Paper Citation Record · LEDGER

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling

As of 17 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2509.01624.

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

pith.paper-citation-record.v1
2509.01624 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:27:44.433560Z

measured 57 of 57 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T10:01:02.421961Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:17:58.040905Z

Reference resolution

55 of 55 outbound references displayed

verified exact3
verified fuzzy23
unresolved29
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0defc3b3-29c6-4d5d-8d2d-58b7205d5cad · outbound

This paper cites URL https://www.rapidata.ai/.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling URL https://www.rapidata.ai/

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.984781Z

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Observation 492782e0-b615-4163-8b33-e178894fd1bc · outbound

This paper cites SegDiff: Image Segmentation with Diffusion Probabilistic Models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling SegDiff: Image Segmentation with Diffusion Probabilistic Models

Reference 2

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Observation 420d5eee-b536-4d74-9755-eb05242eede8 · outbound

This paper cites Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models

Reference 3

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Observation 7d7efaf3-f0db-4654-a634-be42525d3e11 · outbound

This paper cites Structured denoising diffusion models in discrete state-spaces.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Structured denoising diffusion models in discrete state-spaces

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.973016Z

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Observation bd5e31b7-d67e-4e6b-91af-bc89ebd53f4d · outbound

This paper cites Blended diffusion for text-driven editing of natural images.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Blended diffusion for text-driven editing of natural images

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.961722Z

Source-reported events for the cited work

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

Observation b4098fa9-4e78-40d4-a090-828b87a090c3 · outbound

This paper cites Label-Efficient Semantic Segmentation with Diffusion Models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Label-Efficient Semantic Segmentation with Diffusion Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:44.258086Z

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Observation 5783b0eb-27c0-4ab1-aed6-a41c3cb534dc · outbound

This paper cites Flux.1-schnell.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Flux.1-schnell

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.951214Z

Source-reported events for the cited work

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Observation 22d5060e-1400-4278-b070-d873cff1af0a · outbound

This paper cites Denoising pretraining for semantic segmentation.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Denoising pretraining for semantic segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.940252Z

Source-reported events for the cited work

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Observation 8f3c7acc-14d9-4cf0-bbac-cf94fd0aa40e · outbound

This paper cites High-Frequency Space Diffusion Models for Accelerated MRI.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling High-Frequency Space Diffusion Models for Accelerated MRI

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:27:44.644897Z

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Observation ee68b442-9523-4c32-96a5-8091f63a8ffc · outbound

This paper cites Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

Reference 10

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Source-reported events for the cited work

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Observation b9c4433c-08f5-40f1-8060-6c468d1d4393 · outbound

This paper cites Structural pruning for diffusion models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Structural pruning for diffusion models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.928536Z

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Observation 23e12a2e-5a46-443e-906a-dd29237cc9e1 · outbound

This paper cites SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation

Reference 12

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Source-reported events for the cited work

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Observation 1f5f86b8-3ac8-47a2-bf0d-75acba189d43 · outbound

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

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Ptqd: Accurate post-training quantization for diffusion models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.917212Z

Source-reported events for the cited work

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Observation 4ec6cb75-56a3-4351-8fe8-2d23cf283f56 · outbound

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

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:44.287092Z

Source-reported events for the cited work

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Observation c95b54b2-c41c-4e10-b5ff-870ecf60618e · outbound

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

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:44.290914Z

Source-reported events for the cited work

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Observation d178fe08-855f-469b-a61d-05f656a23da7 · outbound

This paper cites Denoising diffusion probabilistic models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Denoising diffusion probabilistic models

Reference 16

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Source-reported events for the cited work

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Observation ee204cd6-eeb2-42f2-8f9a-5e3b86a8a265 · outbound

This paper cites Cascaded diffusion models for high fidelity image generation.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Cascaded diffusion models for high fidelity image generation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.894604Z

Source-reported events for the cited work

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

Observation ecd1dbfa-c3d3-4645-8d27-db7a8fff36ad · outbound

This paper cites Knowledge diffusion for distillation.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Knowledge diffusion for distillation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.884742Z

Source-reported events for the cited work

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

Observation fbfb5b77-b0bb-4a1a-ba7c-09ca78da857b · outbound

This paper cites Rethinking fid: Towards a better evaluation metric for image generation.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Rethinking fid: Towards a better evaluation metric for image generation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.874130Z

Source-reported events for the cited work

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

Observation 970383a9-041c-4feb-91b5-727aab36dbdf · outbound

This paper cites Elucidating the Design Space of Diffusion-Based Generative Models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Elucidating the Design Space of Diffusion-Based Generative Models

Reference 20

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Observation d76977b5-bd43-47f9-a6bb-4f2cce630e64 · outbound

This paper cites Q-refine: A perceptual quality refiner for ai-generated image.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Q-refine: A perceptual quality refiner for ai-generated image

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.863748Z

Source-reported events for the cited work

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

Observation 4724425f-6fdf-4d3d-9353-30524f74616e · outbound

This paper cites Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024

Reference 22

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Observation cbeff166-0d13-4b91-91df-ec13cb36ec84 · outbound

This paper cites Svdquant: Absorbing outliers by low-rank component for 4-bit diffusion models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Svdquant: Absorbing outliers by low-rank component for 4-bit diffusion models

Reference 23

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Source-reported events for the cited work

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

Observation a3c5d482-f279-4161-8f2b-c187f4a60051 · outbound

This paper cites Diffusion-lm improves controllable text generation.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Diffusion-lm improves controllable text generation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.837052Z

Source-reported events for the cited work

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

Observation c132c994-1042-473c-b8d5-7e6bccf64102 · outbound

This paper cites Q-Diffusion: Quantizing Diffusion Models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Q-Diffusion: Quantizing Diffusion Models

Reference 25

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Observation 0b8a1e27-c1cc-4583-b458-520c4a359df9 · outbound

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

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Q-dm: An efficient low-bit quantized diffusion model

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.826895Z

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Observation 2f404633-5c9c-4b13-b932-adb6f9afad6c · outbound

This paper cites Microsoft coco: Common objects in context.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Microsoft coco: Common objects in context

Reference 27

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Observation 9ce295e6-850f-4e99-84c6-60f46b9da002 · outbound

This paper cites Instaflow: One step is enough for high-quality diffusion-based text-to-image generation.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Instaflow: One step is enough for high-quality diffusion-based text-to-image generation

Reference 28

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Source-reported events for the cited work

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Observation 3ff86c7a-ab70-445b-81fc-8de551633820 · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 30

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Observation aed887b8-bdef-44ee-a64e-a8e09e76f279 · outbound

This paper cites Sdedit: Guided image synthesis and editing with stochastic differential equations.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Sdedit: Guided image synthesis and editing with stochastic differential equations

Reference 31

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Observation c34e5e07-730f-4ecf-bbcb-f3843e3a7176 · outbound

This paper cites No-reference image quality assessment in the spatial domain.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling No-reference image quality assessment in the spatial domain

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.794153Z

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

Observation c161580c-d08f-446f-85a4-cf8e1639e571 · outbound

This paper cites Scalable diffusion models with transformers.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Scalable diffusion models with transformers

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.783675Z

Source-reported events for the cited work

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

Observation e4fc8c7f-6c07-4883-8712-a79d8b4e2915 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 34

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Observation 2c0da24f-fccc-4a08-bc74-4ae09aaa6808 · outbound

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

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 35

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Observation 52ffc2f0-0ce9-45dd-9b59-20752699ba36 · outbound

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

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling High- resolution image synthesis with latent diffusion models

Reference 36

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Observation 80bc0fe7-b9db-42ee-8740-6728d628ef51 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling U-net: Convolutional networks for biomedical image segmentation

Reference 37

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Observation c7caa514-71ed-40c1-927b-b17ee60dbc3b · outbound

This paper cites Adversarial diffusion distillation.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Adversarial diffusion distillation

Reference 38

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Observation e19ebbc7-cb4e-4676-8e7a-7a42c7535d34 · outbound

This paper cites Post-training Quantization on Diffusion Models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Post-training Quantization on Diffusion Models

Reference 39

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Observation 955da619-e16d-444b-b273-c1b15dcc741c · outbound

This paper cites Temporal dynamic quantization for diffusion models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Temporal dynamic quantization for diffusion models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.754861Z

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Observation 6b039102-e32b-4772-a623-a810bbaa5a65 · outbound

This paper cites Denoising Diffusion Implicit Models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Denoising Diffusion Implicit Models

Reference 41

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Observation 81d5ab57-af51-45e0-91b7-14c0cf49a3ba · outbound

This paper cites Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole

Reference 42

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This paper cites Consistency Models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Consistency Models

Reference 43

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Observation 6cdb20f1-82f7-4029-88a5-ad35e794699b · outbound

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

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Towards Accurate Post-training Quantization for Diffusion Models

Reference 44

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Observation a8133fd6-d83c-44b5-976b-a9108ec691ff · outbound

This paper cites Phased Consistency Models.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Phased Consistency Models

Reference 45

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Observation ed96a546-2652-4a60-b950-1a181ef006d0 · outbound

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

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning

Reference 46

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Observation f513f5c4-fb7b-4a22-9235-6a29b359d742 · outbound

This paper cites Exploring clip for assessing the look and feel of images.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Exploring clip for assessing the look and feel of images

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.737128Z

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Observation 1782560e-2776-4c6c-9e2a-57bbaa8c8e3b · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Image quality assessment: from error visibility to structural similarity

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.726314Z

Source-reported events for the cited work

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

Observation 84ce6e72-674c-4833-927e-4864f60dda9f · outbound

This paper cites Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.715467Z

Source-reported events for the cited work

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Observation cb66ead4-d74f-4af4-873d-423cae488f39 · outbound

This paper cites Diffusion Probabilistic Modeling for Video Generation.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Diffusion Probabilistic Modeling for Video Generation

Reference 50

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Observation 0f393e94-f3a0-427d-979a-7e1b9681097d · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 51

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Observation 041ac23d-c14b-44f5-8646-1dae106f7da5 · outbound

This paper cites One-step Diffusion with Distribution Matching Distillation.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling One-step Diffusion with Distribution Matching Distillation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:44.419023Z

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Observation 1f9c1edd-8767-4cd2-932b-7124fc851698 · outbound

This paper cites Mixdq: Memory-efficient few-step text-to-image diffusion models with metric-decoupled mixed precision quantization.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Mixdq: Memory-efficient few-step text-to-image diffusion models with metric-decoupled mixed precision quantization

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.704621Z

Source-reported events for the cited work

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

Observation c0f45a45-431f-4a67-88c6-136cbc51bf31 · outbound

This paper cites Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:44.426010Z

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Unavailable: canonical work link unavailable.

Observation c9dc1475-61e4-44fa-95af-f86e19b78cd1 · outbound

This paper cites an unresolved cited work.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-05T12:27:44.694000Z

Source-reported events for the cited work

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

Observation 77887e47-f64f-48b8-ab6d-f5827a52efbc · outbound

This paper cites Why is Q-Sched able to learn a better noise schedule when it is also a linear correction?.

Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling Why is Q-Sched able to learn a better noise schedule when it is also a linear correction?

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:27:44.683851Z

Source-reported events for the cited work

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

Pith citing papers

Observation 7c1f083b-9948-499d-9e7c-8da01cb0a8a2 · inbound

Holding the FP8 Quality Ceiling at 8-Bit Weights and Activations: INT8 and GGUF Post-Training Quantization of Ideogram 4.0 for Consumer GPUs cites this paper.

Holding the FP8 Quality Ceiling at 8-Bit Weights and Activations: INT8 and GGUF Post-Training Quantization of Ideogram 4.0 for Consumer GPUs Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-14T01:20:45.740284Z

Source-reported events for the cited work

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

Observation fc109e16-0596-42fb-8f05-276ac93cc206 · inbound

Quantizing Recursive Reasoning Models cites this paper.

Quantizing Recursive Reasoning Models Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-02T10:01:02.421961Z

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Unavailable: canonical work link unavailable.