{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GIDAKPG26E3WW2WKEGYMHW7WBH","short_pith_number":"pith:GIDAKPG2","schema_version":"1.0","canonical_sha256":"3206053cdaf1376b6aca21b0c3dbf609d82d0d0518d83b2acfe3624039965c3d","source":{"kind":"arxiv","id":"2306.05414","version":3},"attestation_state":"computed","paper":{"title":"Improving Tuning-Free Real Image Editing with Proximal Guidance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Akash Srivastava, Anastasis Stathopoulos, Di Liu, Dimitris Metaxas, Jindong Jiang, Kunpeng Song, Ligong Han, Mengwei Ren, Qi Chen, Qilong Zhangli, Ruijiang Gao, Song Wen, Xiaoxiao He, Yuxiao Chen, Zhaoyang Xia, Zhixing Zhang","submitted_at":"2023-06-08T17:57:18Z","abstract_excerpt":"DDIM inversion has revealed the remarkable potential of real image editing within diffusion-based methods. However, the accuracy of DDIM reconstruction degrades as larger classifier-free guidance (CFG) scales being used for enhanced editing. Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control. Negative-prompt inversion (NPI) further offers a training-free closed-form solution of NTI. However, it may introduce artifacts and is still constrained by DDIM reconstr"},"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":"2306.05414","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-08T17:57:18Z","cross_cats_sorted":[],"title_canon_sha256":"e6ccc778dcf59d961d72b5dc562b36a5ab69801a999f023b2c2b86bec11a9670","abstract_canon_sha256":"be98cf3694343d7a41d810a7cfdbaa25c40c2c6bf0c0c04a83739a3db0796fbc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:28:19.118652Z","signature_b64":"RTr/ddY6/BIfCi9twoE8V66C5L69aaUWN0fIe9s9ny07SvNgEZM7JOAgf2Ac9d5hl1c+uszzV1SOdihRZAvECA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3206053cdaf1376b6aca21b0c3dbf609d82d0d0518d83b2acfe3624039965c3d","last_reissued_at":"2026-07-05T06:28:19.118192Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:28:19.118192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Tuning-Free Real Image Editing with Proximal Guidance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Akash Srivastava, Anastasis Stathopoulos, Di Liu, Dimitris Metaxas, Jindong Jiang, Kunpeng Song, Ligong Han, Mengwei Ren, Qi Chen, Qilong Zhangli, Ruijiang Gao, Song Wen, Xiaoxiao He, Yuxiao Chen, Zhaoyang Xia, Zhixing Zhang","submitted_at":"2023-06-08T17:57:18Z","abstract_excerpt":"DDIM inversion has revealed the remarkable potential of real image editing within diffusion-based methods. However, the accuracy of DDIM reconstruction degrades as larger classifier-free guidance (CFG) scales being used for enhanced editing. Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control. Negative-prompt inversion (NPI) further offers a training-free closed-form solution of NTI. However, it may introduce artifacts and is still constrained by DDIM reconstr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.05414","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/2306.05414/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":"2306.05414","created_at":"2026-07-05T06:28:19.118249+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.05414v3","created_at":"2026-07-05T06:28:19.118249+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.05414","created_at":"2026-07-05T06:28:19.118249+00:00"},{"alias_kind":"pith_short_12","alias_value":"GIDAKPG26E3W","created_at":"2026-07-05T06:28:19.118249+00:00"},{"alias_kind":"pith_short_16","alias_value":"GIDAKPG26E3WW2WK","created_at":"2026-07-05T06:28:19.118249+00:00"},{"alias_kind":"pith_short_8","alias_value":"GIDAKPG2","created_at":"2026-07-05T06:28:19.118249+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2504.13109","citing_title":"UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16399","citing_title":"Stable and Near-Reversible Diffusion ODE Solvers for Image Editing","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25128","citing_title":"ResetEdit: Precise Text-guided Editing of Generated Image via Resettable Starting Latent","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GIDAKPG26E3WW2WKEGYMHW7WBH","json":"https://pith.science/pith/GIDAKPG26E3WW2WKEGYMHW7WBH.json","graph_json":"https://pith.science/api/pith-number/GIDAKPG26E3WW2WKEGYMHW7WBH/graph.json","events_json":"https://pith.science/api/pith-number/GIDAKPG26E3WW2WKEGYMHW7WBH/events.json","paper":"https://pith.science/paper/GIDAKPG2"},"agent_actions":{"view_html":"https://pith.science/pith/GIDAKPG26E3WW2WKEGYMHW7WBH","download_json":"https://pith.science/pith/GIDAKPG26E3WW2WKEGYMHW7WBH.json","view_paper":"https://pith.science/paper/GIDAKPG2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.05414&json=true","fetch_graph":"https://pith.science/api/pith-number/GIDAKPG26E3WW2WKEGYMHW7WBH/graph.json","fetch_events":"https://pith.science/api/pith-number/GIDAKPG26E3WW2WKEGYMHW7WBH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GIDAKPG26E3WW2WKEGYMHW7WBH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GIDAKPG26E3WW2WKEGYMHW7WBH/action/storage_attestation","attest_author":"https://pith.science/pith/GIDAKPG26E3WW2WKEGYMHW7WBH/action/author_attestation","sign_citation":"https://pith.science/pith/GIDAKPG26E3WW2WKEGYMHW7WBH/action/citation_signature","submit_replication":"https://pith.science/pith/GIDAKPG26E3WW2WKEGYMHW7WBH/action/replication_record"}},"created_at":"2026-07-05T06:28:19.118249+00:00","updated_at":"2026-07-05T06:28:19.118249+00:00"}