Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising | Paper Citation Record

Observation e09e7855-7d29-4cfb-936a-80a53aab357c · outbound

This paper cites eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Reference 1

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Observation 3df01fed-48ca-41f9-a58f-21d5db4d3a20 · outbound

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

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising All are worth words: A vit backbone for diffusion models

Reference 2

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Observation 9ae19ae8-b395-46a7-bb9e-12430f2f8ed7 · outbound

This paper cites Video generation models as world simulators.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Video generation models as world simulators

Reference 3

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Observation 91f8a7eb-8ac9-4f3b-b030-28a5af73d8b7 · outbound

This paper cites Exploring Vision Transformers as Diffusion Learners.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Exploring Vision Transformers as Diffusion Learners

Reference 4

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Observation 86041bb7-e9ac-42c9-b9d9-53b3711eacc4 · outbound

This paper cites GenTron: Diffusion Transformers for Image and Video Generation.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising GenTron: Diffusion Transformers for Image and Video Generation

Reference 5

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Observation 478355b4-2cc5-4631-bbb8-574d2b0c7abe · outbound

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

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Imagenet: A large-scale hierarchical image database

Reference 6

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Observation ce57dd8a-49ae-4ac4-af19-c4456dd1bcce · outbound

This paper cites Diffusion models beat gans on image synthesis.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Diffusion models beat gans on image synthesis

Reference 7

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Observation f617ccf7-dd3a-4209-bae5-83171a5d65db · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Observation 5bc6faaa-2d6a-4637-bd78-2ac0b3948311 · outbound

This paper cites Structural pruning for diffusion models.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Structural pruning for diffusion models

Reference 9

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Observation b7976958-de29-4287-820e-6fdefb6eb2bf · outbound

This paper cites Masked diffusion transformer is a strong image synthesizer.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Masked diffusion transformer is a strong image synthesizer

Reference 10

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Observation 2c8f8835-bd6a-4f2c-b769-b8c64fb971b5 · outbound

This paper cites Vector quantized diffusion model for text-to-image synthesis.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Vector quantized diffusion model for text-to-image synthesis

Reference 11

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Observation 922c3e35-114f-4331-ae46-89babbf595d3 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Masked autoencoders are scalable vision learners

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:01.692135Z

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Observation a677f498-56df-4e31-8e20-a19c948b3c53 · outbound

This paper cites Denoising diffusion probabilistic models.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Denoising diffusion probabilistic models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:01.701036Z

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Observation b2ddfb1a-6d76-4bfc-b6aa-b9f225682f53 · outbound

This paper cites U-dit tts: U-diffusion vision transformer for text-to-speech.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising U-dit tts: U-diffusion vision transformer for text-to-speech

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:01.706174Z

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Observation c61d4cb6-a3f6-406f-82bc-f1cd49c3df99 · outbound

This paper cites Multi- architecture multi-expert diffusion models.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Multi- architecture multi-expert diffusion models

Reference 16

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Observation dd9cdbe8-05ed-4898-95ac-1b2730ecaafb · outbound

This paper cites Dlt: Conditioned layout generation with joint discrete-continuous diffusion layout transformer.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Dlt: Conditioned layout generation with joint discrete-continuous diffusion layout transformer

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:01.715869Z

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Observation 2757ffc5-196f-4b54-8931-3f979cb72235 · outbound

This paper cites OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:37:02.255214Z

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Observation 2b0fb5cb-361f-47e4-9a88-167061e57aa5 · outbound

This paper cites ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:01.727003Z

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Observation 9dc8d7ef-2eb3-4b3f-8a56-8ce0ad73bbef · outbound

This paper cites Vdt: General- purpose video diffusion transformers via mask modeling.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Vdt: General- purpose video diffusion transformers via mask modeling

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:02.731235Z

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Observation da9157c3-83f6-428e-a7e4-13e07865c2af · outbound

This paper cites Latte: Latent Diffusion Transformer for Video Generation.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Latte: Latent Diffusion Transformer for Video Generation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:01.738475Z

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Observation 4a68d741-60bd-49d2-abdc-1738ce2b4c97 · outbound

This paper cites DeepCache: Accelerating Diffusion Models for Free.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising DeepCache: Accelerating Diffusion Models for Free

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:01.743326Z

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Observation 3b4b616e-30e0-4453-a1ae-367a6a960a6f · outbound

This paper cites Deepcache: Accelerating diffusion models for free.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Deepcache: Accelerating diffusion models for free

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:02.714959Z

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Observation a0cf65a8-03d7-48b9-8cdd-c20fa9fb645e · outbound

This paper cites Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:01.753229Z

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Observation e1627879-2aae-4396-bedb-7b2cc80e9000 · outbound

This paper cites Dit-3d: Exploring plain diffusion transformers for 3d shape generation.Advances in Neural Information Processing Systems, 36, 2024.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Dit-3d: Exploring plain diffusion transformers for 3d shape generation.Advances in Neural Information Processing Systems, 36, 2024

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:02.698746Z

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Observation 4fa4f3d0-7b84-41af-8967-1977d2aa5fbf · outbound

This paper cites Improved denoising diffusion probabilistic models.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Improved denoising diffusion probabilistic models

Reference 26

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Observation 7215a1e1-fbdc-4cde-809f-5253e7b2b1ed · outbound

This paper cites T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:01.773861Z

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Observation 94b1bad7-2e89-4b1f-a466-9a9cbe89b3f3 · outbound

This paper cites Denoising Task Routing for Diffusion Models.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Denoising Task Routing for Diffusion Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:01.782192Z

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Observation d3d31d5c-501c-49d8-9d9a-c2e720764d8e · outbound

This paper cites Scalable diffusion models with transformers.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Scalable diffusion models with transformers

Reference 29

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Observation b2ccae28-43e9-46c5-9f77-72047a8d562f · outbound

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

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising High-resolution image synthesis with latent diffusion models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:01.792499Z

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Observation 3659ef08-0942-45f7-917f-fa9f639fc8fd · outbound

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

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising U-net: Convolutional networks for biomedical image segmentation

Reference 31

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Observation 97efeea4-a0e7-40a5-bc10-f92316167333 · outbound

This paper cites Denoising Diffusion Implicit Models.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Denoising Diffusion Implicit Models

Reference 32

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Observation 157d2d24-3436-423d-8522-725e17380e66 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Generative modeling by estimating gradients of the data distribution

Reference 33

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Observation 86ebf3e5-d21a-4c52-91c0-2e68409abe4b · outbound

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

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Score-Based Generative Modeling through Stochastic Differential Equations

Reference 34

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Observation 81daa912-513c-48b3-840f-7e5c6bc6e7ab · outbound

This paper cites Attention is all you need.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Attention is all you need

Reference 35

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Observation 5501da59-4040-4ec2-91e0-96a2ba876288 · outbound

This paper cites Factorizing knowledge in neural networks.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Factorizing knowledge in neural networks

Reference 36

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Observation f3245b59-2c58-4461-9fbe-108f3d840614 · outbound

This paper cites Diffusion probabilistic model made slim.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Diffusion probabilistic model made slim

Reference 37

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Observation e852ad19-8585-46eb-9618-2dc2e92f65bd · outbound

This paper cites Deep model reassembly.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Deep model reassembly

Reference 38

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Observation 8a220f16-0eee-475b-bbc3-a845355d8a87 · outbound

This paper cites Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model

Reference 39

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Observation 18ab7e93-b2b8-4d33-b6d6-936aa6fae1bc · outbound

This paper cites Fast Training of Diffusion Models with Masked Transformers.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Fast Training of Diffusion Models with Masked Transformers

Reference 40

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Observation d2d249f8-27f5-4707-ae05-4b4358e5d22f · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 41

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Observation ac3b99c8-63fe-448b-83bc-229dc638bef6 · outbound

This paper cites Limitations.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Limitations

Reference 42

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This paper cites Guidelines: 13 • The answer NA means that the paper does not include theoretical results.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: 13 • The answer NA means that the paper does not include theoretical results

Reference 43

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Observation 49af15f8-817b-4800-b5dd-b8ac1fc2be67 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: • The answer NA means that the paper does not include experiments

Reference 44

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This paper cites 14 Guidelines: • The answer NA means that paper does not include experiments requiring code.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising 14 Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 45

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Observation 925c7ab8-f43f-47e2-9ef4-bf882117d6b3 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: • The answer NA means that the paper does not include experiments

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:02.494549Z

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This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: • The answer NA means that the paper does not include experiments

Reference 47

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This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: • The answer NA means that the paper does not include experiments

Reference 48

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Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:02.444733Z

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Observation 7283e08e-c765-4918-8a36-1126a0d6c274 · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:02.427964Z

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Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: • The answer NA means that the paper poses no such risks

Reference 51

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Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: • The answer NA means that the paper does not use existing assets

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:02.399908Z

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Observation b7478e1b-cd98-4815-9e4d-bc5751f1148a · outbound

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Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising 17 Guidelines: • The answer NA means that the paper does not release new assets

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:02.383435Z

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This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:02.366049Z

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Observation 9ad315cc-6e38-4dea-b944-62e3b63152ae · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:37:02.350312Z

Source-reported events for the cited work

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