Paper Citation Record · LEDGER

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models

As of 11 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2501.04304.

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

pith.paper-citation-record.v1
2501.04304 v2

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:42:07.148684Z

measured 28 of 28 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 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

28 of 28 outbound references displayed

verified exact1
verified fuzzy8
unresolved19
parse uncertain0
malformed identifier0
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External citation measurements

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Outbound references

Observation de8badda-8ef8-4218-9f78-7ce7f126f326 · outbound

This paper cites Understanding and Overcoming the Challenges of Efficient Transformer Quantization.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.011927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7317a66b-b65e-489e-af36-f4bf0727c46c · outbound

This paper cites As shown in Figure 5(b), the maximum values of cross-attention scores vary more dynamically than those of self-attention.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models As shown in Figure 5(b), the maximum values of cross-attention scores vary more dynamically than those of self-attention

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:42:07.489404Z

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.

Observation 46b437a5-0ca1-42ac-9e12-facaf4313f26 · outbound

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

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.041279Z

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Observation cc8b231f-7d14-4243-90e5-c04cda874711 · outbound

This paper cites Gligen: Open-set grounded text-to-image generation.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Gligen: Open-set grounded text-to-image generation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:42:07.602210Z

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Observation 419c91ee-3b85-4451-ae3c-877e1eb47065 · outbound

This paper cites Microsoft coco: Common objects in context.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Microsoft coco: Common objects in context

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:42:07.586393Z

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.

Observation 405535ae-e490-463c-a149-8b5c2ff5bd35 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.066569Z

Source-reported events for the cited work

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Observation ba2172ef-105b-4a68-b0b4-e08dedb03399 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Deep unsupervised learning using nonequilibrium thermodynamics

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.076226Z

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Observation d886baea-5a72-4d4f-8f9a-94720fcdf84b · outbound

This paper cites Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxing.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxing

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:42:07.303370Z

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.

Observation 2e9b8de6-4671-458e-b847-8f36cdb960f1 · outbound

This paper cites Efficient Diffusion Models for Vision: A Survey.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Efficient Diffusion Models for Vision: A Survey

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.091631Z

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Observation 1866a867-3455-4c1f-aec0-a3f60dbceff3 · outbound

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

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.101546Z

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Observation 6e3fefee-ef75-4a8a-aefa-a8f2b5e92e24 · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.106704Z

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Observation 2a57c016-2f90-4eb0-a481-e0c7cf9075f4 · outbound

This paper cites MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.111364Z

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Observation 9c3aac91-eaa5-4e88-b75c-40c56ed6a6a1 · outbound

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

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.116262Z

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Observation df5e3aaf-90c4-4d66-8e13-21477903f164 · outbound

This paper cites A Survey on Model Compression for Large Language Models.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models A Survey on Model Compression for Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.120928Z

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Observation fa673ac2-1c0a-45e2-8f0e-87a815c22cb9 · outbound

This paper cites We analyze the effects of the attention score corresponding to <start> token.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models We analyze the effects of the attention score corresponding to <start> token

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:42:07.551388Z

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

Observation 90a68a02-ce4f-4a33-ad56-f7255f218554 · outbound

This paper cites A cat riding a bike.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models A cat riding a bike

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:42:07.535034Z

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.

Observation 8e89184f-20d0-48d3-ba1d-27312deefa02 · outbound

This paper cites The evaluation is conducted on 30K samples from the MS-COCO dataset.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models The evaluation is conducted on 30K samples from the MS-COCO dataset

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:42:07.520208Z

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.

Observation ce560b7c-f32e-41dd-ba08-5ead91af4d14 · outbound

This paper cites A photo of a cat and a dog.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models A photo of a cat and a dog

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:42:07.504761Z

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.

Observation 78c2584a-4391-463c-869d-8198b3ff27e9 · outbound

This paper cites Figure A.4 shows the visualization of full activation matrix.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Figure A.4 shows the visualization of full activation matrix

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:42:07.473619Z

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.

Observation 3054a986-b066-4e6f-b44b-55a5663dd7ed · outbound

This paper cites BitsFusion: 1.99 bits Weight Quantization of Diffusion Model.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models BitsFusion: 1.99 bits Weight Quantization of Diffusion Model

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.081008Z

Source-reported events for the cited work

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Observation 5aaa8e41-2e36-4c83-b1d7-21958a102a15 · outbound

This paper cites Post-training quantization on diffusion models.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Post-training quantization on diffusion models

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.071442Z

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Observation bff14419-70b3-47e5-837d-04965a8a50eb · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.046746Z

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Observation 0be6da07-af03-4492-bb60-cb0226d923cb · outbound

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

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.027685Z

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Observation a380c7b2-fb3b-4b9f-bf58-d4fe55be8ad7 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.061130Z

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Observation 6259b764-ee52-420f-8178-2245f79f8b3d · outbound

This paper cites Vision Transformers Need Registers.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Vision Transformers Need Registers

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.017645Z

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Observation e8b244ed-95bd-49fb-843a-a66fe4c7c2a7 · outbound

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

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.096741Z

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Observation e6100d58-2126-4a85-a4af-8fc7d1759f44 · outbound

This paper cites BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.037070Z

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Observation f930ae56-96c9-4bba-8eef-16970f6f73cd · outbound

This paper cites Learned Step Size Quantization.

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models Learned Step Size Quantization

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:07.022728Z

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Pith citing papers

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