Paper Citation Record · LEDGER

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity

As of 20 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2501.15448.

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

pith.paper-citation-record.v1
2501.15448 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:20:56.839724Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact1
verified fuzzy20
unresolved18
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 724b54ef-22a1-4606-b5e4-0df5395ac04a · outbound

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

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.705297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e16eb866-bb25-4dab-b1aa-813180a442f7 · outbound

This paper cites Align your latents: High-resolution video synthesis with latent diffusion models,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Align your latents: High-resolution video synthesis with latent diffusion models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.437780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b1d7f048-99e9-4a9a-8c46-6a6dfef1f579 · outbound

This paper cites Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.713396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f56e0bee-0a9c-4240-8e7b-4624d03767db · outbound

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

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Elucidating the design space of diffusion-based generative models,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.426034Z

Source-reported events for the cited work

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Observation f12a7a2c-e4a4-4878-acba-ea5aef964d19 · outbound

This paper cites Analyzing and Improving the Training Dynamics of Diffusion Models.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Analyzing and Improving the Training Dynamics of Diffusion Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.720795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e4fb292-baf3-4262-bbbf-686c5e1b6e40 · outbound

This paper cites Vs-quant: Per-vector scaled quantization for accurate low-precision neural network inference,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Vs-quant: Per-vector scaled quantization for accurate low-precision neural network inference,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.724166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b068ebe2-f159-4119-a03a-0a79155ae484 · outbound

This paper cites Post-training quantiza- tion on diffusion models,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Post-training quantiza- tion on diffusion models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.408000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f8153009-588e-4a3a-bba6-a9959f1e56fd · outbound

This paper cites Q-diffusion: Quantizing diffusion models,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Q-diffusion: Quantizing diffusion models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.395931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e7cb781e-11b7-4d89-9d20-794bb22a3fba · outbound

This paper cites Ptqd: Accurate post-training quantization for diffusion models,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Ptqd: Accurate post-training quantization for diffusion models,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.385257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4e70d620-0775-4cdc-81dc-093dab522bde · outbound

This paper cites Efficient Quantization Strategies for Latent Diffusion Models.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Efficient Quantization Strategies for Latent Diffusion Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.736928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9401c9a5-81c7-4f3d-b80e-5e92bb54a026 · outbound

This paper cites TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.740338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b01cabb0-93e5-4723-b7f7-53e1c7cab0f7 · outbound

This paper cites Tfmq-dm: Temporal feature maintenance quantization for diffusion models,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Tfmq-dm: Temporal feature maintenance quantization for diffusion models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.373719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation febce3e2-e2e8-415b-9784-1a3a83c8f098 · outbound

This paper cites Svdqunat: Absorbing outliers by low-rank com- ponents for 4-bit diffusion models,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Svdqunat: Absorbing outliers by low-rank com- ponents for 4-bit diffusion models,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.746992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4dda9cb4-6071-4d84-9a5a-bfe4f7ce73c4 · outbound

This paper cites Accelerating Sparse Deep Neural Networks.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Accelerating Sparse Deep Neural Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.749918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92b23be9-c34f-4f12-8e44-36e1a0aab128 · outbound

This paper cites Structural pruning for diffusion models,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Structural pruning for diffusion models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.360448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8647eec8-e28c-4c89-98c0-59e1ed1cedae · outbound

This paper cites Sparsedm: Toward sparse efficient diffusion models,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Sparsedm: Toward sparse efficient diffusion models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.349722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b412701d-dce9-4ec9-8675-8b7cf53aa6f0 · outbound

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

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Learning multiple layers of features from tiny images,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.759713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7462036-d3b3-432b-89ce-80e826ec1ca0 · outbound

This paper cites Stargan v2: Diverse image synthesis for multiple domains,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Stargan v2: Diverse image synthesis for multiple domains,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.331740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 74db5ae2-f6c1-44a8-bdf3-853d564954a6 · outbound

This paper cites A style-based generator architecture for generative adversarial networks,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity A style-based generator architecture for generative adversarial networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.320902Z

Source-reported events for the cited work

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Observation 12967aab-4473-459f-aa4c-f9a9d8095c0d · outbound

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

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Imagenet: A large-scale hierarchical image database,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.769808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7329c8e9-04d3-4b58-a14a-54891fffe416 · outbound

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

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.773440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43f6fff7-a872-4f6f-87a4-8e47fe87f75a · outbound

This paper cites Microscaling Data Formats for Deep Learning.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Microscaling Data Formats for Deep Learning

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0be0670e-4549-4984-be59-20d27ee3e476 · outbound

This paper cites Nvidia blackwell platform: Advancing generative ai and accelerated computing,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Nvidia blackwell platform: Advancing generative ai and accelerated computing,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.293548Z

Source-reported events for the cited work

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Observation 5b61f872-52f5-4f05-b7c6-be251cee8795 · outbound

This paper cites Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.784435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feea7b6e-d480-48e5-95dc-8d0fc26c2209 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Imagenet classification with deep convolutional neural networks,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.788925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e598b340-5a18-4987-b636-5e7396a9dc81 · outbound

This paper cites The Sparsity Roofline: Understanding the Hardware Limits of Sparse Neural Networks.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity The Sparsity Roofline: Understanding the Hardware Limits of Sparse Neural Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.792396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d28357e-75d8-4f5e-aae0-aa6f9dc0e3c0 · outbound

This paper cites Magnet: A modular accelerator generator for neural networks,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Magnet: A modular accelerator generator for neural networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.265898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3ad68e9d-4e43-4759-9136-a55a41cf94cf · outbound

This paper cites In-datacenter performance analysis of a tensor processing unit,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity In-datacenter performance analysis of a tensor processing unit,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.254458Z

Source-reported events for the cited work

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Observation 9b0a8092-55ad-4342-93df-f4c402912bf0 · outbound

This paper cites Maeri: Enabling flexible dataflow mapping over dnn accelerators via reconfigurable intercon- nects,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Maeri: Enabling flexible dataflow mapping over dnn accelerators via reconfigurable intercon- nects,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.803336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaf93035-1a25-4598-9852-b95c643e35f9 · outbound

This paper cites Eie: Efficient inference engine on compressed deep neural network,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Eie: Efficient inference engine on compressed deep neural network,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.806816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 536ab053-edab-472a-82b1-b4fd7f3ce7a2 · outbound

This paper cites Cambricon-x: An accelerator for sparse neural networks,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Cambricon-x: An accelerator for sparse neural networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.228208Z

Source-reported events for the cited work

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Observation a7052210-aff9-4363-9e3f-2a751dc06c92 · outbound

This paper cites Sparten: A sparse tensor accelerator for convolutional neural networks,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Sparten: A sparse tensor accelerator for convolutional neural networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.216805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 795c7c1f-2b7d-4885-9305-7c2674d2bef8 · outbound

This paper cites Extensor: An accelerator for sparse tensor algebra,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Extensor: An accelerator for sparse tensor algebra,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.203867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2adec96d-833a-4de8-af13-dbda1ea20c66 · outbound

This paper cites Sigma: A sparse and irregular gemm ac- celerator with flexible interconnects for dnn training,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Sigma: A sparse and irregular gemm ac- celerator with flexible interconnects for dnn training,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.192201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1723a965-d510-4a0d-8101-a439272ab48d · outbound

This paper cites Tensaurus: A versatile accelerator for mixed sparse-dense tensor com- putations,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Tensaurus: A versatile accelerator for mixed sparse-dense tensor com- putations,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.179098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 92cdded2-06ae-43b3-86b4-272b9eda3daf · outbound

This paper cites Griffin: Rethinking sparse optimization for deep learning architectures,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Griffin: Rethinking sparse optimization for deep learning architectures,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.167581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 98e78542-5345-4aed-b9c6-776c2a4745fd · outbound

This paper cites Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:20:56.877780Z

Source-reported events for the cited work

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Observation 42079df5-79a7-4a54-8cfb-b914f1a03c0c · outbound

This paper cites Stonne: Enabling cycle-level microarchitectural simulation for dnn inference accelerators,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Stonne: Enabling cycle-level microarchitectural simulation for dnn inference accelerators,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:20:57.156364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd5a9239-dee1-4fb8-b1a6-21cc415926b9 · outbound

This paper cites Video diffusion models,.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Video diffusion models,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T14:20:56.839724Z

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Pith citing papers

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