Paper Citation Record · LEDGER

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning

As of 15 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2505.21591.

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

pith.paper-citation-record.v1
2505.21591 v1

Coverage vector

measured 46 of 46 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-07T13:45:45.917406Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6f93ce61-8844-4d86-a4ff-2c66e887704f · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 1

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Observation abd6ec1c-820d-4475-bed0-d95d5b1654cb · outbound

This paper cites Low-Bitwidth Floating Point Quantization for Efficient High-Quality Diffusion Models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Low-Bitwidth Floating Point Quantization for Efficient High-Quality Diffusion Models

Reference 2

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Observation 6eb53b47-b592-481b-873e-5d3d25383302 · outbound

This paper cites Qncd: Quantization noise correction for diffusion models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Qncd: Quantization noise correction for diffusion models

Reference 3

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Observation b6e2753e-e51f-44dc-8e21-ec5243b8c0e8 · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Imagenet: A large-scale hierarchical image database

Reference 4

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Observation 58c39e00-df00-4eb7-80a9-44b12f6e5c4c · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning QLoRA: Efficient Finetuning of Quantized LLMs

Reference 5

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Observation f83895c6-3d03-4311-a55d-cc190193ba27 · outbound

This paper cites Learned step size quantization.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Learned step size quantization

Reference 6

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Observation 906c7a53-24c7-45f9-bfb5-947b6de6c62f · outbound

This paper cites Mixture-of-loras: An efficient multitask tun- ing method for large language models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Mixture-of-loras: An efficient multitask tun- ing method for large language models

Reference 7

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Observation 7a9088ae-67f6-4441-ab1a-0f7f6c80430e · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models

Reference 8

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Observation 8efcae50-56d1-467e-87bf-fb15a184767a · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Ptqd: Accurate post-training quantization for diffusion models

Reference 9

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Observation 7809432a-485e-40ca-a6c8-20f407850f8f · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 10

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Observation 38315edd-342a-4a22-aaf7-565764645859 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Denoising dif- fusion probabilistic models

Reference 11

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Observation 6115ffc0-7785-455e-b382-8917fe2954ab · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning LoRA: Low-Rank Adaptation of Large Language Models

Reference 12

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Observation 8660d64c-a6c3-4ad1-b896-0a16cb61ac02 · outbound

This paper cites An empirical study of llama3 quantization: From llms to mllms.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning An empirical study of llama3 quantization: From llms to mllms

Reference 13

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Observation 0e4802fd-af62-4a47-add4-235b5ab0758f · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 14

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Observation f93e198e-4668-45bc-9b8d-e4077c6ff20c · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 15

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Observation dc81b4b7-da52-48ad-9f03-476fa0cfa81c · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Learning multiple layers of features from tiny images

Reference 16

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Observation 073ed858-c9ec-422f-889f-9e0245090852 · outbound

This paper cites Fp8 quanti- zation: The power of the exponent.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Fp8 quanti- zation: The power of the exponent

Reference 17

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Observation 0b40b56f-a1cb-457b-a895-9637b4d0ee42 · outbound

This paper cites Contemporary ad- vances in neural network quantization: A survey.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Contemporary ad- vances in neural network quantization: A survey

Reference 18

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Observation 22e27283-26c0-4600-9310-8252d8576c49 · outbound

This paper cites Q-diffusion: Quantizing diffusion models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Q-diffusion: Quantizing diffusion models

Reference 19

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Observation 50415437-cc34-4be8-882f-1872fbd244cf · outbound

This paper cites Q-dm: An efficient low-bit quantized dif- fusion model.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Q-dm: An efficient low-bit quantized dif- fusion model

Reference 20

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Observation c077dcff-522a-4910-80e2-6d044885c616 · outbound

This paper cites Pruning and quantization for deep neural network acceleration: A survey.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Pruning and quantization for deep neural network acceleration: A survey

Reference 21

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Observation e917de86-2536-4f59-9143-7fffe2520c02 · outbound

This paper cites Microsoft coco: Common objects in context.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Microsoft coco: Common objects in context

Reference 22

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Observation d40cb4a9-cf58-4a83-8188-605e4bad4589 · outbound

This paper cites Improving neural network efficiency via post-training quan- tization with adaptive floating-point.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Improving neural network efficiency via post-training quan- tization with adaptive floating-point

Reference 23

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Observation 7b790076-e84d-4182-9018-14a9e938279f · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Pseudo Numerical Methods for Diffusion Models on Manifolds

Reference 24

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Observation 2e530c43-7315-46f0-a0eb-0ae3f8e057a2 · outbound

This paper cites LLM-FP4: 4-Bit Floating-Point Quantized Transformers.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Reference 25

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Observation 5f0c8e01-a53d-40a3-93f8-1e1a6bf099c6 · outbound

This paper cites DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation

Reference 26

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Observation 08defcd2-ad34-4e7b-b271-99570aa61761 · outbound

This paper cites EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models

Reference 27

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Observation 98373f25-4fdb-42ef-982a-74f7f76d113e · outbound

This paper cites Deep learning face attributes in the wild.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Deep learning face attributes in the wild

Reference 28

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Observation 5598c3e5-9367-40e9-9e18-5d55ce2602b4 · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Reference 29

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Observation 89a617d2-de96-42d7-95cd-84dfe4618626 · outbound

This paper cites FP8 Formats for Deep Learning.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning FP8 Formats for Deep Learning

Reference 30

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Observation 4a2f7bbf-2b2c-4577-bec0-4cd03d496398 · outbound

This paper cites Up or down? adap- tive rounding for post-training quantization.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Up or down? adap- tive rounding for post-training quantization

Reference 31

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Observation 9a4eb73b-f4f5-4cce-8218-31aa538ea4e5 · outbound

This paper cites Blackwell platform sets new llm inference records in mlperf inference v4.1, 2024.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Blackwell platform sets new llm inference records in mlperf inference v4.1, 2024

Reference 32

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Observation d8a4ad47-0e1a-41f7-9d9d-96e8afdb5529 · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning High-resolution image synthesis with latent diffusion models

Reference 33

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Observation 9041568c-faf3-472e-837a-b180eafc7d81 · outbound

This paper cites Improved techniques for training gans.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Improved techniques for training gans

Reference 34

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Observation 2cb5dd0e-8c85-4d55-8014-0bc00fc92612 · outbound

This paper cites Post-training quantization on diffusion models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Post-training quantization on diffusion models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:47.661402Z

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Observation 44d3f7f7-e769-4b4b-bf8f-d297afb8ff0d · outbound

This paper cites Temporal dynamic quantization for dif- fusion models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Temporal dynamic quantization for dif- fusion models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:47.475291Z

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Observation 17dc49a3-e5c7-41e1-9848-a43d5be4d749 · outbound

This paper cites Denoising Diffusion Implicit Models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Denoising Diffusion Implicit Models

Reference 37

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Observation f4bbe5a2-8139-4b8a-90f4-76942fd62b64 · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:45:46.234877Z

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Observation e5530f14-8c65-472e-b013-bfafdf5d73b6 · outbound

This paper cites FP8 versus INT8 for efficient deep learning inference.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning FP8 versus INT8 for efficient deep learning inference

Reference 39

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Observation e42cd56e-114d-4645-88fe-a4b0dcc34f40 · outbound

This paper cites Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later

Reference 40

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Observation aac9255e-eaa7-42b1-a752-7f44eac9991b · outbound

This paper cites Towards Accurate Post-training Quantization for Diffusion Models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Towards Accurate Post-training Quantization for Diffusion Models

Reference 41

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Observation af1f11a9-b088-4d26-ac4c-4aee88a864e3 · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning

Reference 42

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Observation ec952f8b-3394-455c-b16d-1cb3227d9bc8 · outbound

This paper cites Fp4-quantization: Lossless 4bit quantization for large lan- guage models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Fp4-quantization: Lossless 4bit quantization for large lan- guage models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:45:47.318492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8bcc5734-8f52-42df-bf25-6076174cb8a3 · outbound

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

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 44

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Source-reported events for the cited work

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Observation d9c634a9-a520-439b-8047-72b50e9ff91f · outbound

This paper cites Integer or floating point? new outlooks for low-bit quantization on large language models.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning Integer or floating point? new outlooks for low-bit quantization on large language models

Reference 45

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Observation 8185a844-fda6-4497-8e90-9d5ae0455569 · outbound

This paper cites The input channels of the router match the channel count of the timestep embedding in the diffusion model.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning The input channels of the router match the channel count of the timestep embedding in the diffusion model

Reference 46

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T13:45:47.022404Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

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