{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VD4Z4CDQRK6QBOFEP662RMEVLH","short_pith_number":"pith:VD4Z4CDQ","schema_version":"1.0","canonical_sha256":"a8f99e08708abd00b8a47fbda8b09559d10a1b34f21e66b9b00013669d0e3380","source":{"kind":"arxiv","id":"2406.01561","version":4},"attestation_state":"computed","paper":{"title":"Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Hai Huang, Huangjie Zheng, Mingyuan Zhou, Zhendong Wang","submitted_at":"2024-06-03T17:44:11Z","abstract_excerpt":"Diffusion-based text-to-image generation models trained on extensive text-image pairs have demonstrated the ability to produce photorealistic images aligned with textual descriptions. However, a significant limitation of these models is their slow sample generation process, which requires iterative refinement through the same network. To overcome this, we introduce a data-free guided distillation method that enables the efficient distillation of pretrained Stable Diffusion models without access to the real training data, often restricted due to legal, privacy, or cost concerns. This method enh"},"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":"2406.01561","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-03T17:44:11Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG","stat.ML"],"title_canon_sha256":"7b58c50dfcad32bc843c330d9b0b84ffc43ce4d4f4ca31fdf8da677b8f148878","abstract_canon_sha256":"be077ec9b5c535b5164f9aef712dc263b1e51b5caac824b60720e1096b2747ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:11:20.467400Z","signature_b64":"q6PzRthJxBr+3sZ31cBixzNjN8G0tzSrdN5xEZDxeKNQWhSFVP6ypE6+g2aoXu86/SMBgqEy3zhFAjVKboDHCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8f99e08708abd00b8a47fbda8b09559d10a1b34f21e66b9b00013669d0e3380","last_reissued_at":"2026-07-05T10:11:20.466909Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:11:20.466909Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Hai Huang, Huangjie Zheng, Mingyuan Zhou, Zhendong Wang","submitted_at":"2024-06-03T17:44:11Z","abstract_excerpt":"Diffusion-based text-to-image generation models trained on extensive text-image pairs have demonstrated the ability to produce photorealistic images aligned with textual descriptions. However, a significant limitation of these models is their slow sample generation process, which requires iterative refinement through the same network. To overcome this, we introduce a data-free guided distillation method that enables the efficient distillation of pretrained Stable Diffusion models without access to the real training data, often restricted due to legal, privacy, or cost concerns. This method enh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.01561","kind":"arxiv","version":4},"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/2406.01561/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":"2406.01561","created_at":"2026-07-05T10:11:20.466964+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.01561v4","created_at":"2026-07-05T10:11:20.466964+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.01561","created_at":"2026-07-05T10:11:20.466964+00:00"},{"alias_kind":"pith_short_12","alias_value":"VD4Z4CDQRK6Q","created_at":"2026-07-05T10:11:20.466964+00:00"},{"alias_kind":"pith_short_16","alias_value":"VD4Z4CDQRK6QBOFE","created_at":"2026-07-05T10:11:20.466964+00:00"},{"alias_kind":"pith_short_8","alias_value":"VD4Z4CDQ","created_at":"2026-07-05T10:11:20.466964+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.32020","citing_title":"Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21489","citing_title":"Variance Reduction for Expectations with Diffusion Teachers","ref_index":105,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21489","citing_title":"Variance Reduction for Expectations with Diffusion Teachers","ref_index":105,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21484","citing_title":"One-Step Distillation of Discrete Diffusion Image Generators via Fixed-Point Iteration","ref_index":58,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VD4Z4CDQRK6QBOFEP662RMEVLH","json":"https://pith.science/pith/VD4Z4CDQRK6QBOFEP662RMEVLH.json","graph_json":"https://pith.science/api/pith-number/VD4Z4CDQRK6QBOFEP662RMEVLH/graph.json","events_json":"https://pith.science/api/pith-number/VD4Z4CDQRK6QBOFEP662RMEVLH/events.json","paper":"https://pith.science/paper/VD4Z4CDQ"},"agent_actions":{"view_html":"https://pith.science/pith/VD4Z4CDQRK6QBOFEP662RMEVLH","download_json":"https://pith.science/pith/VD4Z4CDQRK6QBOFEP662RMEVLH.json","view_paper":"https://pith.science/paper/VD4Z4CDQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.01561&json=true","fetch_graph":"https://pith.science/api/pith-number/VD4Z4CDQRK6QBOFEP662RMEVLH/graph.json","fetch_events":"https://pith.science/api/pith-number/VD4Z4CDQRK6QBOFEP662RMEVLH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VD4Z4CDQRK6QBOFEP662RMEVLH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VD4Z4CDQRK6QBOFEP662RMEVLH/action/storage_attestation","attest_author":"https://pith.science/pith/VD4Z4CDQRK6QBOFEP662RMEVLH/action/author_attestation","sign_citation":"https://pith.science/pith/VD4Z4CDQRK6QBOFEP662RMEVLH/action/citation_signature","submit_replication":"https://pith.science/pith/VD4Z4CDQRK6QBOFEP662RMEVLH/action/replication_record"}},"created_at":"2026-07-05T10:11:20.466964+00:00","updated_at":"2026-07-05T10:11:20.466964+00:00"}