{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DJDMSYRREDMHTSC4DIPYD5BXWK","short_pith_number":"pith:DJDMSYRR","schema_version":"1.0","canonical_sha256":"1a46c9623120d879c85c1a1f81f437b28137fc1a22f3d75642096d590a2a261c","source":{"kind":"arxiv","id":"2403.00644","version":4},"attestation_state":"computed","paper":{"title":"Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fang Liu, Nanxuan Zhao, Rynson W.H. Lau, Yuhao Liu, Zhanghan Ke","submitted_at":"2024-03-01T16:25:17Z","abstract_excerpt":"Diffusion models trained on large-scale datasets have achieved remarkable progress in image synthesis. However, due to the randomness in the diffusion process, they often struggle with handling diverse low-level tasks that require details preservation. To overcome this limitation, we present a new Diff-Plugin framework to enable a single pre-trained diffusion model to generate high-fidelity results across a variety of low-level tasks. Specifically, we first propose a lightweight Task-Plugin module with a dual branch design to provide task-specific priors, guiding the diffusion process in prese"},"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":"2403.00644","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-01T16:25:17Z","cross_cats_sorted":[],"title_canon_sha256":"03999dc1922fe75847c48f2bdc5a04e2f209b7a77a3769f04cd848ff4eec0f65","abstract_canon_sha256":"63d4552830c9a3d1cf5cfcea56e350f5c6dff3d2561930ff59036e1fa029931d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:03.248958Z","signature_b64":"MybPGgZKLBE9S8rkCeeH+p79W5U/thWZuT4wBIjd0xpZvtxfSxEEQGEv2zCkK4rGjLHf9ICEyQxd80QwjIEpDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a46c9623120d879c85c1a1f81f437b28137fc1a22f3d75642096d590a2a261c","last_reissued_at":"2026-07-05T08:24:03.248429Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:03.248429Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fang Liu, Nanxuan Zhao, Rynson W.H. Lau, Yuhao Liu, Zhanghan Ke","submitted_at":"2024-03-01T16:25:17Z","abstract_excerpt":"Diffusion models trained on large-scale datasets have achieved remarkable progress in image synthesis. However, due to the randomness in the diffusion process, they often struggle with handling diverse low-level tasks that require details preservation. To overcome this limitation, we present a new Diff-Plugin framework to enable a single pre-trained diffusion model to generate high-fidelity results across a variety of low-level tasks. Specifically, we first propose a lightweight Task-Plugin module with a dual branch design to provide task-specific priors, guiding the diffusion process in prese"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.00644","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/2403.00644/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":"2403.00644","created_at":"2026-07-05T08:24:03.248495+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.00644v4","created_at":"2026-07-05T08:24:03.248495+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.00644","created_at":"2026-07-05T08:24:03.248495+00:00"},{"alias_kind":"pith_short_12","alias_value":"DJDMSYRREDMH","created_at":"2026-07-05T08:24:03.248495+00:00"},{"alias_kind":"pith_short_16","alias_value":"DJDMSYRREDMHTSC4","created_at":"2026-07-05T08:24:03.248495+00:00"},{"alias_kind":"pith_short_8","alias_value":"DJDMSYRR","created_at":"2026-07-05T08:24:03.248495+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DJDMSYRREDMHTSC4DIPYD5BXWK","json":"https://pith.science/pith/DJDMSYRREDMHTSC4DIPYD5BXWK.json","graph_json":"https://pith.science/api/pith-number/DJDMSYRREDMHTSC4DIPYD5BXWK/graph.json","events_json":"https://pith.science/api/pith-number/DJDMSYRREDMHTSC4DIPYD5BXWK/events.json","paper":"https://pith.science/paper/DJDMSYRR"},"agent_actions":{"view_html":"https://pith.science/pith/DJDMSYRREDMHTSC4DIPYD5BXWK","download_json":"https://pith.science/pith/DJDMSYRREDMHTSC4DIPYD5BXWK.json","view_paper":"https://pith.science/paper/DJDMSYRR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.00644&json=true","fetch_graph":"https://pith.science/api/pith-number/DJDMSYRREDMHTSC4DIPYD5BXWK/graph.json","fetch_events":"https://pith.science/api/pith-number/DJDMSYRREDMHTSC4DIPYD5BXWK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DJDMSYRREDMHTSC4DIPYD5BXWK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DJDMSYRREDMHTSC4DIPYD5BXWK/action/storage_attestation","attest_author":"https://pith.science/pith/DJDMSYRREDMHTSC4DIPYD5BXWK/action/author_attestation","sign_citation":"https://pith.science/pith/DJDMSYRREDMHTSC4DIPYD5BXWK/action/citation_signature","submit_replication":"https://pith.science/pith/DJDMSYRREDMHTSC4DIPYD5BXWK/action/replication_record"}},"created_at":"2026-07-05T08:24:03.248495+00:00","updated_at":"2026-07-05T08:24:03.248495+00:00"}