{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:S5PMB47E5FHSGI2HGNF2IDLPAU","short_pith_number":"pith:S5PMB47E","schema_version":"1.0","canonical_sha256":"975ec0f3e4e94f232347334ba40d6f050ac3663e22a0e2b38fe1c657b4dd49b9","source":{"kind":"arxiv","id":"2312.11396","version":2},"attestation_state":"computed","paper":{"title":"MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Lan Chen, Mike Zheng Shou, Qi Mao, Yuchao Gu, Zhen Fang","submitted_at":"2023-12-18T17:55:44Z","abstract_excerpt":"Recent diffusion-based image editing approaches have exhibited impressive editing capabilities in images with simple compositions. However, localized editing in complex scenarios has not been well-studied in the literature, despite its growing real-world demands. Existing mask-based inpainting methods fall short of retaining the underlying structure within the edit region. Meanwhile, mask-free attention-based methods often exhibit editing leakage and misalignment in more complex compositions. In this work, we develop MAG-Edit, a training-free, inference-stage optimization method, which enables"},"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":"2312.11396","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-18T17:55:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"18b73f0dd318ca9b54c5fdad43d33f27b55d5f43eed86e7fa4ecae94df047380","abstract_canon_sha256":"101ea5050170ca1cf6dd48f803ecd2134eb33fa8ceb4b26616f9e5803cdc2fdd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:26:42.411844Z","signature_b64":"61FHlmAoyZPohCHJfcurUbwV5w4OQnoUNmmouf7bhsOB1nLXZEdB7wWYkL6zFfQZG9H47A6YyLWv2rsFaGBaCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"975ec0f3e4e94f232347334ba40d6f050ac3663e22a0e2b38fe1c657b4dd49b9","last_reissued_at":"2026-07-05T07:26:42.411356Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:26:42.411356Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Lan Chen, Mike Zheng Shou, Qi Mao, Yuchao Gu, Zhen Fang","submitted_at":"2023-12-18T17:55:44Z","abstract_excerpt":"Recent diffusion-based image editing approaches have exhibited impressive editing capabilities in images with simple compositions. However, localized editing in complex scenarios has not been well-studied in the literature, despite its growing real-world demands. Existing mask-based inpainting methods fall short of retaining the underlying structure within the edit region. Meanwhile, mask-free attention-based methods often exhibit editing leakage and misalignment in more complex compositions. In this work, we develop MAG-Edit, a training-free, inference-stage optimization method, which enables"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.11396","kind":"arxiv","version":2},"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/2312.11396/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":"2312.11396","created_at":"2026-07-05T07:26:42.411409+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.11396v2","created_at":"2026-07-05T07:26:42.411409+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.11396","created_at":"2026-07-05T07:26:42.411409+00:00"},{"alias_kind":"pith_short_12","alias_value":"S5PMB47E5FHS","created_at":"2026-07-05T07:26:42.411409+00:00"},{"alias_kind":"pith_short_16","alias_value":"S5PMB47E5FHSGI2H","created_at":"2026-07-05T07:26:42.411409+00:00"},{"alias_kind":"pith_short_8","alias_value":"S5PMB47E","created_at":"2026-07-05T07:26:42.411409+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25568","citing_title":"Rethinking Scribble-Guided Image Editing: Generalization, Instruction Adherence, and Multi-Tasking","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S5PMB47E5FHSGI2HGNF2IDLPAU","json":"https://pith.science/pith/S5PMB47E5FHSGI2HGNF2IDLPAU.json","graph_json":"https://pith.science/api/pith-number/S5PMB47E5FHSGI2HGNF2IDLPAU/graph.json","events_json":"https://pith.science/api/pith-number/S5PMB47E5FHSGI2HGNF2IDLPAU/events.json","paper":"https://pith.science/paper/S5PMB47E"},"agent_actions":{"view_html":"https://pith.science/pith/S5PMB47E5FHSGI2HGNF2IDLPAU","download_json":"https://pith.science/pith/S5PMB47E5FHSGI2HGNF2IDLPAU.json","view_paper":"https://pith.science/paper/S5PMB47E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.11396&json=true","fetch_graph":"https://pith.science/api/pith-number/S5PMB47E5FHSGI2HGNF2IDLPAU/graph.json","fetch_events":"https://pith.science/api/pith-number/S5PMB47E5FHSGI2HGNF2IDLPAU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S5PMB47E5FHSGI2HGNF2IDLPAU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S5PMB47E5FHSGI2HGNF2IDLPAU/action/storage_attestation","attest_author":"https://pith.science/pith/S5PMB47E5FHSGI2HGNF2IDLPAU/action/author_attestation","sign_citation":"https://pith.science/pith/S5PMB47E5FHSGI2HGNF2IDLPAU/action/citation_signature","submit_replication":"https://pith.science/pith/S5PMB47E5FHSGI2HGNF2IDLPAU/action/replication_record"}},"created_at":"2026-07-05T07:26:42.411409+00:00","updated_at":"2026-07-05T07:26:42.411409+00:00"}