{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:WHQT5TAYDNYLZ425USZI2VH2E6","short_pith_number":"pith:WHQT5TAY","schema_version":"1.0","canonical_sha256":"b1e13ecc181b70bcf35da4b28d54fa2799e3c14eca1e777c1710704b41300df7","source":{"kind":"arxiv","id":"2306.00714","version":1},"attestation_state":"computed","paper":{"title":"Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Jie Zhang, Jingcai Guo, Qihua Zhou, Ruibin Li, Song Guo, Xinyang Jiang, Yifei Shen, Zhenhua Han","submitted_at":"2023-06-01T14:20:06Z","abstract_excerpt":"Diffusion-based Generative Models (DGMs) have achieved unparalleled performance in synthesizing high-quality visual content, opening up the opportunity to improve image super-resolution (SR) tasks. Recent solutions for these tasks often train architecture-specific DGMs from scratch, or require iterative fine-tuning and distillation on pre-trained DGMs, both of which take considerable time and hardware investments. More seriously, since the DGMs are established with a discrete pre-defined upsampling scale, they cannot well match the emerging requirements of arbitrary-scale super-resolution (ASS"},"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":"2306.00714","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-01T14:20:06Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"3c012186d3f755454db1bcc1ef265a5a312c4b9f11955dde658e26c73484699d","abstract_canon_sha256":"8314d8e33cba09d4edea5acca93232d5bb8d921138a4808a6f855a4538674d17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:16:30.222691Z","signature_b64":"CIJwTYYUjUVl9KO3KyU0YQpi+ImczGsNUI2p6kK8PKBykCK0DEjpyRoPLcMbkd587wHdnIQK1vn7g0G7DKokCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1e13ecc181b70bcf35da4b28d54fa2799e3c14eca1e777c1710704b41300df7","last_reissued_at":"2026-07-05T06:16:30.222206Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:16:30.222206Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Jie Zhang, Jingcai Guo, Qihua Zhou, Ruibin Li, Song Guo, Xinyang Jiang, Yifei Shen, Zhenhua Han","submitted_at":"2023-06-01T14:20:06Z","abstract_excerpt":"Diffusion-based Generative Models (DGMs) have achieved unparalleled performance in synthesizing high-quality visual content, opening up the opportunity to improve image super-resolution (SR) tasks. Recent solutions for these tasks often train architecture-specific DGMs from scratch, or require iterative fine-tuning and distillation on pre-trained DGMs, both of which take considerable time and hardware investments. More seriously, since the DGMs are established with a discrete pre-defined upsampling scale, they cannot well match the emerging requirements of arbitrary-scale super-resolution (ASS"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.00714","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/2306.00714/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":"2306.00714","created_at":"2026-07-05T06:16:30.222265+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.00714v1","created_at":"2026-07-05T06:16:30.222265+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.00714","created_at":"2026-07-05T06:16:30.222265+00:00"},{"alias_kind":"pith_short_12","alias_value":"WHQT5TAYDNYL","created_at":"2026-07-05T06:16:30.222265+00:00"},{"alias_kind":"pith_short_16","alias_value":"WHQT5TAYDNYLZ425","created_at":"2026-07-05T06:16:30.222265+00:00"},{"alias_kind":"pith_short_8","alias_value":"WHQT5TAY","created_at":"2026-07-05T06:16:30.222265+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.07429","citing_title":"Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07429","citing_title":"Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WHQT5TAYDNYLZ425USZI2VH2E6","json":"https://pith.science/pith/WHQT5TAYDNYLZ425USZI2VH2E6.json","graph_json":"https://pith.science/api/pith-number/WHQT5TAYDNYLZ425USZI2VH2E6/graph.json","events_json":"https://pith.science/api/pith-number/WHQT5TAYDNYLZ425USZI2VH2E6/events.json","paper":"https://pith.science/paper/WHQT5TAY"},"agent_actions":{"view_html":"https://pith.science/pith/WHQT5TAYDNYLZ425USZI2VH2E6","download_json":"https://pith.science/pith/WHQT5TAYDNYLZ425USZI2VH2E6.json","view_paper":"https://pith.science/paper/WHQT5TAY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.00714&json=true","fetch_graph":"https://pith.science/api/pith-number/WHQT5TAYDNYLZ425USZI2VH2E6/graph.json","fetch_events":"https://pith.science/api/pith-number/WHQT5TAYDNYLZ425USZI2VH2E6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WHQT5TAYDNYLZ425USZI2VH2E6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WHQT5TAYDNYLZ425USZI2VH2E6/action/storage_attestation","attest_author":"https://pith.science/pith/WHQT5TAYDNYLZ425USZI2VH2E6/action/author_attestation","sign_citation":"https://pith.science/pith/WHQT5TAYDNYLZ425USZI2VH2E6/action/citation_signature","submit_replication":"https://pith.science/pith/WHQT5TAYDNYLZ425USZI2VH2E6/action/replication_record"}},"created_at":"2026-07-05T06:16:30.222265+00:00","updated_at":"2026-07-05T06:16:30.222265+00:00"}