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Paper Citation Record · LEDGER

DiTFastAttn: Attention Compression for Diffusion Transformer Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 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 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:06:47.906738Z

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-24T01:17:11.261301Z digest=sha256:207f97b4cc38d63b996bc0ab5547243fb38bf06d85a9ae7c6dd796f6f6a925ab

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.

source=pdf_text observed=2026-08-06T15:06:47.906738Z digest=sha256:b03ef024a708bb69ac9418e0d37bb3abb240cd711fa016ddc0740641d97aa340

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.

source=pdf_text observed=2026-08-05T17:31:13.096559Z digest=sha256:d035caf53697bc28025a5109e3180b8fba5b19153b96f687db7ed6b24226ec0f

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-14T22:03:27.981438Z digest=sha256:63e3658ea16034171ef893862fbc282ba2f1b8d538e3dd23e7103a5f4d25ffde

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T19:40:42.033793Z digest=sha256:6965e30260027de87fe4401ac2d6bbd6667c75e0cd349879eb6bf131d9b09c76