{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MP5OMYV3464EEANHAWRS7GWB5A","short_pith_number":"pith:MP5OMYV3","schema_version":"1.0","canonical_sha256":"63fae662bbe7b84201a705a32f9ac1e8380d1847eb31302133cf8333de860793","source":{"kind":"arxiv","id":"2503.06564","version":1},"attestation_state":"computed","paper":{"title":"TR-DQ: Time-Rotation Diffusion Quantization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Deyang Lin, Fanhu Zeng, Hao Tang, Haotong Qin, Haozhe Wang, Jingcai Guo, Minxi Yan, Muyang Zhang, Siyu Chen, Yan Wang, Yihua Shao, Yuxuan Fan, Ziyang Yan","submitted_at":"2025-03-09T11:37:11Z","abstract_excerpt":"Diffusion models have been widely adopted in image and video generation. However, their complex network architecture leads to high inference overhead for its generation process. Existing diffusion quantization methods primarily focus on the quantization of the model structure while ignoring the impact of time-steps variation during sampling. At the same time, most current approaches fail to account for significant activations that cannot be eliminated, resulting in substantial performance degradation after quantization. To address these issues, we propose Time-Rotation Diffusion Quantization ("},"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":"2503.06564","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-03-09T11:37:11Z","cross_cats_sorted":[],"title_canon_sha256":"de3d790091be8348f868e1ae5a920c98a65b26e66f3e05f87d94d66084f0b017","abstract_canon_sha256":"38c6804cba0845f4451a0b8c17573441a512f3313500ca2b44a9fd55d51cf059"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:27:37.250359Z","signature_b64":"3klhjUwX3oX/bOQHQoIv3KaHw6R2SiAgxvO5ymIJKfX4JQcWRjhv2R0b5UbjpN0HhNToaNUIGOF0lf8ZPyABCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63fae662bbe7b84201a705a32f9ac1e8380d1847eb31302133cf8333de860793","last_reissued_at":"2026-07-05T10:27:37.249675Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:27:37.249675Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TR-DQ: Time-Rotation Diffusion Quantization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Deyang Lin, Fanhu Zeng, Hao Tang, Haotong Qin, Haozhe Wang, Jingcai Guo, Minxi Yan, Muyang Zhang, Siyu Chen, Yan Wang, Yihua Shao, Yuxuan Fan, Ziyang Yan","submitted_at":"2025-03-09T11:37:11Z","abstract_excerpt":"Diffusion models have been widely adopted in image and video generation. However, their complex network architecture leads to high inference overhead for its generation process. Existing diffusion quantization methods primarily focus on the quantization of the model structure while ignoring the impact of time-steps variation during sampling. At the same time, most current approaches fail to account for significant activations that cannot be eliminated, resulting in substantial performance degradation after quantization. To address these issues, we propose Time-Rotation Diffusion Quantization ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06564","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/2503.06564/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":"2503.06564","created_at":"2026-07-05T10:27:37.249747+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.06564v1","created_at":"2026-07-05T10:27:37.249747+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06564","created_at":"2026-07-05T10:27:37.249747+00:00"},{"alias_kind":"pith_short_12","alias_value":"MP5OMYV3464E","created_at":"2026-07-05T10:27:37.249747+00:00"},{"alias_kind":"pith_short_16","alias_value":"MP5OMYV3464EEANH","created_at":"2026-07-05T10:27:37.249747+00:00"},{"alias_kind":"pith_short_8","alias_value":"MP5OMYV3","created_at":"2026-07-05T10:27:37.249747+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29350","citing_title":"Fast Enough to Act: Spatio-Temporal Visual Token Merging for Low-Latency Robotic VLMs and VLAs","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01725","citing_title":"Motion-Aware Caching for Efficient Autoregressive Video Generation","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01725","citing_title":"Motion-Aware Caching for Efficient Autoregressive Video Generation","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15911","citing_title":"Efficient Video Diffusion Models: Advancements and Challenges","ref_index":115,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MP5OMYV3464EEANHAWRS7GWB5A","json":"https://pith.science/pith/MP5OMYV3464EEANHAWRS7GWB5A.json","graph_json":"https://pith.science/api/pith-number/MP5OMYV3464EEANHAWRS7GWB5A/graph.json","events_json":"https://pith.science/api/pith-number/MP5OMYV3464EEANHAWRS7GWB5A/events.json","paper":"https://pith.science/paper/MP5OMYV3"},"agent_actions":{"view_html":"https://pith.science/pith/MP5OMYV3464EEANHAWRS7GWB5A","download_json":"https://pith.science/pith/MP5OMYV3464EEANHAWRS7GWB5A.json","view_paper":"https://pith.science/paper/MP5OMYV3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.06564&json=true","fetch_graph":"https://pith.science/api/pith-number/MP5OMYV3464EEANHAWRS7GWB5A/graph.json","fetch_events":"https://pith.science/api/pith-number/MP5OMYV3464EEANHAWRS7GWB5A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MP5OMYV3464EEANHAWRS7GWB5A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MP5OMYV3464EEANHAWRS7GWB5A/action/storage_attestation","attest_author":"https://pith.science/pith/MP5OMYV3464EEANHAWRS7GWB5A/action/author_attestation","sign_citation":"https://pith.science/pith/MP5OMYV3464EEANHAWRS7GWB5A/action/citation_signature","submit_replication":"https://pith.science/pith/MP5OMYV3464EEANHAWRS7GWB5A/action/replication_record"}},"created_at":"2026-07-05T10:27:37.249747+00:00","updated_at":"2026-07-05T10:27:37.249747+00:00"}