{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OLFKG6M2BWTFOR2AX4TELRYYM2","short_pith_number":"pith:OLFKG6M2","schema_version":"1.0","canonical_sha256":"72caa3799a0da6574740bf2645c718668ac3d8e0d20969b2754276bcd6adbc97","source":{"kind":"arxiv","id":"2312.02432","version":3},"attestation_state":"computed","paper":{"title":"Orthogonal Adaptation for Modular Customization of Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gordon Wetzstein, Guandao Yang, Kfir Aberman, Ryan Po","submitted_at":"2023-12-05T02:17:48Z","abstract_excerpt":"Customization techniques for text-to-image models have paved the way for a wide range of previously unattainable applications, enabling the generation of specific concepts across diverse contexts and styles. While existing methods facilitate high-fidelity customization for individual concepts or a limited, pre-defined set of them, they fall short of achieving scalability, where a single model can seamlessly render countless concepts. In this paper, we address a new problem called Modular Customization, with the goal of efficiently merging customized models that were fine-tuned independently fo"},"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":"2312.02432","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-05T02:17:48Z","cross_cats_sorted":[],"title_canon_sha256":"bef9df2b140e2f4031c51bd0293151d60e0ebe86fc9e612537fc7241c30b97a6","abstract_canon_sha256":"40e2e40e7759195ec9d5a086170d2d4730aa7fcfa45b11fa4862f650f12d5966"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:44:32.998609Z","signature_b64":"mmXNOAkVDMWEWbYRuf9O2DpTb6HJu0HE+tI12hlBrIbjL+B0/+LH2aOPJNVJMHKB0k7xPN8kRYLCYFnvhPxpBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72caa3799a0da6574740bf2645c718668ac3d8e0d20969b2754276bcd6adbc97","last_reissued_at":"2026-07-05T09:44:32.998122Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:44:32.998122Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Orthogonal Adaptation for Modular Customization of Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gordon Wetzstein, Guandao Yang, Kfir Aberman, Ryan Po","submitted_at":"2023-12-05T02:17:48Z","abstract_excerpt":"Customization techniques for text-to-image models have paved the way for a wide range of previously unattainable applications, enabling the generation of specific concepts across diverse contexts and styles. While existing methods facilitate high-fidelity customization for individual concepts or a limited, pre-defined set of them, they fall short of achieving scalability, where a single model can seamlessly render countless concepts. In this paper, we address a new problem called Modular Customization, with the goal of efficiently merging customized models that were fine-tuned independently fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.02432","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/2312.02432/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":"2312.02432","created_at":"2026-07-05T09:44:32.998179+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.02432v3","created_at":"2026-07-05T09:44:32.998179+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.02432","created_at":"2026-07-05T09:44:32.998179+00:00"},{"alias_kind":"pith_short_12","alias_value":"OLFKG6M2BWTF","created_at":"2026-07-05T09:44:32.998179+00:00"},{"alias_kind":"pith_short_16","alias_value":"OLFKG6M2BWTFOR2A","created_at":"2026-07-05T09:44:32.998179+00:00"},{"alias_kind":"pith_short_8","alias_value":"OLFKG6M2","created_at":"2026-07-05T09:44:32.998179+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.18068","citing_title":"PersonaCraft: Personalized and Controllable Full-Body Multi-Human Scene Generation Using Occlusion-Aware 3D-Conditioned Diffusion","ref_index":63,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OLFKG6M2BWTFOR2AX4TELRYYM2","json":"https://pith.science/pith/OLFKG6M2BWTFOR2AX4TELRYYM2.json","graph_json":"https://pith.science/api/pith-number/OLFKG6M2BWTFOR2AX4TELRYYM2/graph.json","events_json":"https://pith.science/api/pith-number/OLFKG6M2BWTFOR2AX4TELRYYM2/events.json","paper":"https://pith.science/paper/OLFKG6M2"},"agent_actions":{"view_html":"https://pith.science/pith/OLFKG6M2BWTFOR2AX4TELRYYM2","download_json":"https://pith.science/pith/OLFKG6M2BWTFOR2AX4TELRYYM2.json","view_paper":"https://pith.science/paper/OLFKG6M2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.02432&json=true","fetch_graph":"https://pith.science/api/pith-number/OLFKG6M2BWTFOR2AX4TELRYYM2/graph.json","fetch_events":"https://pith.science/api/pith-number/OLFKG6M2BWTFOR2AX4TELRYYM2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OLFKG6M2BWTFOR2AX4TELRYYM2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OLFKG6M2BWTFOR2AX4TELRYYM2/action/storage_attestation","attest_author":"https://pith.science/pith/OLFKG6M2BWTFOR2AX4TELRYYM2/action/author_attestation","sign_citation":"https://pith.science/pith/OLFKG6M2BWTFOR2AX4TELRYYM2/action/citation_signature","submit_replication":"https://pith.science/pith/OLFKG6M2BWTFOR2AX4TELRYYM2/action/replication_record"}},"created_at":"2026-07-05T09:44:32.998179+00:00","updated_at":"2026-07-05T09:44:32.998179+00:00"}