{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QKRTTKWURHHFHRDFIVO3JBJZIJ","short_pith_number":"pith:QKRTTKWU","schema_version":"1.0","canonical_sha256":"82a339aad489ce53c465455db485394279064df8f30751e4d03b498a431ee28b","source":{"kind":"arxiv","id":"2607.05711","version":1},"attestation_state":"computed","paper":{"title":"FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Bowen Xue, Haocheng Xi, Jun-Yan Zhu, Lvmin Zhang, Maneesh Agrawala, Muyang Li, Song Han, Xingyang Li, Yujun Lin, Zhekai Zhang, Zihan Min","submitted_at":"2026-07-07T00:34:30Z","abstract_excerpt":"Diffusion models have become a dominant paradigm for high-quality generative modeling, while post-training is essential for adapting them to diverse downstream applications. However, post-training of large diffusion models is still challenging due to the prohibitive memory footprints and slow training speed, which existing parameter-efficient fine-tuning methods only partially address. To overcome these limitations, we propose FourTune, an efficient post-training framework for diffusion models based on an end-to-end W4A4G4 paradigm. FourTune introduces a triple-branch hybrid pipeline that augm"},"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":"2607.05711","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-07T00:34:30Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"d0219e28f7ee1612d0384fe31a226aa0d4cc84503fd84f87b2297836fc8dafe4","abstract_canon_sha256":"c1a63285e1b2def489aa39ca5b0b29b0f4bfdc8a8890c8c82a976554f3d7db3f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-08T01:18:42.171501Z","signature_b64":"wEkBRJH6aL1hBtroXqbVglUt8tqh9hLNx7K7byeAyFStcNtTxPzIo5DQu1kj64vKpsv1px5FT5M1WtVvjC+kCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82a339aad489ce53c465455db485394279064df8f30751e4d03b498a431ee28b","last_reissued_at":"2026-07-08T01:18:42.171027Z","signature_status":"signed_v1","first_computed_at":"2026-07-08T01:18:42.171027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Bowen Xue, Haocheng Xi, Jun-Yan Zhu, Lvmin Zhang, Maneesh Agrawala, Muyang Li, Song Han, Xingyang Li, Yujun Lin, Zhekai Zhang, Zihan Min","submitted_at":"2026-07-07T00:34:30Z","abstract_excerpt":"Diffusion models have become a dominant paradigm for high-quality generative modeling, while post-training is essential for adapting them to diverse downstream applications. However, post-training of large diffusion models is still challenging due to the prohibitive memory footprints and slow training speed, which existing parameter-efficient fine-tuning methods only partially address. To overcome these limitations, we propose FourTune, an efficient post-training framework for diffusion models based on an end-to-end W4A4G4 paradigm. FourTune introduces a triple-branch hybrid pipeline that augm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.05711","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/2607.05711/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":"2607.05711","created_at":"2026-07-08T01:18:42.171085+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.05711v1","created_at":"2026-07-08T01:18:42.171085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.05711","created_at":"2026-07-08T01:18:42.171085+00:00"},{"alias_kind":"pith_short_12","alias_value":"QKRTTKWURHHF","created_at":"2026-07-08T01:18:42.171085+00:00"},{"alias_kind":"pith_short_16","alias_value":"QKRTTKWURHHFHRDF","created_at":"2026-07-08T01:18:42.171085+00:00"},{"alias_kind":"pith_short_8","alias_value":"QKRTTKWU","created_at":"2026-07-08T01:18:42.171085+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/QKRTTKWURHHFHRDFIVO3JBJZIJ","json":"https://pith.science/pith/QKRTTKWURHHFHRDFIVO3JBJZIJ.json","graph_json":"https://pith.science/api/pith-number/QKRTTKWURHHFHRDFIVO3JBJZIJ/graph.json","events_json":"https://pith.science/api/pith-number/QKRTTKWURHHFHRDFIVO3JBJZIJ/events.json","paper":"https://pith.science/paper/QKRTTKWU"},"agent_actions":{"view_html":"https://pith.science/pith/QKRTTKWURHHFHRDFIVO3JBJZIJ","download_json":"https://pith.science/pith/QKRTTKWURHHFHRDFIVO3JBJZIJ.json","view_paper":"https://pith.science/paper/QKRTTKWU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.05711&json=true","fetch_graph":"https://pith.science/api/pith-number/QKRTTKWURHHFHRDFIVO3JBJZIJ/graph.json","fetch_events":"https://pith.science/api/pith-number/QKRTTKWURHHFHRDFIVO3JBJZIJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QKRTTKWURHHFHRDFIVO3JBJZIJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QKRTTKWURHHFHRDFIVO3JBJZIJ/action/storage_attestation","attest_author":"https://pith.science/pith/QKRTTKWURHHFHRDFIVO3JBJZIJ/action/author_attestation","sign_citation":"https://pith.science/pith/QKRTTKWURHHFHRDFIVO3JBJZIJ/action/citation_signature","submit_replication":"https://pith.science/pith/QKRTTKWURHHFHRDFIVO3JBJZIJ/action/replication_record"}},"created_at":"2026-07-08T01:18:42.171085+00:00","updated_at":"2026-07-08T01:18:42.171085+00:00"}