DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization | Paper Citation Record

Observation 9016cda8-3f78-447c-b9ff-7c365c2de513 · outbound

This paper cites Hardware approximate techniques for deep neural network accelerators: A survey.ACM Computing Sur- veys, 55(4):1–36, 2022.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Hardware approximate techniques for deep neural network accelerators: A survey.ACM Computing Sur- veys, 55(4):1–36, 2022

Reference 1

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Observation cc08d352-0c7a-43da-8b20-fe2e7e710525 · outbound

This paper cites Improving image generation with better captions.Computer Science.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Improving image generation with better captions.Computer Science

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Observation be9e06a9-dc1b-4e02-8d4b-22d51ddce2b6 · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Align your latents: High-resolution video synthesis with la- tent diffusion models

Reference 3

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This paper cites High per- formance convolutional neural networks for document pro- cessing.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization High per- formance convolutional neural networks for document pro- cessing

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This paper cites PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Reference 5

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Observation b3b404d0-65b0-4c0f-be6b-6b41734a41b8 · outbound

This paper cites cuDNN: Efficient Primitives for Deep Learning.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization cuDNN: Efficient Primitives for Deep Learning

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Observation 30160f52-5e82-450f-b2db-fd5ab2642d04 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization PACT: Parameterized Clipping Activation for Quantized Neural Networks

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Observation 0199672a-0a47-45e7-9380-fc2c47b716b6 · outbound

This paper cites Consistent diffusion models: Mitigating sampling drift by learning to be consistent.Advances in Neu- ral Information Processing Systems, 36, 2024.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Consistent diffusion models: Mitigating sampling drift by learning to be consistent.Advances in Neu- ral Information Processing Systems, 36, 2024

Reference 8

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Observation 6ad6ea38-15e5-42d9-b900-062e9d76a63d · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Imagenet: A large-scale hierarchical image database

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Unresolved cited work

Reference 10

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Observation bb5af1e5-1290-4ac5-9a12-1b501543b768 · outbound

This paper cites Deepshift: Towards multiplication- less neural networks.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Deepshift: Towards multiplication- less neural networks

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Observation cbfc8fdb-1a46-43e1-b2ad-11d343256647 · outbound

This paper cites Learned Step Size Quantization.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Learned Step Size Quantization

Reference 12

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This paper cites Erasing concepts from diffusion models.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Erasing concepts from diffusion models

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Observation 20c63b92-3e48-4f66-8704-378360ac6992 · outbound

This paper cites A survey of quan- tization methods for efficient neural network inference.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization A survey of quan- tization methods for efficient neural network inference

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Observation 60d9919c-5e58-490f-9357-359ea1b546d4 · outbound

This paper cites Efficientdm: Efficient quantization-aware fine- tuning of low-bit diffusion models.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Efficientdm: Efficient quantization-aware fine- tuning of low-bit diffusion models

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This paper cites Ptqd: Accurate post-training quantization for diffusion models.Advances in Neural Information Pro- cessing Systems, 36, 2024.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Ptqd: Accurate post-training quantization for diffusion models.Advances in Neural Information Pro- cessing Systems, 36, 2024

Reference 16

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Observation 1946c089-9add-4272-9b67-08431f5aea57 · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 17

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Observation 531c8028-da67-4241-88d0-44e106b8902d · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017

Reference 18

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Observation 2f248ac2-78c6-4e64-afb3-9177e09ea865 · outbound

This paper cites Classifier-Free Diffusion Guidance.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Classifier-Free Diffusion Guidance

Reference 19

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Observation 8d2cc892-48a4-434a-b924-cdae8d4c741c · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 20

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Observation 093bd038-c6a7-4058-90ed-365647032236 · outbound

This paper cites Video dif- fusion models.Advances in Neural Information Processing Systems, 35:8633–8646, 2022.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Video dif- fusion models.Advances in Neural Information Processing Systems, 35:8633–8646, 2022

Reference 21

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Observation 18753302-e31a-415f-9a50-314934ab08a7 · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Tfmq-dm: Temporal feature maintenance quantization for diffusion models

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Observation 5b959d09-c90b-4d92-9600-f631bf6c2b27 · outbound

This paper cites Expanding expressiveness of diffusion models with limited data via self-distillation based fine-tuning.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Expanding expressiveness of diffusion models with limited data via self-distillation based fine-tuning

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Observation 99f9cf72-e359-4f95-bb98-ae00857729d3 · outbound

This paper cites Scalable Adaptive Computation for Iterative Generation.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Scalable Adaptive Computation for Iterative Generation

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Observation 80f91b17-7ba3-46cb-a2e4-40d39ba6f19a · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Quantization and training of neural networks for efficient integer-arithmetic-only inference

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Observation 1769fe49-889a-4832-8701-2c55dc2767b3 · outbound

This paper cites Learning to quantize deep networks by op- timizing quantization intervals with task loss.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Learning to quantize deep networks by op- timizing quantization intervals with task loss

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Observation f0a9ad09-f203-4194-b2e7-a42ca5823f26 · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization A style-based generator architecture for generative adversarial networks

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Observation 8ab990bd-5af3-4f2c-84de-8e2c97b704a1 · outbound

This paper cites Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation

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This paper cites Quantization for Rapid Deployment of Deep Neural Networks.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Quantization for Rapid Deployment of Deep Neural Networks

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Observation 362d79f0-c276-4ec4-bf84-18629b426456 · outbound

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Srdiff: Single image super-resolution with diffusion probabilistic models

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Observation 01be1a3f-93d2-428d-ab52-b37fdfceaa10 · outbound

This paper cites Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time Steps.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time Steps

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Observation 60b4165e-fc11-42fe-912f-02c176364218 · outbound

This paper cites Q-diffusion: Quantizing diffusion models.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Q-diffusion: Quantizing diffusion models

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Observation b381eb5e-978d-482f-9f30-f6ce54cb0610 · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

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Observation 974ef2c3-502f-4c30-9c4f-1cae1fdd9463 · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Q-dm: An efficient low-bit quantized dif- fusion model

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Observation a486a81c-cf45-4b40-a7dd-01627a08cb34 · outbound

This paper cites Magic3d: High-resolution text-to-3d content creation.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Magic3d: High-resolution text-to-3d content creation

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Observation bca7466f-e083-44d3-8f7a-6b751da152ca · outbound

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Awq: Activation-aware weight quantization for on-device llm compression and acceleration

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Microsoft coco: Common objects in context

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Focal loss for dense object detection

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models

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Observation 3df6ec69-0a86-4096-a8e1-65c56f0a2df8 · outbound

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Repaint: Inpainting using denoising diffusion probabilistic models

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Observation a57d6a7f-8e7c-4666-90f3-f9875627a2c7 · outbound

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Data-free quantization through weight equal- ization and bias correction

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Observation 4a967722-68c9-44ef-81b4-bc5c40af0d46 · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Up or down? adap- tive rounding for post-training quantization

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This paper cites A White Paper on Neural Network Quantization.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization A White Paper on Neural Network Quantization

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Generating Images with Sparse Representations

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Improved denoising diffusion probabilistic models

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Elucidating the Exposure Bias in Diffusion Models

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Scalable diffusion models with transformers

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This paper cites Barron, and Ben Milden- hall.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Barron, and Ben Milden- hall

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Searching for Activation Functions

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization High-resolution image synthesis with latent diffusion models

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Observation f9e056e3-8af2-4c9e-8cc3-6334f3fa624f · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

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Observation 716d9fdb-b52f-48a4-9c36-3ed6880e3c35 · outbound

This paper cites Improved techniques for training gans.Advances in neural information processing systems, 29, 2016.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Improved techniques for training gans.Advances in neural information processing systems, 29, 2016

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Observation 64d676de-0ed4-4f23-81f1-5d886abe4d8c · outbound

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DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Post-training quantization on diffusion models

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Observation c44c7d07-a5d0-4439-bde7-4f4353019627 · outbound

This paper cites Dragdiffusion: Harnessing diffusion models for interactive point-based image editing.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Dragdiffusion: Harnessing diffusion models for interactive point-based image editing

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.150998Z

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Observation b78c3eba-849a-4335-97cf-1863396ee765 · outbound

This paper cites Make-A-Video: Text-to-Video Generation without Text-Video Data.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.155639Z

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Observation 4a39ca0f-1c8a-4e40-a7e0-debf49bc4cab · outbound

This paper cites Temporal dynamic quantization for dif- fusion models.Advances in Neural Information Processing Systems, 36, 2024.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Temporal dynamic quantization for dif- fusion models.Advances in Neural Information Processing Systems, 36, 2024

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.951922Z

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Observation 8613d21c-49aa-4dca-8d76-dc8ccda1187e · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Deep unsupervised learning using nonequilibrium thermodynamics

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.936658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5c4e2856-d791-45b4-921f-c7670eb5ead0 · outbound

This paper cites Denoising Diffusion Implicit Models.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Denoising Diffusion Implicit Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.170473Z

Source-reported events for the cited work

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Observation e6b0130e-8d22-4ab1-9034-18c87798f186 · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization BitsFusion: 1.99 bits Weight Quantization of Diffusion Model

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:42:12.437005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fc8ea66b-aa11-4141-b359-615f33316f7f · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 2017.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Attention is all you need.Advances in Neural Information Processing Systems, 2017

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.180842Z

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Observation 44b9d64d-44c0-4f01-a43a-5d4678ff2016 · outbound

This paper cites Towards accurate post-training quantization for diffusion models.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Towards accurate post-training quantization for diffusion models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.910461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b4791315-bc56-4413-9ce6-5322b3c238c9 · outbound

This paper cites Outlier suppression: Pushing the limit of low-bit transformer language models.Advances in Neural Informa- tion Processing Systems, 35:17402–17414, 2022.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Outlier suppression: Pushing the limit of low-bit transformer language models.Advances in Neural Informa- tion Processing Systems, 35:17402–17414, 2022

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.895674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6b86ac59-2285-4dfb-8993-1e5bd2975a13 · outbound

This paper cites Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.194747Z

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Observation cdc6778d-2a6f-4b35-86d9-374984c81022 · outbound

This paper cites Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.199052Z

Source-reported events for the cited work

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Observation 19167fcc-b934-4d2b-9c87-31d526edf448 · outbound

This paper cites Ptq4dit: Post-training quantization for diffu- sion transformers.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Ptq4dit: Post-training quantization for diffu- sion transformers

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.878530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4f876c41-df90-4d60-8fc0-b8824343d5cd · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Smoothquant: Accurate and effi- cient post-training quantization for large language models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.862711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b63353de-6c0d-4827-8f03-fdf4451b23f8 · outbound

This paper cites Timestep-Aware Correction for Quantized Diffusion Models.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Timestep-Aware Correction for Quantized Diffusion Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.212546Z

Source-reported events for the cited work

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Observation 3fbe5201-afca-47c6-a701-8406e23024f4 · outbound

This paper cites Zeroquant: Ef- ficient and affordable post-training quantization for large- scale transformers.Advances in Neural Information Process- ing Systems, 35:27168–27183, 2022.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Zeroquant: Ef- ficient and affordable post-training quantization for large- scale transformers.Advances in Neural Information Process- ing Systems, 35:27168–27183, 2022

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.847717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0ec71e02-d972-4ec7-94c0-a475596cd8e5 · outbound

This paper cites One-step diffusion with distribution matching distillation.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization One-step diffusion with distribution matching distillation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.832519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e09e94e-293e-41bb-903a-bd27c15d6f56 · outbound

This paper cites Shiftaddnet: A hardware-inspired deep network.Advances in Neural Information Processing Systems, 33:2771–2783,.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Shiftaddnet: A hardware-inspired deep network.Advances in Neural Information Processing Systems, 33:2771–2783,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.817772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 46296f5e-1033-4888-92b8-e539f4b33564 · outbound

This paper cites ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.232938Z

Source-reported events for the cited work

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Observation d71a4aa0-4ae9-4046-9d55-a389004d9103 · outbound

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

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.238530Z

Source-reported events for the cited work

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Observation f60d90d5-04b5-461d-b565-deac8ed8c8c9 · outbound

This paper cites Text-to-3D with Classifier Score Distillation.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Text-to-3D with Classifier Score Distillation

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.243698Z

Source-reported events for the cited work

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Observation 1fa78223-ea28-402b-978c-7b110f5552fe · outbound

This paper cites Adding conditional control to text-to-image diffusion models, 2023.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Adding conditional control to text-to-image diffusion models, 2023

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.799829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e2aea38e-1b2c-4b88-9356-c9bd594c7d9c · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization The unreasonable effectiveness of deep features as a perceptual metric

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.253951Z

Source-reported events for the cited work

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Observation 6f811c1c-0535-4d6c-b3a0-cc77be75548a · outbound

This paper cites ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:12.258915Z

Source-reported events for the cited work

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Observation 99a36461-548a-4634-aad9-98e0267f990f · outbound

This paper cites Thus, we modify their code to quantize those layers for fair comparison.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Thus, we modify their code to quantize those layers for fair comparison

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.756747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 20aacaab-79f3-4d94-9392-c7999c7562cb · outbound

This paper cites Implementation details This section provides a more detailed description of the experimental implementation presented in the main manuscript.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Implementation details This section provides a more detailed description of the experimental implementation presented in the main manuscript

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:42:12.772925Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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