{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KR7MOEXBAOQDZ3RMACFVZJNWYA","short_pith_number":"pith:KR7MOEXB","schema_version":"1.0","canonical_sha256":"547ec712e103a03cee2c008b5ca5b6c00c0eb88cd227d6ed516560bda5eeffa9","source":{"kind":"arxiv","id":"2401.15559","version":1},"attestation_state":"computed","paper":{"title":"IntentTuner: An Interactive Framework for Integrating Human Intents in Fine-tuning Text-to-Image Generative Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Wei Zeng, Xingchen Zeng, Yilin Ye, Ziyao Gao","submitted_at":"2024-01-28T03:53:06Z","abstract_excerpt":"Fine-tuning facilitates the adaptation of text-to-image generative models to novel concepts (e.g., styles and portraits), empowering users to forge creatively customized content. Recent efforts on fine-tuning focus on reducing training data and lightening computation overload but neglect alignment with user intentions, particularly in manual curation of multi-modal training data and intent-oriented evaluation. Informed by a formative study with fine-tuning practitioners for comprehending user intentions, we propose IntentTuner, an interactive framework that intelligently incorporates human int"},"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":"2401.15559","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-01-28T03:53:06Z","cross_cats_sorted":[],"title_canon_sha256":"9234c9eb3d99d93cde175de0b5933edd07ed5c18fe275784bb000487cc708c4b","abstract_canon_sha256":"7db161773500a101af301fda1b8febca5acb9a8493b9bbcd3010557170eabdb8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:38:36.898643Z","signature_b64":"OyCKNq/z3RAFtrEtIx91z+8nm4ynbxlIfDdBer0cENFeTvbZrgVrwKA7KyQkD/4GIFg3V2G1lJrSXmAedE1tBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"547ec712e103a03cee2c008b5ca5b6c00c0eb88cd227d6ed516560bda5eeffa9","last_reissued_at":"2026-07-05T07:38:36.898204Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:38:36.898204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IntentTuner: An Interactive Framework for Integrating Human Intents in Fine-tuning Text-to-Image Generative Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Wei Zeng, Xingchen Zeng, Yilin Ye, Ziyao Gao","submitted_at":"2024-01-28T03:53:06Z","abstract_excerpt":"Fine-tuning facilitates the adaptation of text-to-image generative models to novel concepts (e.g., styles and portraits), empowering users to forge creatively customized content. Recent efforts on fine-tuning focus on reducing training data and lightening computation overload but neglect alignment with user intentions, particularly in manual curation of multi-modal training data and intent-oriented evaluation. Informed by a formative study with fine-tuning practitioners for comprehending user intentions, we propose IntentTuner, an interactive framework that intelligently incorporates human int"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.15559","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/2401.15559/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":"2401.15559","created_at":"2026-07-05T07:38:36.898265+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.15559v1","created_at":"2026-07-05T07:38:36.898265+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.15559","created_at":"2026-07-05T07:38:36.898265+00:00"},{"alias_kind":"pith_short_12","alias_value":"KR7MOEXBAOQD","created_at":"2026-07-05T07:38:36.898265+00:00"},{"alias_kind":"pith_short_16","alias_value":"KR7MOEXBAOQDZ3RM","created_at":"2026-07-05T07:38:36.898265+00:00"},{"alias_kind":"pith_short_8","alias_value":"KR7MOEXB","created_at":"2026-07-05T07:38:36.898265+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/KR7MOEXBAOQDZ3RMACFVZJNWYA","json":"https://pith.science/pith/KR7MOEXBAOQDZ3RMACFVZJNWYA.json","graph_json":"https://pith.science/api/pith-number/KR7MOEXBAOQDZ3RMACFVZJNWYA/graph.json","events_json":"https://pith.science/api/pith-number/KR7MOEXBAOQDZ3RMACFVZJNWYA/events.json","paper":"https://pith.science/paper/KR7MOEXB"},"agent_actions":{"view_html":"https://pith.science/pith/KR7MOEXBAOQDZ3RMACFVZJNWYA","download_json":"https://pith.science/pith/KR7MOEXBAOQDZ3RMACFVZJNWYA.json","view_paper":"https://pith.science/paper/KR7MOEXB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.15559&json=true","fetch_graph":"https://pith.science/api/pith-number/KR7MOEXBAOQDZ3RMACFVZJNWYA/graph.json","fetch_events":"https://pith.science/api/pith-number/KR7MOEXBAOQDZ3RMACFVZJNWYA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KR7MOEXBAOQDZ3RMACFVZJNWYA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KR7MOEXBAOQDZ3RMACFVZJNWYA/action/storage_attestation","attest_author":"https://pith.science/pith/KR7MOEXBAOQDZ3RMACFVZJNWYA/action/author_attestation","sign_citation":"https://pith.science/pith/KR7MOEXBAOQDZ3RMACFVZJNWYA/action/citation_signature","submit_replication":"https://pith.science/pith/KR7MOEXBAOQDZ3RMACFVZJNWYA/action/replication_record"}},"created_at":"2026-07-05T07:38:36.898265+00:00","updated_at":"2026-07-05T07:38:36.898265+00:00"}