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Paper Citation Record · LEDGER

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation

As of 11 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 1 inbound Pith citation observation for arXiv:2507.04290.

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

pith.paper-citation-record.v1
2507.04290 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:57:23.734370Z

measured 71 of 71 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T13:11:39.719989Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T13:14:53.449483Z

Reference resolution

70 of 70 outbound references displayed

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External citation measurements

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Outbound references

Observation 2cdb6f4d-7fe1-4e4e-84be-6bfd24ac2805 · outbound

This paper cites Denoising diffusion probabilistic models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Denoising diffusion probabilistic models,

Reference 1

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Observation b7046212-e4e6-4b92-b7fb-14493ddb4400 · outbound

This paper cites Diffusion models beat gans on image synthesis,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Diffusion models beat gans on image synthesis,

Reference 2

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Observation 8f82ce01-6dfb-4a73-8eb6-663154dd43be · outbound

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

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation High- resolution image synthesis with latent diffusion models,

Reference 3

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Observation c9245d86-98d3-4731-9d57-40c8f10b5f5d · outbound

This paper cites Text- guided mask-free local image retouching,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Text- guided mask-free local image retouching,

Reference 4

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Observation 5433f205-47a5-4927-85fb-a9635c238338 · outbound

This paper cites Vidm: Video implicit diffusion models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Vidm: Video implicit diffusion models,

Reference 5

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Source-reported events for the cited work

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Observation b32677a9-2474-40f8-a7f5-4b7deed740ab · outbound

This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 6

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Observation 7295d146-953f-4fa3-9a01-0f2d2733fc0d · outbound

This paper cites Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Reference 7

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Observation c4e53dc8-6308-4bc1-88a9-1011889139d7 · outbound

This paper cites Diffusion-based layer-wise semantic reconstruction for unsupervised out-of-distribution detection,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Diffusion-based layer-wise semantic reconstruction for unsupervised out-of-distribution detection,

Reference 8

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Observation 11584a02-ea06-44c6-9d91-bb6adbacb464 · outbound

This paper cites Osdface: One-step diffusion model for face restoration,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Osdface: One-step diffusion model for face restoration,

Reference 9

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Source-reported events for the cited work

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Observation bdb366e9-50f0-4711-99f6-962014b3a2e2 · outbound

This paper cites A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI

Reference 10

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Observation 5ffd6710-75d2-484e-8973-31fc363966ec · outbound

This paper cites Diffusion models in vision: A survey,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Diffusion models in vision: A survey,

Reference 11

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Observation b63a0f43-719a-46cf-94c6-e4a6faffe459 · outbound

This paper cites A resource-aware workload scheduling method for unbalanced gemms on gpus,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation A resource-aware workload scheduling method for unbalanced gemms on gpus,

Reference 12

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Source-reported events for the cited work

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Observation a27867d3-068b-4de6-97fb-a312350390b1 · outbound

This paper cites Sketch-fusion: A gradient compression method with multi-layer fusion for communication- efficient distributed training,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Sketch-fusion: A gradient compression method with multi-layer fusion for communication- efficient distributed training,

Reference 13

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Source-reported events for the cited work

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Observation 272b9f2e-f420-4ec5-8b8c-1c3a14e2784f · outbound

This paper cites Foundation models and intelligent decision-making: Progress, challenges, and perspectives,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Foundation models and intelligent decision-making: Progress, challenges, and perspectives,

Reference 14

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Observation d2ab61af-4706-425d-89f9-748a1f5fa789 · outbound

This paper cites A survey of quantization methods for efficient neural network infer- ence,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation A survey of quantization methods for efficient neural network infer- ence,

Reference 15

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Observation b9762ee9-bb06-4716-aeec-dade8746b094 · outbound

This paper cites Compression of convolutional neural networks: A short survey,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Compression of convolutional neural networks: A short survey,

Reference 16

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Source-reported events for the cited work

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Observation bb64a371-e120-4030-a486-199824502db3 · outbound

This paper cites Reg-ptq: Regression- specialized post-training quantization for fully quantized object detec- tor,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Reg-ptq: Regression- specialized post-training quantization for fully quantized object detec- tor,

Reference 17

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Observation 285dad94-bf83-4d76-a444-c717abaab85e · outbound

This paper cites Scp: A structure combination pruning method via structured sparse for deep convolutional neural net- works,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Scp: A structure combination pruning method via structured sparse for deep convolutional neural net- works,

Reference 18

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Observation 7b0c8a48-0ec2-4bef-9c8b-426375d76fa4 · outbound

This paper cites A survey of techniques for optimizing transformer inference,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation A survey of techniques for optimizing transformer inference,

Reference 19

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Observation 0745a72c-4674-486d-ae16-fb8f0a929cf9 · outbound

This paper cites Q-mamba: Towards more efficient mamba models via post-training quantization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Q-mamba: Towards more efficient mamba models via post-training quantization,

Reference 20

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Source-reported events for the cited work

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Observation 5ba1ab1c-54b5-46ad-9826-4c29e530bdf4 · outbound

This paper cites QMamba: On First Exploration of Vision Mamba for Image Quality Assessment.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation QMamba: On First Exploration of Vision Mamba for Image Quality Assessment

Reference 21

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Observation 1764b4e4-73de-4d2d-9015-b1c14d93593c · outbound

This paper cites Learned Step Size Quantization.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Learned Step Size Quantization

Reference 22

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Observation 9d634e07-11a1-4a77-aca4-5203781a9e58 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 23

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Observation 4d546754-4828-465c-992c-d8a737d5bef3 · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 24

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Observation ffa1d577-37d9-4203-8c20-0fcc588b32bf · outbound

This paper cites BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models

Reference 25

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Observation 3d316bb4-871e-4f26-8ab7-78fdf7c332e6 · outbound

This paper cites Q-dm: An efficient low-bit quantized diffusion model,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Q-dm: An efficient low-bit quantized diffusion model,

Reference 26

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Source-reported events for the cited work

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Observation dbaa5bf7-e016-4200-87da-7ef3e51e2456 · outbound

This paper cites BiDM: Pushing the Limit of Quantization for Diffusion Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation BiDM: Pushing the Limit of Quantization for Diffusion Models

Reference 27

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Observation 3b7ab945-efb3-4775-a249-7c125eaf362d · outbound

This paper cites Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming

Reference 28

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Observation 499b938e-349e-4366-bde9-e4bd329fc2c0 · outbound

This paper cites Towards accurate post- training network quantization via bit-split and stitching,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Towards accurate post- training network quantization via bit-split and stitching,

Reference 29

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Observation 5795d9c2-4698-4cd4-abac-13eb26129ca2 · outbound

This paper cites QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 30

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Observation fa9a5654-8135-48df-a2bb-d494ea34c72f · outbound

This paper cites Pd-quant: Post-training quantization based on prediction difference metric,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Pd-quant: Post-training quantization based on prediction difference metric,

Reference 31

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Source-reported events for the cited work

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Observation ed23d257-a762-43e2-b69f-3c8544d85646 · outbound

This paper cites EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models

Reference 32

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Observation c2b79ab8-91f8-411c-a13c-1cf7648720d7 · outbound

This paper cites Mpq-dm: Mixed precision quantization for extremely low bit diffusion models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Mpq-dm: Mixed precision quantization for extremely low bit diffusion models,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:20.431070Z digest=sha256:f1602f34c0d4f62d141cad30d882bd5c048adbdfb2fb393b9f977a3282c99747

Observation 18e1ee50-b53f-440d-b84a-a957b3b331bc · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation LoRA: Low-Rank Adaptation of Large Language Models

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:20.545296Z digest=sha256:c021b6f45e8e62b17cbe3c9ad89af82342daf3a0b907448160f3765db7d95784

Observation 774980ae-fc46-4fd2-b56d-47a5f93d6bf0 · outbound

This paper cites Denoising Diffusion Implicit Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Denoising Diffusion Implicit Models

Reference 35

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source=pdf_text observed=2026-08-06T19:57:20.627108Z digest=sha256:52219d7838c1f89f3d1ed3106cb1c425dbc45f9157906bfa27c8d07efa593bc1

Observation e9dda51f-eafa-4dc5-92f5-41f936cfb8e9 · outbound

This paper cites Scalable diffusion models with transformers,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Scalable diffusion models with transformers,

Reference 36

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raw_fallback, observed 2026-08-06T19:57:27.174943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:20.679398Z digest=sha256:53aa589fbdec1213becf8acb941494d3a5c4774c5366c39b673653c344e340b8

Observation 1fcc8d90-5c6c-4769-8525-4784ce4b7edb · outbound

This paper cites Multi-party collaborative attention control for image customization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Multi-party collaborative attention control for image customization,

Reference 37

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raw_fallback, observed 2026-08-06T19:57:27.015735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:20.766075Z digest=sha256:f2f7a7dd7ae3d01c652a3dfffb39843226abee255353f419b913e6ba8d0cf09a

Observation ed309121-fc1a-4670-a1d7-c0401e8f8c8c · outbound

This paper cites One-step effective diffusion network for real-world image super-resolution,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation One-step effective diffusion network for real-world image super-resolution,

Reference 38

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raw_fallback, observed 2026-08-06T19:57:26.853660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:20.829421Z digest=sha256:13575f006f2de45c3e7313391a12d3a0e34acfe53aa836f678f4cf112dfee8f7

Observation 8510113a-804a-46c5-a01f-26015fc989d2 · outbound

This paper cites Pseudo Numerical Methods for Diffusion Models on Manifolds.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Pseudo Numerical Methods for Diffusion Models on Manifolds

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:20.906166Z digest=sha256:70b3e2127f410b02dc9b466ff62c03d1f08303f31ac151269e7cf9feb36503c8

Observation 85a251a3-eefb-4b11-8a97-fa3396604afd · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,

Reference 40

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no resolver link, observed 2026-08-06T19:57:21.016029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:21.016029Z digest=sha256:f919c948397405fa92b47f01bbb5692c00c660774facef2e2ead9476f7230c1a

Observation 1c3d29c2-c3fa-463f-aea2-c8d1f18e72c5 · outbound

This paper cites DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

Reference 41

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no resolver link, observed 2026-08-06T19:57:21.114781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:21.114781Z digest=sha256:cbb4abb0cd3141f3b079f8bce23e5893c66010cb8b0b7e4b4cb046551da4e9e5

Observation 7db33e1f-6c59-4c27-a3a9-71989d2e30f6 · outbound

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

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Progressive Distillation for Fast Sampling of Diffusion Models

Reference 42

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:21.170338Z digest=sha256:3a497453ccae92d03ebfb07f51fad3d263db6d17656f6a759e29577a70a50d1b

Observation 8206ba11-1fc2-4311-9097-60b837d7f615 · outbound

This paper cites Consistency models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Consistency models,

Reference 43

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:21.220931Z digest=sha256:a6d4c02be7d5ab90215c885cd13b7a32eeda20b02cfa169593a658c9ab591c4f

Observation 54ee26ed-d220-47df-8e07-3c03732b2168 · outbound

This paper cites Relational diffusion distillation for efficient image generation,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Relational diffusion distillation for efficient image generation,

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-06T19:57:26.732683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:21.319583Z digest=sha256:264e681d18a84a58d7bd93fb1ef317b9f78bcfe017ddddb35952f3a87c8016ba

Observation 86e26931-433c-4b03-9323-d7a8c20173eb · outbound

This paper cites Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers

Reference 45

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:21.407355Z digest=sha256:26fdfcb18e259bc4b73740267ac9d8cbae544d2dd0a2e7caf0d789cec7e3067b

Observation eae78f77-1dca-454f-9ef1-1594d0536305 · outbound

This paper cites Diverse sample generation: Pushing the limit of generative data-free quantization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Diverse sample generation: Pushing the limit of generative data-free quantization,

Reference 46

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raw_fallback, observed 2026-08-06T19:57:26.543486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:21.477236Z digest=sha256:cc4a17da21d1066e71d71a08ccb8385d6b701992c722c83e3ad1951bf8b7ef2a

Observation 38268eb8-0a98-4881-801b-9a42e45ea8eb · outbound

This paper cites Pushing the limit of post-training quantization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Pushing the limit of post-training quantization,

Reference 47

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raw_fallback, observed 2026-08-06T19:57:26.415974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:21.583736Z digest=sha256:50cb27c0bad9e919c499631871ce23502384fc77d51694bee01b816a0086ddd8

Observation d9900592-98d8-4397-b5f0-ce1d28bf5225 · outbound

This paper cites Hawq: Hessian aware quantization of neural networks with mixed-precision,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Hawq: Hessian aware quantization of neural networks with mixed-precision,

Reference 48

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raw_fallback, observed 2026-08-06T19:57:26.249743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:21.688349Z digest=sha256:ab02348de56f401eac147c61b13ca4536c6f23da837074c4e4d5e901a872d892

Observation e55b3709-a2d3-478f-8f89-f166c643bb61 · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Smoothquant: Accurate and efficient post-training quantization for large language models,

Reference 49

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:21.802585Z digest=sha256:f7aac1c4230302933d478dc5477b8a0e077c107854c13aa38923d8bc51d98b9d

Observation ec899cc6-e505-4c9d-b718-9fea72ed24f2 · outbound

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

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Q-diffusion: Quantizing diffusion models,

Reference 50

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raw_fallback, observed 2026-08-06T19:57:26.073085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:21.908495Z digest=sha256:5f956f56457bfc0202e70ce58bbfb213cb3e866ae65eb4899008bcf9c71791f9

Observation ba3b2624-6072-4784-96a7-dde935958891 · outbound

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

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Post-training quantiza- tion on diffusion models,

Reference 51

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raw_fallback, observed 2026-08-06T19:57:25.938649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:22.010605Z digest=sha256:181727ee0e67bbbca5c175d056e1b04ae55cfd235c90b47ed82bcc8312b97a14

Observation c1888dcb-33d5-47b4-9189-714b1a2fb9d1 · outbound

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

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Ptqd: Accurate post-training quantization for diffusion models,

Reference 52

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raw_fallback, observed 2026-08-06T19:57:25.785627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:22.089373Z digest=sha256:39cb923752dffa9e3f0c9b10427f0ec03115262c78dc4c911ba3859d68d8c711

Observation cebcff45-38ae-4933-aa65-4419e6e84f7c · outbound

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

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Tfmq-dm: Temporal feature maintenance quantization for diffusion models,

Reference 53

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verified fuzzy
raw_fallback, observed 2026-08-06T19:57:25.570670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:22.195637Z digest=sha256:d5e1ba9db9d304307e7aa067acce0207686fd3c19b41c612e8f7981bc34f9d0f

Observation 440aba8f-a336-4299-9e8b-9e95bbd086ff · outbound

This paper cites Towards accu- rate post-training quantization for diffusion models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Towards accu- rate post-training quantization for diffusion models,

Reference 54

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raw_fallback, observed 2026-08-06T19:57:25.356543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:22.287556Z digest=sha256:3e0ba700956a8d6ae48aab22fa6704685b643cda465e8c89abcc80d0a914bef9

Observation 00657131-b141-4fbc-914a-05c4c4633a78 · outbound

This paper cites PTQ4DiT: Post-training Quantization for Diffusion Transformers.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation PTQ4DiT: Post-training Quantization for Diffusion Transformers

Reference 55

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:22.345861Z digest=sha256:2bec07e687a57b797241b4813bc1aa8084f41f7eae53fd040e572ce6556a44af

Observation 56536415-1f04-4325-b510-0458de825246 · outbound

This paper cites QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning

Reference 56

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:22.457468Z digest=sha256:24879e016a5ee1d101c65279f0490c51947a6456a02a8b1193f2e7e46204ccc5

Observation e96810b9-489a-4852-95a0-b5d3ec9ebcbf · outbound

This paper cites Ompq: Orthogonal mixed precision quantization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Ompq: Orthogonal mixed precision quantization,

Reference 57

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raw_fallback, observed 2026-08-06T19:57:25.202272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:22.537831Z digest=sha256:7e6573777e6e6294b5e19a684024a717e447b7ffaf9942b2196c852c3113cd61

Observation 9722269a-dc1a-4947-8f8a-7be18db64b33 · outbound

This paper cites dabnn: A super fast inference framework for binary neural networks on arm devices,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation dabnn: A super fast inference framework for binary neural networks on arm devices,

Reference 58

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raw_fallback, observed 2026-08-06T19:57:25.025696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:22.615311Z digest=sha256:703176e780778dcaddeed90305290d2e5b92c3a974008552a5c99c880915795b

Observation b9935fa1-1505-4895-8dfb-7029b00b1d86 · outbound

This paper cites Online knowledge distillation via mutual contrastive learning for visual recogni- tion,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Online knowledge distillation via mutual contrastive learning for visual recogni- tion,

Reference 59

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:22.699065Z digest=sha256:f0412616ccf9b551ebaf24618e1ed0b0900b330783c457f01f65b0aace7e111c

Observation c20b336f-27a3-4c83-aa91-95ae95dda740 · outbound

This paper cites Self-supervised visual feature learning with deep neural networks: A survey,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Self-supervised visual feature learning with deep neural networks: A survey,

Reference 60

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raw_fallback, observed 2026-08-06T19:57:24.863227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:22.772412Z digest=sha256:633c00451c75b15462399560239752d7a688cb89220ed5a53295173028884480

Observation 47fff58d-d3ff-4b8c-ac1a-1cc33bcaaade · outbound

This paper cites A generalization of the eckart-young-mirsky matrix approximation theorem,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation A generalization of the eckart-young-mirsky matrix approximation theorem,

Reference 61

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raw_fallback, observed 2026-08-06T19:57:24.689939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:22.876331Z digest=sha256:fbf7395b1bf57fa67fea6eadb6bccd23b37da7a761f6f67f2e5c918897f386b0

Observation 7903f5e3-6f8b-4b4a-8e50-c7926ace7a8a · outbound

This paper cites Hawq-v3: Dyadic neural net- work quantization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Hawq-v3: Dyadic neural net- work quantization,

Reference 62

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raw_fallback, observed 2026-08-06T19:57:24.552525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T19:57:22.956923Z digest=sha256:27cce28eb5016d0a12960fe848aee695043d179d6b2975920b176acbfe3bc555

Observation b020707e-8694-4dc9-b7b3-2c5ec2182059 · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 63

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:23.027311Z digest=sha256:caa282f2c764d4195fd907906f1e863fef32cad6f27fe7d7afe851d55e9d1657

Observation c1b544f2-9055-4e54-b68f-5c9cc3f9dc05 · outbound

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

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Imagenet: A large-scale hierarchical image database,

Reference 64

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:23.121768Z digest=sha256:a39d1bdeb65fc71beff0eaa24f076d1bbafacf6222f44d384a9685edb79af3a3

Observation e836c862-bcf7-4ba1-a6c4-677017c45b6a · outbound

This paper cites Microsoft coco: Common objects in context,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Microsoft coco: Common objects in context,

Reference 65

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no resolver link, observed 2026-08-06T19:57:23.193526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:23.193526Z digest=sha256:223b994d13864c8cb054582d64671d6fd0da7e62bd99dbe69b96e9198b822318

Observation 3c4bde06-95ab-4039-8a5b-c7e640449cb4 · outbound

This paper cites Improved techniques for training gans,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Improved techniques for training gans,

Reference 66

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:23.293982Z digest=sha256:c364614ebc25a4a91a897ebc10316398792345c9a71ce5b4dd3194f7a50aa620

Observation db416036-5d8c-48dc-929a-7813f8f02415 · outbound

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

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 67

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:57:23.396871Z digest=sha256:5279219362ba91fd0a433c1a5823286684e9dacbfa779c9dac566337984a29c0

Observation 0e01c73a-7991-4632-beb1-d78244652435 · outbound

This paper cites Generating Images with Sparse Representations.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Generating Images with Sparse Representations

Reference 68

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Observation ca152dbc-485e-4baf-985b-42bddba045af · outbound

This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 69

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Observation 9babebd8-f55a-46e4-a5fc-46935ec5c5de · outbound

This paper cites Cross- image relational knowledge distillation for semantic segmentation,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Cross- image relational knowledge distillation for semantic segmentation,

Reference 70

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Pith citing papers

Observation e0782ca2-f77f-441b-969e-baa0267b9f71 · inbound

RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization cites this paper.

RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation

Reference 16

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