Paper Citation Record · LEDGER

Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.17343.

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

pith.paper-citation-record.v1
2406.17343 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:08:18.124350Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T20:46:13.522029Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7f7a6b72-d101-4161-a237-b86f2f88b6d1 · inbound

FPQVAR: Floating Point Quantization for Visual Autoregressive Model with FPGA Hardware Co-design cites this paper.

FPQVAR: Floating Point Quantization for Visual Autoregressive Model with FPGA Hardware Co-design Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T15:08:18.124350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1633e1d-d337-422c-abc5-40a05b7c6ee3 · inbound

QwT-v2: Practical, Effective and Efficient Post-Training Quantization cites this paper.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:30.736528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90373be9-13dd-4fbe-aeaf-99e779030ca8 · inbound

Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers cites this paper.

Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T13:22:14.679938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:44.272734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5609ffd5-44d1-4dd4-8e4d-892e86ec8250 · inbound

Boundary-Protection W8A8 HiFloat8 Quantization for Large-Scale Text-to-Video Diffusion Transformers cites this paper.

Boundary-Protection W8A8 HiFloat8 Quantization for Large-Scale Text-to-Video Diffusion Transformers Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-01T20:46:13.523814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a81bfd35-4a83-446b-9999-ee5314664980 · inbound

Importance-Aware OBS Pruning for Diffusion Models cites this paper.

Importance-Aware OBS Pruning for Diffusion Models Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-01T10:59:55.347631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.