{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:GX5GXJXP7D7AF45W7ZHEN5JZ5T","short_pith_number":"pith:GX5GXJXP","canonical_record":{"source":{"id":"2503.06930","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-10T05:21:04Z","cross_cats_sorted":[],"title_canon_sha256":"f7c80fced2696698a4f6d4fde99d6aa0428d680a8b1074b0369a9a8dbc0a5fec","abstract_canon_sha256":"ff74c99ba00f2a091404ee735bc065ed373bc1f20ac18efca7007fe032bc1529"},"schema_version":"1.0"},"canonical_sha256":"35fa6ba6eff8fe02f3b6fe4e46f539ecdb38066c6342aa47e77ca949ac128ddb","source":{"kind":"arxiv","id":"2503.06930","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.06930","created_at":"2026-07-05T10:41:21Z"},{"alias_kind":"arxiv_version","alias_value":"2503.06930v2","created_at":"2026-07-05T10:41:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06930","created_at":"2026-07-05T10:41:21Z"},{"alias_kind":"pith_short_12","alias_value":"GX5GXJXP7D7A","created_at":"2026-07-05T10:41:21Z"},{"alias_kind":"pith_short_16","alias_value":"GX5GXJXP7D7AF45W","created_at":"2026-07-05T10:41:21Z"},{"alias_kind":"pith_short_8","alias_value":"GX5GXJXP","created_at":"2026-07-05T10:41:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:GX5GXJXP7D7AF45W7ZHEN5JZ5T","target":"record","payload":{"canonical_record":{"source":{"id":"2503.06930","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-10T05:21:04Z","cross_cats_sorted":[],"title_canon_sha256":"f7c80fced2696698a4f6d4fde99d6aa0428d680a8b1074b0369a9a8dbc0a5fec","abstract_canon_sha256":"ff74c99ba00f2a091404ee735bc065ed373bc1f20ac18efca7007fe032bc1529"},"schema_version":"1.0"},"canonical_sha256":"35fa6ba6eff8fe02f3b6fe4e46f539ecdb38066c6342aa47e77ca949ac128ddb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:41:21.364171Z","signature_b64":"Le3VRdEguCGY6JcF4/Ss+u3UNpYG+pZjbvzfezBGU4tHVcMqaiwzaPLMc6AxJldYyG3QAXhboqriJAgSvGXsBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35fa6ba6eff8fe02f3b6fe4e46f539ecdb38066c6342aa47e77ca949ac128ddb","last_reissued_at":"2026-07-05T10:41:21.363591Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:41:21.363591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.06930","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:41:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ORVuraAjffVGLNxdfNDeTU0d5dQdqpCJU8XAywvKmUAwEwo8Le95u+rn7tomPUhj3GtgmtYS+b9ZTaoIP/fHAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T12:49:19.477509Z"},"content_sha256":"527992a04ff79be4000bac1fca1ec9f3163e5e275d5f5bfd61523d0e389a2ea7","schema_version":"1.0","event_id":"sha256:527992a04ff79be4000bac1fca1ec9f3163e5e275d5f5bfd61523d0e389a2ea7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:GX5GXJXP7D7AF45W7ZHEN5JZ5T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Xu, Jing Han, Kai Han, Ning Ding, Yehui Tang, Yuchuan Tian","submitted_at":"2025-03-10T05:21:04Z","abstract_excerpt":"Diffusion Transformer (DiT) has now become the preferred choice for building image generation models due to its great generation capability. Unlike previous convolution-based UNet models, DiT is purely composed of a stack of transformer blocks, which renders DiT excellent in scalability like large language models. However, the growing model size and multi-step sampling paradigm bring about considerable pressure on deployment and inference. In this work, we propose a post-training quantization framework tailored for Diffusion Transforms to tackle these challenges. We firstly locate that the qua"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06930","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/2503.06930/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:41:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k2Q2tBReIi4OdWlu+Mg/960josTbcQYAv9KPa1ys9Sb70UblVtjB+mMyyypTylCCz/MKJB+ErrT89LJdSmI4Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T12:49:19.478508Z"},"content_sha256":"232c649faffb125be8c486a13bc6d758f54fc76f19bfa6a23cd458d7e9632d4a","schema_version":"1.0","event_id":"sha256:232c649faffb125be8c486a13bc6d758f54fc76f19bfa6a23cd458d7e9632d4a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GX5GXJXP7D7AF45W7ZHEN5JZ5T/bundle.json","state_url":"https://pith.science/pith/GX5GXJXP7D7AF45W7ZHEN5JZ5T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GX5GXJXP7D7AF45W7ZHEN5JZ5T/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T12:49:19Z","links":{"resolver":"https://pith.science/pith/GX5GXJXP7D7AF45W7ZHEN5JZ5T","bundle":"https://pith.science/pith/GX5GXJXP7D7AF45W7ZHEN5JZ5T/bundle.json","state":"https://pith.science/pith/GX5GXJXP7D7AF45W7ZHEN5JZ5T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GX5GXJXP7D7AF45W7ZHEN5JZ5T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GX5GXJXP7D7AF45W7ZHEN5JZ5T","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ff74c99ba00f2a091404ee735bc065ed373bc1f20ac18efca7007fe032bc1529","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-10T05:21:04Z","title_canon_sha256":"f7c80fced2696698a4f6d4fde99d6aa0428d680a8b1074b0369a9a8dbc0a5fec"},"schema_version":"1.0","source":{"id":"2503.06930","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.06930","created_at":"2026-07-05T10:41:21Z"},{"alias_kind":"arxiv_version","alias_value":"2503.06930v2","created_at":"2026-07-05T10:41:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06930","created_at":"2026-07-05T10:41:21Z"},{"alias_kind":"pith_short_12","alias_value":"GX5GXJXP7D7A","created_at":"2026-07-05T10:41:21Z"},{"alias_kind":"pith_short_16","alias_value":"GX5GXJXP7D7AF45W","created_at":"2026-07-05T10:41:21Z"},{"alias_kind":"pith_short_8","alias_value":"GX5GXJXP","created_at":"2026-07-05T10:41:21Z"}],"graph_snapshots":[{"event_id":"sha256:232c649faffb125be8c486a13bc6d758f54fc76f19bfa6a23cd458d7e9632d4a","target":"graph","created_at":"2026-07-05T10:41:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.06930/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion Transformer (DiT) has now become the preferred choice for building image generation models due to its great generation capability. Unlike previous convolution-based UNet models, DiT is purely composed of a stack of transformer blocks, which renders DiT excellent in scalability like large language models. However, the growing model size and multi-step sampling paradigm bring about considerable pressure on deployment and inference. In this work, we propose a post-training quantization framework tailored for Diffusion Transforms to tackle these challenges. We firstly locate that the qua","authors_text":"Chao Xu, Jing Han, Kai Han, Ning Ding, Yehui Tang, Yuchuan Tian","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-10T05:21:04Z","title":"Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06930","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:527992a04ff79be4000bac1fca1ec9f3163e5e275d5f5bfd61523d0e389a2ea7","target":"record","created_at":"2026-07-05T10:41:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ff74c99ba00f2a091404ee735bc065ed373bc1f20ac18efca7007fe032bc1529","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-10T05:21:04Z","title_canon_sha256":"f7c80fced2696698a4f6d4fde99d6aa0428d680a8b1074b0369a9a8dbc0a5fec"},"schema_version":"1.0","source":{"id":"2503.06930","kind":"arxiv","version":2}},"canonical_sha256":"35fa6ba6eff8fe02f3b6fe4e46f539ecdb38066c6342aa47e77ca949ac128ddb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35fa6ba6eff8fe02f3b6fe4e46f539ecdb38066c6342aa47e77ca949ac128ddb","first_computed_at":"2026-07-05T10:41:21.363591Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:41:21.363591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Le3VRdEguCGY6JcF4/Ss+u3UNpYG+pZjbvzfezBGU4tHVcMqaiwzaPLMc6AxJldYyG3QAXhboqriJAgSvGXsBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:41:21.364171Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.06930","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:527992a04ff79be4000bac1fca1ec9f3163e5e275d5f5bfd61523d0e389a2ea7","sha256:232c649faffb125be8c486a13bc6d758f54fc76f19bfa6a23cd458d7e9632d4a"],"state_sha256":"b5bdd8a6777e95173775ef7a8bae3f8c65d8aaed93bc9d2fa10cddb1fc26b5d8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QcHJBf8MdvEOOSC4pKwY2iUj++9RCktv29SpJ04argbmozBpzGzphJGAZGGLLaJPvZ8UkpSA6sbBDN86mUsbAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T12:49:19.485457Z","bundle_sha256":"6c345aae37ec7e285dd66e5ae77b17ed9a40bdc457d9d675a4207c52563f35fa"}}