{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:V2SH6CTRCHDHZDRWHDMOHUIZZA","short_pith_number":"pith:V2SH6CTR","schema_version":"1.0","canonical_sha256":"aea47f0a7111c67c8e3638d8e3d119c81c244d25f34c862cfb3e1200114414d4","source":{"kind":"arxiv","id":"2503.19462","version":1},"attestation_state":"computed","paper":{"title":"AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haiyu Zhang, Xihui Liu, Xinyuan Chen, Yaohui Wang, Yunhong Wang, Yu Qiao","submitted_at":"2025-03-25T08:52:07Z","abstract_excerpt":"Diffusion models have achieved remarkable progress in the field of video generation. However, their iterative denoising nature requires a large number of inference steps to generate a video, which is slow and computationally expensive. In this paper, we begin with a detailed analysis of the challenges present in existing diffusion distillation methods and propose a novel efficient method, namely AccVideo, to reduce the inference steps for accelerating video diffusion models with synthetic dataset. We leverage the pretrained video diffusion model to generate multiple valid denoising trajectorie"},"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":"2503.19462","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-25T08:52:07Z","cross_cats_sorted":[],"title_canon_sha256":"80a1c3836a86e3c7550c27ecd57d3903c296a439c2457dec537c6fc3cf76fbf4","abstract_canon_sha256":"b7bc15a2c336f4e1607a8b564c3ad087c1b884cef8d2d3b8a0aedf57ed92b28e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:38:55.740757Z","signature_b64":"ryOynVAL+LqQ0CnnrrfC4Azu9rOsKfde/K5EE3fppAsk95Nh+xSvHJ56+GDmfhH4LNlnWYlB9+4vPOB+U5CiCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aea47f0a7111c67c8e3638d8e3d119c81c244d25f34c862cfb3e1200114414d4","last_reissued_at":"2026-07-05T10:38:55.740255Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:38:55.740255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haiyu Zhang, Xihui Liu, Xinyuan Chen, Yaohui Wang, Yunhong Wang, Yu Qiao","submitted_at":"2025-03-25T08:52:07Z","abstract_excerpt":"Diffusion models have achieved remarkable progress in the field of video generation. However, their iterative denoising nature requires a large number of inference steps to generate a video, which is slow and computationally expensive. In this paper, we begin with a detailed analysis of the challenges present in existing diffusion distillation methods and propose a novel efficient method, namely AccVideo, to reduce the inference steps for accelerating video diffusion models with synthetic dataset. We leverage the pretrained video diffusion model to generate multiple valid denoising trajectorie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.19462","kind":"arxiv","version":1},"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/2503.19462/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":"2503.19462","created_at":"2026-07-05T10:38:55.740324+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.19462v1","created_at":"2026-07-05T10:38:55.740324+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.19462","created_at":"2026-07-05T10:38:55.740324+00:00"},{"alias_kind":"pith_short_12","alias_value":"V2SH6CTRCHDH","created_at":"2026-07-05T10:38:55.740324+00:00"},{"alias_kind":"pith_short_16","alias_value":"V2SH6CTRCHDHZDRW","created_at":"2026-07-05T10:38:55.740324+00:00"},{"alias_kind":"pith_short_8","alias_value":"V2SH6CTR","created_at":"2026-07-05T10:38:55.740324+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.27336","citing_title":"PARE: Pruning and Adaptive Routing for Efficient Video Generation","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2603.21002","citing_title":"SURF: Signature-Retained Fast Video Generation","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2602.05449","citing_title":"DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching","ref_index":76,"is_internal_anchor":false},{"citing_arxiv_id":"2512.14614","citing_title":"WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling","ref_index":78,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21291","citing_title":"Exploring the Role of Synthetic Data Augmentation in Controllable Human-Centric Video Generation","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02849","citing_title":"Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V2SH6CTRCHDHZDRWHDMOHUIZZA","json":"https://pith.science/pith/V2SH6CTRCHDHZDRWHDMOHUIZZA.json","graph_json":"https://pith.science/api/pith-number/V2SH6CTRCHDHZDRWHDMOHUIZZA/graph.json","events_json":"https://pith.science/api/pith-number/V2SH6CTRCHDHZDRWHDMOHUIZZA/events.json","paper":"https://pith.science/paper/V2SH6CTR"},"agent_actions":{"view_html":"https://pith.science/pith/V2SH6CTRCHDHZDRWHDMOHUIZZA","download_json":"https://pith.science/pith/V2SH6CTRCHDHZDRWHDMOHUIZZA.json","view_paper":"https://pith.science/paper/V2SH6CTR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.19462&json=true","fetch_graph":"https://pith.science/api/pith-number/V2SH6CTRCHDHZDRWHDMOHUIZZA/graph.json","fetch_events":"https://pith.science/api/pith-number/V2SH6CTRCHDHZDRWHDMOHUIZZA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V2SH6CTRCHDHZDRWHDMOHUIZZA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V2SH6CTRCHDHZDRWHDMOHUIZZA/action/storage_attestation","attest_author":"https://pith.science/pith/V2SH6CTRCHDHZDRWHDMOHUIZZA/action/author_attestation","sign_citation":"https://pith.science/pith/V2SH6CTRCHDHZDRWHDMOHUIZZA/action/citation_signature","submit_replication":"https://pith.science/pith/V2SH6CTRCHDHZDRWHDMOHUIZZA/action/replication_record"}},"created_at":"2026-07-05T10:38:55.740324+00:00","updated_at":"2026-07-05T10:38:55.740324+00:00"}