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Paper Citation Record · LEDGER

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

As of 17 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation 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 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:13:36.253421Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T23:13:37.639536Z

Reference resolution

28 of 28 outbound references displayed

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  • verified fuzzy8
  • unresolved19
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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

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source=pdf_text observed=2026-08-10T21:42:07.011927Z digest=sha256:c5e1571680af04b2795e2e990784c75d6a9a7748dd1f8e145ba2edef03951b43

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

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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

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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

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source=pdf_text observed=2026-08-10T21:42:07.051689Z digest=sha256:a0aa606399d3712e6d8c8a38c6e4a73aaf81754fcc33687bb70f5ed3d936941a

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

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source=pdf_text observed=2026-08-10T21:42:07.056403Z digest=sha256:89f9d2391365e054a82949e0a36d50afadb25c3a0d297d4101aeb1a0095ff735

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

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source=pdf_text observed=2026-08-10T21:42:07.066569Z digest=sha256:375f673fb326bc83c43f46b3ba25499fc6bd12e11a95f1629068b05eeb537ef9

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

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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

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source=pdf_text observed=2026-08-10T21:42:07.086285Z digest=sha256:df8bda7d3f1f3d5ffd1b91f922bbeb8b24917ab2f97cc5c31a751e12c5d2dc2a

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

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source=pdf_text observed=2026-08-10T21:42:07.091631Z digest=sha256:5fa133f82b2fe0412031466f38465c71abcc59b32d95e3ccac4f6312ebd1b23c

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

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source=pdf_text observed=2026-08-10T21:42:07.101546Z digest=sha256:ff197942a882f15139b7ddec5ffc5f064145bd407125186acb0030c67461efbd

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

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source=pdf_text observed=2026-08-10T21:42:07.106704Z digest=sha256:78f5f84690f0fea48ad97fe6146ddc5bde6210f64a58b748f47fe581dc3032c5

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

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source=pdf_text observed=2026-08-10T21:42:07.111364Z digest=sha256:4505e415a3964b501e43f6e6118a6de4d11e3c5568b27be65e2e897da44130d7

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

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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

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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

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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

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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

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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

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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

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source=pdf_text observed=2026-08-10T21:42:07.081008Z digest=sha256:0c94788b8c17cf77c567c98dc3e8de0b904102cf405461a59f2ed7ba25e832c6

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

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source=pdf_text observed=2026-08-10T21:42:07.071442Z digest=sha256:43aa685afa012383b440c61e3977e540540bcaea58f91a27d261cf2abaeafb9d

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

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source=pdf_text observed=2026-08-10T21:42:07.046746Z digest=sha256:4fba8bff96c2516578e0e72e9d87c0042e49aaf35d0aae63a10b930936fbffa5

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

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source=pdf_text observed=2026-08-10T21:42:07.027685Z digest=sha256:6471ffaeaaf1e0c24c604ae8e03f04c47d7ebdd455d314a2b7d033caecfb12ba

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

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source=pdf_text observed=2026-08-10T21:42:07.061130Z digest=sha256:b59dd36b4c2a0eb05f36a5829ef37a1e4d51d388f0da5fbdd3189d97f2ab6633

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

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source=pdf_text observed=2026-08-10T21:42:07.017645Z digest=sha256:9edbb4cda2007ec28ec400d1cbba6de1eba8fa6211a8fcb5a7ce10aa96dad2ea

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

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source=pdf_text observed=2026-08-10T21:42:07.096741Z digest=sha256:b86ea6cc0ca7616591b19446b840d0c15b357ae51d2184cbcf555d4a76b4523b

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

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source=pdf_text observed=2026-08-10T21:42:07.037070Z digest=sha256:4b1993c3b9ecd422810b678c7af795127021d238fd2866b123532bd5ca1cec5f

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

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source=pdf_text observed=2026-08-10T21:42:07.022728Z digest=sha256:b39808c3e3c2e00e5990a5cde28388bdab79044e471f278edd6173d8b76d8d7e

Pith citing papers

Observation 4f1bc6af-33df-44a0-b209-92a4b1734c25 · inbound

Diffusion Model Quantization: A Review cites this paper.

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

Reference 110

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T23:13:36.253421Z digest=sha256:9e6addf1af2e52480b668a3d8ffa9568f4540d5acbeef6fbd6d65e9736981918