{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3ZLUM2SYXESZ5SZRDR2PACTDAZ","short_pith_number":"pith:3ZLUM2SY","schema_version":"1.0","canonical_sha256":"de57466a58b9259ecb311c74f00a6306609c910aabed423de18615f95c701383","source":{"kind":"arxiv","id":"2406.06911","version":3},"attestation_state":"computed","paper":{"title":"AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gongfan Fang, Xinchao Wang, Xinyin Ma, Zhenxiong Tan, Zigeng Chen","submitted_at":"2024-06-11T03:09:37Z","abstract_excerpt":"Diffusion models have garnered significant interest from the community for their great generative ability across various applications. However, their typical multi-step sequential-denoising nature gives rise to high cumulative latency, thereby precluding the possibilities of parallel computation. To address this, we introduce AsyncDiff, a universal and plug-and-play acceleration scheme that enables model parallelism across multiple devices. Our approach divides the cumbersome noise prediction model into multiple components, assigning each to a different device. To break the dependency chain be"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2406.06911","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-11T03:09:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"efea0f14ab1bd84aaa1791a626d03c2ad8e73fe1fc8fc71beb9f8aef24326585","abstract_canon_sha256":"f40b47d26e09ab1df05b14593ee9bc44c60c9ecfe74f0e969eaee578a4f8afc7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:12:07.141959Z","signature_b64":"OX2gWesAsR2zUQsUXGkwmQRM30N82ZVtTtfJ6aFGZiB2JSEDnKZwD+9GjVkH2Tu1RvN3GDUzryvCx/GC/GmKDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de57466a58b9259ecb311c74f00a6306609c910aabed423de18615f95c701383","last_reissued_at":"2026-07-05T09:12:07.141449Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:12:07.141449Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gongfan Fang, Xinchao Wang, Xinyin Ma, Zhenxiong Tan, Zigeng Chen","submitted_at":"2024-06-11T03:09:37Z","abstract_excerpt":"Diffusion models have garnered significant interest from the community for their great generative ability across various applications. However, their typical multi-step sequential-denoising nature gives rise to high cumulative latency, thereby precluding the possibilities of parallel computation. To address this, we introduce AsyncDiff, a universal and plug-and-play acceleration scheme that enables model parallelism across multiple devices. Our approach divides the cumbersome noise prediction model into multiple components, assigning each to a different device. To break the dependency chain be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.06911","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2406.06911/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2406.06911","created_at":"2026-07-05T09:12:07.141507+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.06911v3","created_at":"2026-07-05T09:12:07.141507+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.06911","created_at":"2026-07-05T09:12:07.141507+00:00"},{"alias_kind":"pith_short_12","alias_value":"3ZLUM2SYXESZ","created_at":"2026-07-05T09:12:07.141507+00:00"},{"alias_kind":"pith_short_16","alias_value":"3ZLUM2SYXESZ5SZR","created_at":"2026-07-05T09:12:07.141507+00:00"},{"alias_kind":"pith_short_8","alias_value":"3ZLUM2SY","created_at":"2026-07-05T09:12:07.141507+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.31026","citing_title":"OTCache: Optimal Transport for Geometry-Aware Caching in Diffusion Models","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14561","citing_title":"CoCoDiff: Optimizing Collective Communications for Distributed Diffusion Transformer Inference Under Ulysses Sequence Parallelism","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3ZLUM2SYXESZ5SZRDR2PACTDAZ","json":"https://pith.science/pith/3ZLUM2SYXESZ5SZRDR2PACTDAZ.json","graph_json":"https://pith.science/api/pith-number/3ZLUM2SYXESZ5SZRDR2PACTDAZ/graph.json","events_json":"https://pith.science/api/pith-number/3ZLUM2SYXESZ5SZRDR2PACTDAZ/events.json","paper":"https://pith.science/paper/3ZLUM2SY"},"agent_actions":{"view_html":"https://pith.science/pith/3ZLUM2SYXESZ5SZRDR2PACTDAZ","download_json":"https://pith.science/pith/3ZLUM2SYXESZ5SZRDR2PACTDAZ.json","view_paper":"https://pith.science/paper/3ZLUM2SY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.06911&json=true","fetch_graph":"https://pith.science/api/pith-number/3ZLUM2SYXESZ5SZRDR2PACTDAZ/graph.json","fetch_events":"https://pith.science/api/pith-number/3ZLUM2SYXESZ5SZRDR2PACTDAZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3ZLUM2SYXESZ5SZRDR2PACTDAZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3ZLUM2SYXESZ5SZRDR2PACTDAZ/action/storage_attestation","attest_author":"https://pith.science/pith/3ZLUM2SYXESZ5SZRDR2PACTDAZ/action/author_attestation","sign_citation":"https://pith.science/pith/3ZLUM2SYXESZ5SZRDR2PACTDAZ/action/citation_signature","submit_replication":"https://pith.science/pith/3ZLUM2SYXESZ5SZRDR2PACTDAZ/action/replication_record"}},"created_at":"2026-07-05T09:12:07.141507+00:00","updated_at":"2026-07-05T09:12:07.141507+00:00"}