{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LS3NORC2RF6SOY5OELBF76LJSK","short_pith_number":"pith:LS3NORC2","schema_version":"1.0","canonical_sha256":"5cb6d7445a897d2763ae22c25ff96992838590f5d56f03e33d7bb3fa71e37c0b","source":{"kind":"arxiv","id":"2406.01954","version":2},"attestation_state":"computed","paper":{"title":"Plug-and-Play Diffusion Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hui Qu, Kevin Duarte, Mingi Kwon, Ratheesh Kalarot, Siavash Khodadadeh, Wei-An Lin, Yi-Ting Hsiao","submitted_at":"2024-06-04T04:22:47Z","abstract_excerpt":"Diffusion models have shown tremendous results in image generation. However, due to the iterative nature of the diffusion process and its reliance on classifier-free guidance, inference times are slow. In this paper, we propose a new distillation approach for guided diffusion models in which an external lightweight guide model is trained while the original text-to-image model remains frozen. We show that our method reduces the inference computation of classifier-free guided latent-space diffusion models by almost half, and only requires 1\\% trainable parameters of the base model. Furthermore, "},"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":"2406.01954","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-04T04:22:47Z","cross_cats_sorted":[],"title_canon_sha256":"bc9f0acc711c3e084c88aab3fd5dc8815c21220384e3ffea29125160c09f2597","abstract_canon_sha256":"2e68405dbb3dda116f772ed87304f21ace613b5cd3990ad2151483bd6cb9a075"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:31:52.099435Z","signature_b64":"2ja7BwWC4cPVVyo45YBAc1tbZK7Mj0jXzilLb7LDE5AnPEoafSepKeJClVX4u3IuFHX4sQ7EavMA8cDBzSbXBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cb6d7445a897d2763ae22c25ff96992838590f5d56f03e33d7bb3fa71e37c0b","last_reissued_at":"2026-07-05T08:31:52.098899Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:31:52.098899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Plug-and-Play Diffusion Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hui Qu, Kevin Duarte, Mingi Kwon, Ratheesh Kalarot, Siavash Khodadadeh, Wei-An Lin, Yi-Ting Hsiao","submitted_at":"2024-06-04T04:22:47Z","abstract_excerpt":"Diffusion models have shown tremendous results in image generation. However, due to the iterative nature of the diffusion process and its reliance on classifier-free guidance, inference times are slow. In this paper, we propose a new distillation approach for guided diffusion models in which an external lightweight guide model is trained while the original text-to-image model remains frozen. We show that our method reduces the inference computation of classifier-free guided latent-space diffusion models by almost half, and only requires 1\\% trainable parameters of the base model. Furthermore, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.01954","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/2406.01954/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":"2406.01954","created_at":"2026-07-05T08:31:52.098970+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.01954v2","created_at":"2026-07-05T08:31:52.098970+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.01954","created_at":"2026-07-05T08:31:52.098970+00:00"},{"alias_kind":"pith_short_12","alias_value":"LS3NORC2RF6S","created_at":"2026-07-05T08:31:52.098970+00:00"},{"alias_kind":"pith_short_16","alias_value":"LS3NORC2RF6SOY5O","created_at":"2026-07-05T08:31:52.098970+00:00"},{"alias_kind":"pith_short_8","alias_value":"LS3NORC2","created_at":"2026-07-05T08:31:52.098970+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/LS3NORC2RF6SOY5OELBF76LJSK","json":"https://pith.science/pith/LS3NORC2RF6SOY5OELBF76LJSK.json","graph_json":"https://pith.science/api/pith-number/LS3NORC2RF6SOY5OELBF76LJSK/graph.json","events_json":"https://pith.science/api/pith-number/LS3NORC2RF6SOY5OELBF76LJSK/events.json","paper":"https://pith.science/paper/LS3NORC2"},"agent_actions":{"view_html":"https://pith.science/pith/LS3NORC2RF6SOY5OELBF76LJSK","download_json":"https://pith.science/pith/LS3NORC2RF6SOY5OELBF76LJSK.json","view_paper":"https://pith.science/paper/LS3NORC2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.01954&json=true","fetch_graph":"https://pith.science/api/pith-number/LS3NORC2RF6SOY5OELBF76LJSK/graph.json","fetch_events":"https://pith.science/api/pith-number/LS3NORC2RF6SOY5OELBF76LJSK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LS3NORC2RF6SOY5OELBF76LJSK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LS3NORC2RF6SOY5OELBF76LJSK/action/storage_attestation","attest_author":"https://pith.science/pith/LS3NORC2RF6SOY5OELBF76LJSK/action/author_attestation","sign_citation":"https://pith.science/pith/LS3NORC2RF6SOY5OELBF76LJSK/action/citation_signature","submit_replication":"https://pith.science/pith/LS3NORC2RF6SOY5OELBF76LJSK/action/replication_record"}},"created_at":"2026-07-05T08:31:52.098970+00:00","updated_at":"2026-07-05T08:31:52.098970+00:00"}