{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BUJ6OVNRN6SXQ4THSPPMW3OWC2","short_pith_number":"pith:BUJ6OVNR","schema_version":"1.0","canonical_sha256":"0d13e755b16fa578726793decb6dd616b0845c12c3bca88ff810774ad938f85b","source":{"kind":"arxiv","id":"2412.00144","version":1},"attestation_state":"computed","paper":{"title":"MPQ-Diff: Mixed Precision Quantization for Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Basile Lewandowski, Lydia Y. Chen, Rocco Manz Maruzzelli","submitted_at":"2024-11-28T19:38:26Z","abstract_excerpt":"Diffusion models (DMs) generate remarkable high quality images via the stochastic denoising process, which unfortunately incurs high sampling time. Post-quantizing the trained diffusion models in fixed bit-widths, e.g., 4 bits on weights and 8 bits on activation, is shown effective in accelerating sampling time while maintaining the image quality. Motivated by the observation that the cross-layer dependency of DMs vary across layers and sampling steps, we propose a mixed precision quantization scheme, MPQ-Diff, which allocates different bit-width to the weights and activation of the layers. We"},"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":"2412.00144","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-28T19:38:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5cf09703cd2fbc37ac6c0ce0b5695020e2d2ada648c5ddde246b88f6d82fa35f","abstract_canon_sha256":"7a599464698f09a293b293ac409bafcfc70861d59b65fa8777f6c148341406cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:42:36.874511Z","signature_b64":"yxUtl60CX9PknufNgne9iljgSWc6mofw20mZcBZ3kQdG00R5cmcBHyT0zrl0QnFxfiAFCzXt3i71VsNHZ8hGDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d13e755b16fa578726793decb6dd616b0845c12c3bca88ff810774ad938f85b","last_reissued_at":"2026-07-05T09:42:36.874075Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:42:36.874075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MPQ-Diff: Mixed Precision Quantization for Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Basile Lewandowski, Lydia Y. Chen, Rocco Manz Maruzzelli","submitted_at":"2024-11-28T19:38:26Z","abstract_excerpt":"Diffusion models (DMs) generate remarkable high quality images via the stochastic denoising process, which unfortunately incurs high sampling time. Post-quantizing the trained diffusion models in fixed bit-widths, e.g., 4 bits on weights and 8 bits on activation, is shown effective in accelerating sampling time while maintaining the image quality. Motivated by the observation that the cross-layer dependency of DMs vary across layers and sampling steps, we propose a mixed precision quantization scheme, MPQ-Diff, which allocates different bit-width to the weights and activation of the layers. We"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.00144","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/2412.00144/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":"2412.00144","created_at":"2026-07-05T09:42:36.874131+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.00144v1","created_at":"2026-07-05T09:42:36.874131+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.00144","created_at":"2026-07-05T09:42:36.874131+00:00"},{"alias_kind":"pith_short_12","alias_value":"BUJ6OVNRN6SX","created_at":"2026-07-05T09:42:36.874131+00:00"},{"alias_kind":"pith_short_16","alias_value":"BUJ6OVNRN6SXQ4TH","created_at":"2026-07-05T09:42:36.874131+00:00"},{"alias_kind":"pith_short_8","alias_value":"BUJ6OVNR","created_at":"2026-07-05T09:42:36.874131+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.03057","citing_title":"TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BUJ6OVNRN6SXQ4THSPPMW3OWC2","json":"https://pith.science/pith/BUJ6OVNRN6SXQ4THSPPMW3OWC2.json","graph_json":"https://pith.science/api/pith-number/BUJ6OVNRN6SXQ4THSPPMW3OWC2/graph.json","events_json":"https://pith.science/api/pith-number/BUJ6OVNRN6SXQ4THSPPMW3OWC2/events.json","paper":"https://pith.science/paper/BUJ6OVNR"},"agent_actions":{"view_html":"https://pith.science/pith/BUJ6OVNRN6SXQ4THSPPMW3OWC2","download_json":"https://pith.science/pith/BUJ6OVNRN6SXQ4THSPPMW3OWC2.json","view_paper":"https://pith.science/paper/BUJ6OVNR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.00144&json=true","fetch_graph":"https://pith.science/api/pith-number/BUJ6OVNRN6SXQ4THSPPMW3OWC2/graph.json","fetch_events":"https://pith.science/api/pith-number/BUJ6OVNRN6SXQ4THSPPMW3OWC2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BUJ6OVNRN6SXQ4THSPPMW3OWC2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BUJ6OVNRN6SXQ4THSPPMW3OWC2/action/storage_attestation","attest_author":"https://pith.science/pith/BUJ6OVNRN6SXQ4THSPPMW3OWC2/action/author_attestation","sign_citation":"https://pith.science/pith/BUJ6OVNRN6SXQ4THSPPMW3OWC2/action/citation_signature","submit_replication":"https://pith.science/pith/BUJ6OVNRN6SXQ4THSPPMW3OWC2/action/replication_record"}},"created_at":"2026-07-05T09:42:36.874131+00:00","updated_at":"2026-07-05T09:42:36.874131+00:00"}