{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:MOZIAAFRNC6UMADKS4UDCGKP4B","short_pith_number":"pith:MOZIAAFR","canonical_record":{"source":{"id":"2405.14822","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-23T17:39:09Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"30ed3ee902ee110af5630500a416ad5b4d48de37e2d679e4e8ee72bcc6134755","abstract_canon_sha256":"882216beee658c9d2a4042e5ea1fb698a75112b08d225cdcebdea7792585dd5b"},"schema_version":"1.0"},"canonical_sha256":"63b28000b168bd46006a972831194fe04e81a5eff2ea42f4d1626e6a45a22f43","source":{"kind":"arxiv","id":"2405.14822","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.14822","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"arxiv_version","alias_value":"2405.14822v2","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.14822","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"pith_short_12","alias_value":"MOZIAAFRNC6U","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"pith_short_16","alias_value":"MOZIAAFRNC6UMADK","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"pith_short_8","alias_value":"MOZIAAFR","created_at":"2026-07-05T09:27:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:MOZIAAFRNC6UMADKS4UDCGKP4B","target":"record","payload":{"canonical_record":{"source":{"id":"2405.14822","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-23T17:39:09Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"30ed3ee902ee110af5630500a416ad5b4d48de37e2d679e4e8ee72bcc6134755","abstract_canon_sha256":"882216beee658c9d2a4042e5ea1fb698a75112b08d225cdcebdea7792585dd5b"},"schema_version":"1.0"},"canonical_sha256":"63b28000b168bd46006a972831194fe04e81a5eff2ea42f4d1626e6a45a22f43","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:27:54.462703Z","signature_b64":"SAyIvyuaNPKllPj16oLfKEe2ejMh8eLrGeSsKemf8LT3pEZ4wS4yd8v066rDNhgXNy7vPVUaZWxNay+un8teDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63b28000b168bd46006a972831194fe04e81a5eff2ea42f4d1626e6a45a22f43","last_reissued_at":"2026-07-05T09:27:54.462177Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:27:54.462177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.14822","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":"2026-07-05T09:27:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gqlAZHykxbiF9eVrpr5eJxWSa6iCPcUN+IztWD/Y+ogVH0FYxlHBbfByXQgnnrZnXOC4Z2c4nby4Ixzvruk6Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T07:34:13.904806Z"},"content_sha256":"7b1aefc74b7c4174d2f57d988d267a29e493ff75c2c731bca6db2dc7b4c43c3d","schema_version":"1.0","event_id":"sha256:7b1aefc74b7c4174d2f57d988d267a29e493ff75c2c731bca6db2dc7b4c43c3d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:MOZIAAFRNC6UMADKS4UDCGKP4B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Chieh-Hsin Lai, Dongjun Kim, Naoki Murata, Stefano Ermon, Toshimitsu Uesaka, Wei-Hsiang Liao, Yuhta Takida, Yuki Mitsufuji","submitted_at":"2024-05-23T17:39:09Z","abstract_excerpt":"The diffusion model performs remarkable in generating high-dimensional content but is computationally intensive, especially during training. We propose Progressive Growing of Diffusion Autoencoder (PaGoDA), a novel pipeline that reduces the training costs through three stages: training diffusion on downsampled data, distilling the pretrained diffusion, and progressive super-resolution. With the proposed pipeline, PaGoDA achieves a $64\\times$ reduced cost in training its diffusion model on 8x downsampled data; while at the inference, with the single-step, it performs state-of-the-art on ImageNe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.14822","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/2405.14822/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":"2026-07-05T09:27:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JMRhS4xnRfg63uvplthT32JLndpPlj2pS3gPbtbBeCuAyWVQbJ2W4/LvqDSxfTTs/5ybHF3x9/+ifv405YevAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T07:34:13.905188Z"},"content_sha256":"38cdfa430747ee9e106d6c235733b5be4fd7e02118af58b5c970d2323c042ef3","schema_version":"1.0","event_id":"sha256:38cdfa430747ee9e106d6c235733b5be4fd7e02118af58b5c970d2323c042ef3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MOZIAAFRNC6UMADKS4UDCGKP4B/bundle.json","state_url":"https://pith.science/pith/MOZIAAFRNC6UMADKS4UDCGKP4B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MOZIAAFRNC6UMADKS4UDCGKP4B/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":"2026-07-06T07:34:13Z","links":{"resolver":"https://pith.science/pith/MOZIAAFRNC6UMADKS4UDCGKP4B","bundle":"https://pith.science/pith/MOZIAAFRNC6UMADKS4UDCGKP4B/bundle.json","state":"https://pith.science/pith/MOZIAAFRNC6UMADKS4UDCGKP4B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MOZIAAFRNC6UMADKS4UDCGKP4B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MOZIAAFRNC6UMADKS4UDCGKP4B","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":"882216beee658c9d2a4042e5ea1fb698a75112b08d225cdcebdea7792585dd5b","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-23T17:39:09Z","title_canon_sha256":"30ed3ee902ee110af5630500a416ad5b4d48de37e2d679e4e8ee72bcc6134755"},"schema_version":"1.0","source":{"id":"2405.14822","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.14822","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"arxiv_version","alias_value":"2405.14822v2","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.14822","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"pith_short_12","alias_value":"MOZIAAFRNC6U","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"pith_short_16","alias_value":"MOZIAAFRNC6UMADK","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"pith_short_8","alias_value":"MOZIAAFR","created_at":"2026-07-05T09:27:54Z"}],"graph_snapshots":[{"event_id":"sha256:38cdfa430747ee9e106d6c235733b5be4fd7e02118af58b5c970d2323c042ef3","target":"graph","created_at":"2026-07-05T09:27:54Z","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/2405.14822/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The diffusion model performs remarkable in generating high-dimensional content but is computationally intensive, especially during training. We propose Progressive Growing of Diffusion Autoencoder (PaGoDA), a novel pipeline that reduces the training costs through three stages: training diffusion on downsampled data, distilling the pretrained diffusion, and progressive super-resolution. With the proposed pipeline, PaGoDA achieves a $64\\times$ reduced cost in training its diffusion model on 8x downsampled data; while at the inference, with the single-step, it performs state-of-the-art on ImageNe","authors_text":"Chieh-Hsin Lai, Dongjun Kim, Naoki Murata, Stefano Ermon, Toshimitsu Uesaka, Wei-Hsiang Liao, Yuhta Takida, Yuki Mitsufuji","cross_cats":["cs.AI","cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-23T17:39:09Z","title":"PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.14822","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":"sha256:7b1aefc74b7c4174d2f57d988d267a29e493ff75c2c731bca6db2dc7b4c43c3d","target":"record","created_at":"2026-07-05T09:27:54Z","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":"882216beee658c9d2a4042e5ea1fb698a75112b08d225cdcebdea7792585dd5b","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-23T17:39:09Z","title_canon_sha256":"30ed3ee902ee110af5630500a416ad5b4d48de37e2d679e4e8ee72bcc6134755"},"schema_version":"1.0","source":{"id":"2405.14822","kind":"arxiv","version":2}},"canonical_sha256":"63b28000b168bd46006a972831194fe04e81a5eff2ea42f4d1626e6a45a22f43","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"63b28000b168bd46006a972831194fe04e81a5eff2ea42f4d1626e6a45a22f43","first_computed_at":"2026-07-05T09:27:54.462177Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:27:54.462177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SAyIvyuaNPKllPj16oLfKEe2ejMh8eLrGeSsKemf8LT3pEZ4wS4yd8v066rDNhgXNy7vPVUaZWxNay+un8teDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:27:54.462703Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.14822","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7b1aefc74b7c4174d2f57d988d267a29e493ff75c2c731bca6db2dc7b4c43c3d","sha256:38cdfa430747ee9e106d6c235733b5be4fd7e02118af58b5c970d2323c042ef3"],"state_sha256":"46c4c7b7b2bc4f346e44ff6ba8fdd7ced1fc04cb22a0f094e81a91b7eea1e870"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GY/IYAk03t0SKbYQumZRasRpS5MVBFhzpwOqQjGlbNLOYzTmYxlb3+Hv/UgwqvPjSM5dhv0uX3hxACKT4GjiBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T07:34:13.907176Z","bundle_sha256":"1ecbb775e136b64236e65a2ec7280b29b19fcbe45858d27079f8ac85ce007871"}}