Paper Citation Record · LEDGER

DiTFastAttn: Attention Compression for Diffusion Transformer Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.08552.

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

pith.paper-citation-record.v1
2406.08552 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 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:35:10.656633Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T19:43:54.708519Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
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 06386444-43ac-4a00-b733-0562152542ab · inbound

PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference cites this paper.

PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference DiTFastAttn: Attention Compression for Diffusion Transformer Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-24T01:18:42.394407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3fc96d14-e17a-4868-956e-0a68bd083f52 · inbound

Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity cites this paper.

Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity DiTFastAttn: Attention Compression for Diffusion Transformer Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-09T14:35:10.656633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4dfadbb-03ce-420f-aa00-c83686f05533 · inbound

SADA: Stability-guided Adaptive Diffusion Acceleration cites this paper.

SADA: Stability-guided Adaptive Diffusion Acceleration DiTFastAttn: Attention Compression for Diffusion Transformer Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.906738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 743e656b-1d9c-49d3-9c35-0a4365b2cdf1 · inbound

Forecast then Calibrate: Feature Caching as ODE for Efficient Diffusion Transformers cites this paper.

Forecast then Calibrate: Feature Caching as ODE for Efficient Diffusion Transformers DiTFastAttn: Attention Compression for Diffusion Transformer Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T17:31:13.096559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c180d5e-51bb-43e0-93cb-20035b90f25e · inbound

Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting cites this paper.

Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting DiTFastAttn: Attention Compression for Diffusion Transformer Models

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:08:04.870474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 58272790-d84c-41da-82ed-f6b0279438d3 · inbound

RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models cites this paper.

RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models DiTFastAttn: Attention Compression for Diffusion Transformer Models

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:43:54.710009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.