

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:53.166038Z

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Observation 76b3f1cf-3061-4a3c-8d95-74e16c4bcecb · outbound

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DiC: Rethinking Conv3x3 Designs in Diffusion Models Unresolved cited work

Reference 48

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Pith citing papers

No inbound Pith citation observations are available.