{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AWLNMFHZQ5AFBMTCV7CIW5DEEU","short_pith_number":"pith:AWLNMFHZ","schema_version":"1.0","canonical_sha256":"0596d614f9874050b262afc48b7464250679fb02a826aef3ac508d4e9bf1cc4c","source":{"kind":"arxiv","id":"2412.17162","version":1},"attestation_state":"computed","paper":{"title":"Generative Diffusion Modeling: A Practical Handbook","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Chi Jin, Zihan Ding","submitted_at":"2024-12-22T21:02:36Z","abstract_excerpt":"This handbook offers a unified perspective on diffusion models, encompassing diffusion probabilistic models, score-based generative models, consistency models, rectified flow, and related methods. By standardizing notations and aligning them with code implementations, it aims to bridge the \"paper-to-code\" gap and facilitate robust implementations and fair comparisons. The content encompasses the fundamentals of diffusion models, the pre-training process, and various post-training methods. Post-training techniques include model distillation and reward-based fine-tuning. Designed as a practical "},"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":"2412.17162","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-22T21:02:36Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"61df2a0e27ec759db3512daed3dc19978a056cbda67e7056f814d249c754a378","abstract_canon_sha256":"7d4cf98adfe5d307839da56963ea4ea85d9d44353ce121feee8af0716e17052f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:53:18.317940Z","signature_b64":"rNTxPxmjRShvgkW9777OP/NHMiHRkhqre9Hn05qAnsBtH3JBTeznYiCDjxn90T6rBepDGrLshJCiGjsQbyZkDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0596d614f9874050b262afc48b7464250679fb02a826aef3ac508d4e9bf1cc4c","last_reissued_at":"2026-07-05T09:53:18.317487Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:53:18.317487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative Diffusion Modeling: A Practical Handbook","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Chi Jin, Zihan Ding","submitted_at":"2024-12-22T21:02:36Z","abstract_excerpt":"This handbook offers a unified perspective on diffusion models, encompassing diffusion probabilistic models, score-based generative models, consistency models, rectified flow, and related methods. By standardizing notations and aligning them with code implementations, it aims to bridge the \"paper-to-code\" gap and facilitate robust implementations and fair comparisons. The content encompasses the fundamentals of diffusion models, the pre-training process, and various post-training methods. Post-training techniques include model distillation and reward-based fine-tuning. Designed as a practical "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.17162","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/2412.17162/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":"2412.17162","created_at":"2026-07-05T09:53:18.317538+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.17162v1","created_at":"2026-07-05T09:53:18.317538+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.17162","created_at":"2026-07-05T09:53:18.317538+00:00"},{"alias_kind":"pith_short_12","alias_value":"AWLNMFHZQ5AF","created_at":"2026-07-05T09:53:18.317538+00:00"},{"alias_kind":"pith_short_16","alias_value":"AWLNMFHZQ5AFBMTC","created_at":"2026-07-05T09:53:18.317538+00:00"},{"alias_kind":"pith_short_8","alias_value":"AWLNMFHZ","created_at":"2026-07-05T09:53:18.317538+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AWLNMFHZQ5AFBMTCV7CIW5DEEU","json":"https://pith.science/pith/AWLNMFHZQ5AFBMTCV7CIW5DEEU.json","graph_json":"https://pith.science/api/pith-number/AWLNMFHZQ5AFBMTCV7CIW5DEEU/graph.json","events_json":"https://pith.science/api/pith-number/AWLNMFHZQ5AFBMTCV7CIW5DEEU/events.json","paper":"https://pith.science/paper/AWLNMFHZ"},"agent_actions":{"view_html":"https://pith.science/pith/AWLNMFHZQ5AFBMTCV7CIW5DEEU","download_json":"https://pith.science/pith/AWLNMFHZQ5AFBMTCV7CIW5DEEU.json","view_paper":"https://pith.science/paper/AWLNMFHZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.17162&json=true","fetch_graph":"https://pith.science/api/pith-number/AWLNMFHZQ5AFBMTCV7CIW5DEEU/graph.json","fetch_events":"https://pith.science/api/pith-number/AWLNMFHZQ5AFBMTCV7CIW5DEEU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AWLNMFHZQ5AFBMTCV7CIW5DEEU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AWLNMFHZQ5AFBMTCV7CIW5DEEU/action/storage_attestation","attest_author":"https://pith.science/pith/AWLNMFHZQ5AFBMTCV7CIW5DEEU/action/author_attestation","sign_citation":"https://pith.science/pith/AWLNMFHZQ5AFBMTCV7CIW5DEEU/action/citation_signature","submit_replication":"https://pith.science/pith/AWLNMFHZQ5AFBMTCV7CIW5DEEU/action/replication_record"}},"created_at":"2026-07-05T09:53:18.317538+00:00","updated_at":"2026-07-05T09:53:18.317538+00:00"}