Paper Citation Record · LEDGER

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models

As of 20 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2506.18251.

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

pith.paper-citation-record.v1
2506.18251 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:28:12.909614Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy4
unresolved8
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e03b67f8-30ab-477f-9c8e-9f6cb2687edf · outbound

This paper cites Learning in Implicit Generative Models.

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models Learning in Implicit Generative Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:11.715204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1f55283-9aa7-4c1d-8032-e62b8ba90c1f · outbound

This paper cites Learning to Efficiently Sample from Diffusion Probabilistic Models.

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models Learning to Efficiently Sample from Diffusion Probabilistic Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:12.194823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f100a8c8-b303-422f-9537-3ddf1d173b24 · outbound

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

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:12.275148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c15d4621-2dcc-4511-8de6-3fa5bf28545e · outbound

This paper cites Appendix A.1.

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models Appendix A.1

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:13.799718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ca2b9b82-36d5-4266-97ec-452d451f4177 · outbound

This paper cites Following the popular evaluation protocol, we evaluate the text-to-image diffusion models under zero-shot text-to-image generation on the MS-COCO 2014 validation set (Lin et al.,.

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models Following the popular evaluation protocol, we evaluate the text-to-image diffusion models under zero-shot text-to-image generation on the MS-COCO 2014 validation set (Lin et al.,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:13.722409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9681a4e3-2a72-4529-b754-7858e2b4c27e · outbound

This paper cites N denotes that the Dot model is Ntimes faster than the Dash model.h×wdenotes the resolution of input feature maps.

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models N denotes that the Dot model is Ntimes faster than the Dash model.h×wdenotes the resolution of input feature maps

Reference 12

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T23:28:13.444822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b16b2d37-d9b2-4648-98fa-8b2f96f14f3b · outbound

This paper cites For a DM, we collect its official pre-trained model as the Dash model.

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models For a DM, we collect its official pre-trained model as the Dash model

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:13.361842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f6ea6fb9-0fea-4e8a-a9a3-52fa903bf490 · outbound

This paper cites an unresolved cited work.

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:28:13.548839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0525a52c-f437-479f-844e-eedb532d2a86 · outbound

This paper cites All the generated images are down-sampled from 512×512 to 256×256 for evaluation.

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models All the generated images are down-sampled from 512×512 to 256×256 for evaluation

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:13.658575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f169a4a4-fc52-4026-bef1-1400a4e9ad4b · outbound

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

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:11.967592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e00d50fc-1ab9-46df-8a85-0e4c251f8639 · outbound

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

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:11.575916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a1e5b9b-b686-40fd-b92e-0782348f6893 · outbound

This paper cites Accelerating Large Language Model Decoding with Speculative Sampling.

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models Accelerating Large Language Model Decoding with Speculative Sampling

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:11.324884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11250470-bfe8-4d14-80af-f7149c0c2ca4 · outbound

This paper cites Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference.

Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:11.464744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.