{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4KJGVQDGDDLIZ3STAIIQ2CDIFR","short_pith_number":"pith:4KJGVQDG","schema_version":"1.0","canonical_sha256":"e2926ac06618d68cee5302110d08682c5a552bee00625f1db742ee5b51423aab","source":{"kind":"arxiv","id":"2411.19156","version":3},"attestation_state":"computed","paper":{"title":"LoRA of Change: Learning to Generate LoRA for the Editing Instruction from A Single Before-After Image Pair","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chi Zhang, Hanwang Zhang, Jiaxin Shi, Jiequan Cui, Jingjing Chen, Xue Song, Yu-Gang Jiang","submitted_at":"2024-11-28T13:55:06Z","abstract_excerpt":"In this paper, we propose the LoRA of Change (LoC) framework for image editing with visual instructions, i.e., before-after image pairs. Compared to the ambiguities, insufficient specificity, and diverse interpretations of natural language, visual instructions can accurately reflect users' intent. Building on the success of LoRA in text-based image editing and generation, we dynamically learn an instruction-specific LoRA to encode the \"change\" in a before-after image pair, enhancing the interpretability and reusability of our model. Furthermore, generalizable models for image editing with visu"},"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":"2411.19156","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-28T13:55:06Z","cross_cats_sorted":[],"title_canon_sha256":"8bd53227a688a3023bed5c753ae88496a49b60c5c34a5fb5529d063c0a44a33d","abstract_canon_sha256":"a02edb90f43004c1f4f8298e22d244568e648ef024b9642bb1cf7f88c5f02144"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:58.977445Z","signature_b64":"M2iDHQ++yZtxZL+tRzq3rQBNGpPsa4auZ5BeNNYoDzLnLNet/iXkXDjE9rX09X2+ke/cpaT76kmsXY8+4v9zBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2926ac06618d68cee5302110d08682c5a552bee00625f1db742ee5b51423aab","last_reissued_at":"2026-07-05T09:45:58.976999Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:58.976999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LoRA of Change: Learning to Generate LoRA for the Editing Instruction from A Single Before-After Image Pair","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chi Zhang, Hanwang Zhang, Jiaxin Shi, Jiequan Cui, Jingjing Chen, Xue Song, Yu-Gang Jiang","submitted_at":"2024-11-28T13:55:06Z","abstract_excerpt":"In this paper, we propose the LoRA of Change (LoC) framework for image editing with visual instructions, i.e., before-after image pairs. Compared to the ambiguities, insufficient specificity, and diverse interpretations of natural language, visual instructions can accurately reflect users' intent. Building on the success of LoRA in text-based image editing and generation, we dynamically learn an instruction-specific LoRA to encode the \"change\" in a before-after image pair, enhancing the interpretability and reusability of our model. Furthermore, generalizable models for image editing with visu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.19156","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/2411.19156/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":"2411.19156","created_at":"2026-07-05T09:45:58.977054+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.19156v3","created_at":"2026-07-05T09:45:58.977054+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.19156","created_at":"2026-07-05T09:45:58.977054+00:00"},{"alias_kind":"pith_short_12","alias_value":"4KJGVQDGDDLI","created_at":"2026-07-05T09:45:58.977054+00:00"},{"alias_kind":"pith_short_16","alias_value":"4KJGVQDGDDLIZ3ST","created_at":"2026-07-05T09:45:58.977054+00:00"},{"alias_kind":"pith_short_8","alias_value":"4KJGVQDG","created_at":"2026-07-05T09:45:58.977054+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.07940","citing_title":"Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4KJGVQDGDDLIZ3STAIIQ2CDIFR","json":"https://pith.science/pith/4KJGVQDGDDLIZ3STAIIQ2CDIFR.json","graph_json":"https://pith.science/api/pith-number/4KJGVQDGDDLIZ3STAIIQ2CDIFR/graph.json","events_json":"https://pith.science/api/pith-number/4KJGVQDGDDLIZ3STAIIQ2CDIFR/events.json","paper":"https://pith.science/paper/4KJGVQDG"},"agent_actions":{"view_html":"https://pith.science/pith/4KJGVQDGDDLIZ3STAIIQ2CDIFR","download_json":"https://pith.science/pith/4KJGVQDGDDLIZ3STAIIQ2CDIFR.json","view_paper":"https://pith.science/paper/4KJGVQDG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.19156&json=true","fetch_graph":"https://pith.science/api/pith-number/4KJGVQDGDDLIZ3STAIIQ2CDIFR/graph.json","fetch_events":"https://pith.science/api/pith-number/4KJGVQDGDDLIZ3STAIIQ2CDIFR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4KJGVQDGDDLIZ3STAIIQ2CDIFR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4KJGVQDGDDLIZ3STAIIQ2CDIFR/action/storage_attestation","attest_author":"https://pith.science/pith/4KJGVQDGDDLIZ3STAIIQ2CDIFR/action/author_attestation","sign_citation":"https://pith.science/pith/4KJGVQDGDDLIZ3STAIIQ2CDIFR/action/citation_signature","submit_replication":"https://pith.science/pith/4KJGVQDGDDLIZ3STAIIQ2CDIFR/action/replication_record"}},"created_at":"2026-07-05T09:45:58.977054+00:00","updated_at":"2026-07-05T09:45:58.977054+00:00"}