Paper Citation Record · LEDGER

Unified Continuous Generative Models

As of 18 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 8 inbound Pith citation observations for arXiv:2505.07447.

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

pith.paper-citation-record.v1
2505.07447 v2

Coverage vector

measured 77 of 77 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:25:31.306591Z

measured 85 of 85 standing notices

One-hop event checks from named stored sources.

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measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:19:54.482983Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:11:01.756825Z

Reference resolution

77 of 77 outbound references displayed

verified exact0
verified fuzzy13
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External citation measurements

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Outbound references

Observation 76fce76b-5633-41f1-88b1-96d4e4f2f802 · outbound

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

Unified Continuous Generative Models All are worth words: A vit backbone for diffusion models

Reference 1

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Observation 42fe7a0f-c2a7-4ab3-8a37-9a5b611db4c6 · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Unified Continuous Generative Models Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 2

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Observation 9c48ab1f-e7fc-46b8-ad48-fa0c810c4c96 · outbound

This paper cites Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models.

Unified Continuous Generative Models Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models

Reference 3

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Observation f44a794a-f461-410b-8d6f-fc9d24a7d2b1 · outbound

This paper cites Sana-sprint: One-step diffusion with continuous-time consistency distillation.

Unified Continuous Generative Models Sana-sprint: One-step diffusion with continuous-time consistency distillation

Reference 4

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Observation 95149909-4900-4e2f-89c1-81c38096a664 · outbound

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

Unified Continuous Generative Models Imagenet: A large- scale hierarchical image database

Reference 5

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Observation 29694af2-7a47-4673-9f2c-c3185c7e92ed · outbound

This paper cites Diffusion models beat gans on image synthesis.

Unified Continuous Generative Models Diffusion models beat gans on image synthesis

Reference 6

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Observation 1c43c5de-2773-4c60-a7ce-99ef0da53eb4 · outbound

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

Unified Continuous Generative Models Taming transformers for high-resolution image synthesis

Reference 7

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Observation badfca7c-0833-4b8d-a818-da90b4e1bfae · outbound

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

Unified Continuous Generative Models Scaling rectified flow trans- formers for high-resolution image synthesis

Reference 8

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Observation ca134af3-facf-41df-8e99-9d99bd7256d6 · outbound

This paper cites One Step Diffusion via Shortcut Models.

Unified Continuous Generative Models One Step Diffusion via Shortcut Models

Reference 9

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Observation 97b362ec-e2be-421e-96bb-c0137dbbefbc · outbound

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

Unified Continuous Generative Models Masked diffusion transformer is a strong image synthesizer

Reference 10

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Observation efa300df-5898-434d-b732-5864eb4a0ff7 · outbound

This paper cites Consistency Models Made Easy.

Unified Continuous Generative Models Consistency Models Made Easy

Reference 11

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Observation 92c4cbed-9331-463c-bbd7-9e8d80c00bec · outbound

This paper cites Generative adversarial networks.

Unified Continuous Generative Models Generative adversarial networks

Reference 12

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Observation 72c48230-f3b7-4e25-96b7-47416543edff · outbound

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

Unified Continuous Generative Models Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 13

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Observation e5d8116d-f07e-45b7-be24-2449a9c91cbd · outbound

This paper cites Classifier-Free Diffusion Guidance.

Unified Continuous Generative Models Classifier-Free Diffusion Guidance

Reference 14

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Observation 37d4c355-24ad-4883-9cbc-084d56d2a629 · outbound

This paper cites Denoising diffusion probabilistic models.

Unified Continuous Generative Models Denoising diffusion probabilistic models

Reference 15

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Observation b29c6a82-81c8-4770-8946-1ffd193c2e68 · outbound

This paper cites Video diffusion models.

Unified Continuous Generative Models Video diffusion models

Reference 16

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Observation da9e9a5f-c013-4d41-8169-b144e7ab16d7 · outbound

This paper cites Scaling up gans for text-to-image synthesis.

Unified Continuous Generative Models Scaling up gans for text-to-image synthesis

Reference 17

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Observation 6b3db73d-75e4-4e88-b880-0382ed65d0f8 · outbound

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

Unified Continuous Generative Models Elucidating the design space of diffusion-based generative models

Reference 18

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Observation 24bed9a7-8d34-4c26-a241-1fe3c879f6ac · outbound

This paper cites Guiding a diffusion model with a bad version of itself.

Unified Continuous Generative Models Guiding a diffusion model with a bad version of itself

Reference 19

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Observation 9a48af75-5792-46a3-bd3c-cfa78f4df662 · outbound

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

Unified Continuous Generative Models Analyzing and improving the training dynamics of diffusion models

Reference 20

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Observation 5aa17244-a1b2-42cd-8f34-b1b3f1cf00f5 · outbound

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

Unified Continuous Generative Models Learning multiple layers of features from tiny images

Reference 21

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Observation 1b62f498-99d3-451d-b8b7-a40a41f2ed63 · outbound

This paper cites Cifar-10 and cifar-100 datasets.

Unified Continuous Generative Models Cifar-10 and cifar-100 datasets

Reference 22

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Observation 6511fc48-0ec9-4bf8-81bb-04ee51bf3c65 · outbound

This paper cites Repa-e: Unlocking vae for end-to-end tuning with latent diffusion transformers.

Unified Continuous Generative Models Repa-e: Unlocking vae for end-to-end tuning with latent diffusion transformers

Reference 23

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Observation 348fa4d1-42d1-4294-825b-598851c23b63 · outbound

This paper cites Autoregressive image generation without vector quantization.

Unified Continuous Generative Models Autoregressive image generation without vector quantization

Reference 24

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Observation e9c56877-1522-4882-bcc6-d1bf2daaf180 · outbound

This paper cites Flow Matching for Generative Modeling.

Unified Continuous Generative Models Flow Matching for Generative Modeling

Reference 25

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Observation 2544e12b-bf62-438b-bdca-1dcf81fcfbba · outbound

This paper cites On the Variance of the Adaptive Learning Rate and Beyond.

Unified Continuous Generative Models On the Variance of the Adaptive Learning Rate and Beyond

Reference 26

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Observation 2ed92b07-e33c-46e3-a4e8-33b1629d12ae · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Unified Continuous Generative Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 27

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Observation c0db0787-a25e-48ff-94f8-fc1ff07b40ae · outbound

This paper cites Decoupled Weight Decay Regularization.

Unified Continuous Generative Models Decoupled Weight Decay Regularization

Reference 28

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Observation f71b1497-b999-4bc8-bbbb-d5887467712f · outbound

This paper cites Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models.

Unified Continuous Generative Models Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models

Reference 29

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Observation ecf0bd49-d0c3-40af-8835-6cba3fc23e2d · outbound

This paper cites Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers.

Unified Continuous Generative Models Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers

Reference 30

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Observation 8e9b0b37-c5c6-4f68-bb3a-8ca9ba3adfeb · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Unified Continuous Generative Models PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 31

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Observation 0bf0ff08-e2eb-4b15-83b3-bee8d43449ac · outbound

This paper cites Scikit- learn: Machine learning in python.

Unified Continuous Generative Models Scikit- learn: Machine learning in python

Reference 32

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Observation 0531a1df-d56e-4673-94c6-d00ea139c434 · outbound

This paper cites Scalable diffusion models with transformers.

Unified Continuous Generative Models Scalable diffusion models with transformers

Reference 33

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Observation ead167ad-f3d9-4ae1-b2d9-9c9ef6359d14 · outbound

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

Unified Continuous Generative Models High- resolution image synthesis with latent diffusion models

Reference 34

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Observation 66e61149-366c-4acf-a385-b8bb070a0284 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Unified Continuous Generative Models Progressive Distillation for Fast Sampling of Diffusion Models

Reference 35

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Observation f6429375-fb6c-4c83-aac7-a92aaa18d301 · outbound

This paper cites Stylegan-xl: Scaling stylegan to large diverse datasets.

Unified Continuous Generative Models Stylegan-xl: Scaling stylegan to large diverse datasets

Reference 36

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Observation 9e6dbddf-ac28-48cd-a967-b4b9f3c7f04b · outbound

This paper cites GLU Variants Improve Transformer.

Unified Continuous Generative Models GLU Variants Improve Transformer

Reference 37

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This paper cites Denoising Diffusion Implicit Models.

Unified Continuous Generative Models Denoising Diffusion Implicit Models

Reference 38

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This paper cites Improved Techniques for Training Consistency Models.

Unified Continuous Generative Models Improved Techniques for Training Consistency Models

Reference 39

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

Unified Continuous Generative Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 40

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This paper cites Consistency Models.

Unified Continuous Generative Models Consistency Models

Reference 41

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This paper cites Roformer: Enhanced transformer with rotary position embedding.

Unified Continuous Generative Models Roformer: Enhanced transformer with rotary position embedding

Reference 42

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This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

Unified Continuous Generative Models Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 43

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This paper cites Diffusion Models without Classifier-free Guidance.

Unified Continuous Generative Models Diffusion Models without Classifier-free Guidance

Reference 44

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Observation ccc0a91f-729f-4d1e-9afd-a1b15776c590 · outbound

This paper cites Visual autoregressive modeling: Scalable image generation via next-scale prediction.

Unified Continuous Generative Models Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 45

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This paper cites Attention is all you need.

Unified Continuous Generative Models Attention is all you need

Reference 46

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This paper cites DDT: Decoupled Diffusion Transformer.

Unified Continuous Generative Models DDT: Decoupled Diffusion Transformer

Reference 47

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This paper cites SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers.

Unified Continuous Generative Models SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Reference 48

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This paper cites Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models.

Unified Continuous Generative Models Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 49

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This paper cites One-step diffusion with distribution matching distillation.

Unified Continuous Generative Models One-step diffusion with distribution matching distillation

Reference 50

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This paper cites Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation.

Unified Continuous Generative Models Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 51

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

Unified Continuous Generative Models Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 52

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Unified Continuous Generative Models Root mean square layer normalization

Reference 53

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Unified Continuous Generative Models Fast Training of Diffusion Models with Masked Transformers

Reference 54

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This paper cites Inductive Moment Matching.

Unified Continuous Generative Models Inductive Moment Matching

Reference 55

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This paper cites C.1.3 Detailed Implementation Details Experiments were conducted on a cluster equipped with 8 H800 GPUs, each with 80 GB of VRAM.

Unified Continuous Generative Models C.1.3 Detailed Implementation Details Experiments were conducted on a cluster equipped with 8 H800 GPUs, each with 80 GB of VRAM

Reference 56

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Unified Continuous Generative Models TrigLinear

Reference 57

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Unified Continuous Generative Models Unresolved cited work

Reference 58

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Unified Continuous Generative Models Unresolved cited work

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Unified Continuous Generative Models Unresolved cited work

Reference 60

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Unified Continuous Generative Models Unresolved cited work

Reference 61

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Unified Continuous Generative Models Unresolved cited work

Reference 62

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This paper cites Additionally, we also include autoregressive models [ 24, 45, 51] as the baselines, which generate data sequentially, often in discrete domains.

Unified Continuous Generative Models Additionally, we also include autoregressive models [ 24, 45, 51] as the baselines, which generate data sequentially, often in discrete domains

Reference 63

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T22:25:32.237566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a5dea9ff-626d-4cbd-8fc3-2a22aae2b9d4 · outbound

This paper cites Since z and x are independent, E[xt| x0 = x] = γ(t) x.

Unified Continuous Generative Models Since z and x are independent, E[xt| x0 = x] = γ(t) x

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:32.221443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 04f68ea4-7538-4ac2-885e-c0e1a5f3d23a · outbound

This paper cites The covariance of xt given x0 is Var(xt| x0) =α(t)2 Id.

Unified Continuous Generative Models The covariance of xt given x0 is Var(xt| x0) =α(t)2 Id

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:32.205985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 38ae8d1a-b7e8-4c44-b5ef-e3404d27d740 · outbound

This paper cites By general theory (see, e.g., de Bortoli et al.), the probability flow ODE associated with the SDE dxt = f(xt,t ) dt +g(t) dwt is d xt dt = f(xt,t ) − 1 2g(t)2∇xt logpt(xt).

Unified Continuous Generative Models By general theory (see, e.g., de Bortoli et al.), the probability flow ODE associated with the SDE dxt = f(xt,t ) dt +g(t) dwt is d xt dt = f(xt,t ) − 1 2g(t)2∇xt logpt(xt)

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:32.189334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 115d2d6a-2657-4b12-9e30-5cf5c0f99c13 · outbound

This paper cites an unresolved cited work.

Unified Continuous Generative Models Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:25:32.174201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0af6d3f3-ebdf-4173-8997-47bc3bf503ae · outbound

This paper cites an unresolved cited work.

Unified Continuous Generative Models Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:25:32.155425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f108b026-02b2-46f7-8479-2982df37e105 · outbound

This paper cites an unresolved cited work.

Unified Continuous Generative Models Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:25:32.135807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f48b3543-59ae-4616-a2d0-7f7c82e6fc81 · outbound

This paper cites Substituting these into the general drift gives dxt dt =− xt 1−t + t (1−t) Σt h xt−m (1−t) tanh m (1−t) Σt xt i , which is the claimed closed-form Probability-Flow ODE.

Unified Continuous Generative Models Substituting these into the general drift gives dxt dt =− xt 1−t + t (1−t) Σt h xt−m (1−t) tanh m (1−t) Σt xt i , which is the claimed closed-form Probability-Flow ODE

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:32.120076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 43946f21-7803-4f45-90a1-605c5e61e7ae · outbound

This paper cites an unresolved cited work.

Unified Continuous Generative Models Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:25:32.104870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4403fa7f-e271-4f6e-be60-23c99039547c · outbound

This paper cites an unresolved cited work.

Unified Continuous Generative Models Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:25:32.089539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3af68b8a-1812-4bca-8a19-bcd75e06e2c1 · outbound

This paper cites 44 0.0 0.2 0.4 0.6 0.8 1.0 Time t 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5Prob.

Unified Continuous Generative Models 44 0.0 0.2 0.4 0.6 0.8 1.0 Time t 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5Prob

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:32.060679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e923a75f-9cdb-4784-b5a8-58fccbe43e77 · outbound

This paper cites Replacing x(ti) by ˜xi in the leading terms gives x(ti+1) = ˜xi +h vi + h2 2 v′(˜xi,ti) +O(h3).

Unified Continuous Generative Models Replacing x(ti) by ˜xi in the leading terms gives x(ti+1) = ˜xi +h vi + h2 2 v′(˜xi,ti) +O(h3)

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:32.044078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d2266423-2a20-46a5-978c-b0aa873a3769 · outbound

This paper cites base point.

Unified Continuous Generative Models base point

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:32.028942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6a5bcfa4-3a07-4df8-8307-57767ba00275 · outbound

This paper cites By the Implicit Function Theorem, for each fixed (t1,t 0,t 2) near ( 1 4, 1 2, 3.

Unified Continuous Generative Models By the Implicit Function Theorem, for each fixed (t1,t 0,t 2) near ( 1 4, 1 2, 3

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:32.010615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f9723611-34d8-4604-9375-99f0fc64c558 · outbound

This paper cites subtract-then-scale.

Unified Continuous Generative Models subtract-then-scale

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:31.984756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 013941b4-868a-44bf-966b-d9b4378790f2 · inbound

Transition Models: Rethinking the Generative Learning Objective cites this paper.

Transition Models: Rethinking the Generative Learning Objective Unified Continuous Generative Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-05T10:19:54.482983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0611a257-3859-4b29-a303-08abf920d6f7 · inbound

Transition Matching Distillation for Fast Video Generation cites this paper.

Transition Matching Distillation for Fast Video Generation Unified Continuous Generative Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T10:35:06.599060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df2a2ba1-251c-4ded-85c3-2da0c3cdb4e2 · inbound

Stabilizing Consistency Training: A Flow Map Analysis and Self-Distillation cites this paper.

Stabilizing Consistency Training: A Flow Map Analysis and Self-Distillation Unified Continuous Generative Models

Reference 6851

Resolution: unresolved; no resolver link, observed 2026-08-03T06:33:25.026644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b194ae7-4857-420d-9e05-34282f6b6f50 · inbound

Self-Adversarial One Step Generation via Condition Shifting cites this paper.

Self-Adversarial One Step Generation via Condition Shifting Unified Continuous Generative Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:11:01.760247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation acf92aee-f3a4-4b3a-8348-b4d9862dfa14 · inbound

LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model cites this paper.

LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model Unified Continuous Generative Models

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T00:49:48.322712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 429aa4a3-ea4b-46be-b6f0-ba7e3a6cc9b4 · inbound

Three-Body Scattering for Generative Modeling cites this paper.

Three-Body Scattering for Generative Modeling Unified Continuous Generative Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-01T15:49:34.306071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2f72065-b2b4-44a6-861b-4b6816f0ab1a · inbound

Amortized Moment Matching for Visual Generation cites this paper.

Amortized Moment Matching for Visual Generation Unified Continuous Generative Models

Reference 95

Resolution: unresolved; no resolver link, observed 2026-07-30T18:58:28.186475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52574316-9c96-4a09-993e-de09bc29a79e · inbound

CMuon: Accelerating and Stabilizing Diffusion Transformer Training via Chunked Momentum Orthogonalization cites this paper.

CMuon: Accelerating and Stabilizing Diffusion Transformer Training via Chunked Momentum Orthogonalization Unified Continuous Generative Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T06:04:05.398635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.