{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GSBN4NSPLUJIVPHKOHTBUPHZCF","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"904f5fe3ed694b35fa6d695cdcae53ee00f7ab5c9d9a6869e2335d834921ad80","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-02T07:05:39Z","title_canon_sha256":"acf3dafdbd6adaf308ec36320a9abda49fcbc0d144c9da552035264d3c8e264e"},"schema_version":"1.0","source":{"id":"2412.01199","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.01199","created_at":"2026-07-05T09:42:55Z"},{"alias_kind":"arxiv_version","alias_value":"2412.01199v1","created_at":"2026-07-05T09:42:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.01199","created_at":"2026-07-05T09:42:55Z"},{"alias_kind":"pith_short_12","alias_value":"GSBN4NSPLUJI","created_at":"2026-07-05T09:42:55Z"},{"alias_kind":"pith_short_16","alias_value":"GSBN4NSPLUJIVPHK","created_at":"2026-07-05T09:42:55Z"},{"alias_kind":"pith_short_8","alias_value":"GSBN4NSP","created_at":"2026-07-05T09:42:55Z"}],"graph_snapshots":[{"event_id":"sha256:4d35e9d692c46528ae0e9e1bdce2850d0a63992e81210d1da75004285a208ee3","target":"graph","created_at":"2026-07-05T09:42:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.01199/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion Transformers have demonstrated remarkable capabilities in image generation but often come with excessive parameterization, resulting in considerable inference overhead in real-world applications. In this work, we present TinyFusion, a depth pruning method designed to remove redundant layers from diffusion transformers via end-to-end learning. The core principle of our approach is to create a pruned model with high recoverability, allowing it to regain strong performance after fine-tuning. To accomplish this, we introduce a differentiable sampling technique to make pruning learnable, ","authors_text":"Gongfan Fang, Kunjun Li, Xinchao Wang, Xinyin Ma","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-02T07:05:39Z","title":"TinyFusion: Diffusion Transformers Learned Shallow"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.01199","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3231878692d1fae7bcfaf207141736bd31deabd1654af595dba4b04d77828f1f","target":"record","created_at":"2026-07-05T09:42:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"904f5fe3ed694b35fa6d695cdcae53ee00f7ab5c9d9a6869e2335d834921ad80","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-02T07:05:39Z","title_canon_sha256":"acf3dafdbd6adaf308ec36320a9abda49fcbc0d144c9da552035264d3c8e264e"},"schema_version":"1.0","source":{"id":"2412.01199","kind":"arxiv","version":1}},"canonical_sha256":"3482de364f5d128abcea71e61a3cf911585167b70cc26181614dc8ee6e56c7cb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3482de364f5d128abcea71e61a3cf911585167b70cc26181614dc8ee6e56c7cb","first_computed_at":"2026-07-05T09:42:55.716139Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:42:55.716139Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QeF5Xpv04yMy4ZT77YnyDuoB22NKpHEUmmRRTySXqFQnTIgJQ4dtUEEC5WCgSHxWyTrtla7nLY7y9FvB36+HAA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:42:55.716652Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.01199","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3231878692d1fae7bcfaf207141736bd31deabd1654af595dba4b04d77828f1f","sha256:4d35e9d692c46528ae0e9e1bdce2850d0a63992e81210d1da75004285a208ee3"],"state_sha256":"92078eaea21f6d5f77822496479bc4a8925a343ed8bb57596cff46ea8dccb9aa"}