{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:KP37WB6HNQQ7VSYW2H6FO6QWX4","short_pith_number":"pith:KP37WB6H","canonical_record":{"source":{"id":"2212.07352","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-14T17:26:35Z","cross_cats_sorted":[],"title_canon_sha256":"3ce23689831409bf9c067b6dec7b79091401296335e495a5bbf03937dfa50fe1","abstract_canon_sha256":"82db96669ac8562581e1752b1f86601c3d960064fc848833833ecc6cd2d83290"},"schema_version":"1.0"},"canonical_sha256":"53f7fb07c76c21facb16d1fc577a16bf0140379a047f3d8ae72e3f41f7191fae","source":{"kind":"arxiv","id":"2212.07352","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.07352","created_at":"2026-07-05T05:25:24Z"},{"alias_kind":"arxiv_version","alias_value":"2212.07352v1","created_at":"2026-07-05T05:25:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.07352","created_at":"2026-07-05T05:25:24Z"},{"alias_kind":"pith_short_12","alias_value":"KP37WB6HNQQ7","created_at":"2026-07-05T05:25:24Z"},{"alias_kind":"pith_short_16","alias_value":"KP37WB6HNQQ7VSYW","created_at":"2026-07-05T05:25:24Z"},{"alias_kind":"pith_short_8","alias_value":"KP37WB6H","created_at":"2026-07-05T05:25:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:KP37WB6HNQQ7VSYW2H6FO6QWX4","target":"record","payload":{"canonical_record":{"source":{"id":"2212.07352","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-14T17:26:35Z","cross_cats_sorted":[],"title_canon_sha256":"3ce23689831409bf9c067b6dec7b79091401296335e495a5bbf03937dfa50fe1","abstract_canon_sha256":"82db96669ac8562581e1752b1f86601c3d960064fc848833833ecc6cd2d83290"},"schema_version":"1.0"},"canonical_sha256":"53f7fb07c76c21facb16d1fc577a16bf0140379a047f3d8ae72e3f41f7191fae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:25:24.140371Z","signature_b64":"/9j3ZhWr+Yls2nxUnscEij1/cXb0NHAPl4bqgfSW7YeakyHsaVcjgU1mGwx2GMCrGdOYCGJ1AhGZZiqu+/f2Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53f7fb07c76c21facb16d1fc577a16bf0140379a047f3d8ae72e3f41f7191fae","last_reissued_at":"2026-07-05T05:25:24.139907Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:25:24.139907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.07352","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:25:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iv7+ORFJnl6KyOkLaYowAQsLFelHviMRe5m/JlegG1DoNLvcSY+GmbKqH/4Ic5hu+PmeblhKj0m40eep7fd4DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T07:10:12.494786Z"},"content_sha256":"e7fc13c14538f6c1ebc4aa386760e8531fbec67f216e8ec6ca2dfcb107a48d39","schema_version":"1.0","event_id":"sha256:e7fc13c14538f6c1ebc4aa386760e8531fbec67f216e8ec6ca2dfcb107a48d39"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:KP37WB6HNQQ7VSYW2H6FO6QWX4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kangfu Mei, Nithin Gopalakrishnan Nair, Vishal M. Patel","submitted_at":"2022-12-14T17:26:35Z","abstract_excerpt":"Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentimes their results can be unrealistic with observable color shifts and textures. We believe that this issue results from the divergence between the probabilistic distribution learned by the model and the distribution of natural images. The delicate conditions gradually enlarge the divergence during each sampling timestep. To address this issue, we introduce a new method that brings the predicted samples to the training "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.07352","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/2212.07352/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":"2026-07-05T05:25:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FeypMq2tmM+0gpX5gp1c7A35AR6pF0DMtt+cqVaTKTnXXW0z5n8ZvyIScGUw5zpFqPTvk15U3pY1cRGY3vseAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T07:10:12.495289Z"},"content_sha256":"ed42d7cac7696a4f75a4466d4d2310215aca7f9c629803ec2b3ebe1d3d7c9324","schema_version":"1.0","event_id":"sha256:ed42d7cac7696a4f75a4466d4d2310215aca7f9c629803ec2b3ebe1d3d7c9324"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KP37WB6HNQQ7VSYW2H6FO6QWX4/bundle.json","state_url":"https://pith.science/pith/KP37WB6HNQQ7VSYW2H6FO6QWX4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KP37WB6HNQQ7VSYW2H6FO6QWX4/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":"2026-08-16T07:10:12Z","links":{"resolver":"https://pith.science/pith/KP37WB6HNQQ7VSYW2H6FO6QWX4","bundle":"https://pith.science/pith/KP37WB6HNQQ7VSYW2H6FO6QWX4/bundle.json","state":"https://pith.science/pith/KP37WB6HNQQ7VSYW2H6FO6QWX4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KP37WB6HNQQ7VSYW2H6FO6QWX4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:KP37WB6HNQQ7VSYW2H6FO6QWX4","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":"82db96669ac8562581e1752b1f86601c3d960064fc848833833ecc6cd2d83290","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-14T17:26:35Z","title_canon_sha256":"3ce23689831409bf9c067b6dec7b79091401296335e495a5bbf03937dfa50fe1"},"schema_version":"1.0","source":{"id":"2212.07352","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.07352","created_at":"2026-07-05T05:25:24Z"},{"alias_kind":"arxiv_version","alias_value":"2212.07352v1","created_at":"2026-07-05T05:25:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.07352","created_at":"2026-07-05T05:25:24Z"},{"alias_kind":"pith_short_12","alias_value":"KP37WB6HNQQ7","created_at":"2026-07-05T05:25:24Z"},{"alias_kind":"pith_short_16","alias_value":"KP37WB6HNQQ7VSYW","created_at":"2026-07-05T05:25:24Z"},{"alias_kind":"pith_short_8","alias_value":"KP37WB6H","created_at":"2026-07-05T05:25:24Z"}],"graph_snapshots":[{"event_id":"sha256:ed42d7cac7696a4f75a4466d4d2310215aca7f9c629803ec2b3ebe1d3d7c9324","target":"graph","created_at":"2026-07-05T05:25:24Z","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/2212.07352/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentimes their results can be unrealistic with observable color shifts and textures. We believe that this issue results from the divergence between the probabilistic distribution learned by the model and the distribution of natural images. The delicate conditions gradually enlarge the divergence during each sampling timestep. To address this issue, we introduce a new method that brings the predicted samples to the training ","authors_text":"Kangfu Mei, Nithin Gopalakrishnan Nair, Vishal M. Patel","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-14T17:26:35Z","title":"Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.07352","kind":"arxiv","version":1},"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:e7fc13c14538f6c1ebc4aa386760e8531fbec67f216e8ec6ca2dfcb107a48d39","target":"record","created_at":"2026-07-05T05:25:24Z","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":"82db96669ac8562581e1752b1f86601c3d960064fc848833833ecc6cd2d83290","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-14T17:26:35Z","title_canon_sha256":"3ce23689831409bf9c067b6dec7b79091401296335e495a5bbf03937dfa50fe1"},"schema_version":"1.0","source":{"id":"2212.07352","kind":"arxiv","version":1}},"canonical_sha256":"53f7fb07c76c21facb16d1fc577a16bf0140379a047f3d8ae72e3f41f7191fae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"53f7fb07c76c21facb16d1fc577a16bf0140379a047f3d8ae72e3f41f7191fae","first_computed_at":"2026-07-05T05:25:24.139907Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:25:24.139907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/9j3ZhWr+Yls2nxUnscEij1/cXb0NHAPl4bqgfSW7YeakyHsaVcjgU1mGwx2GMCrGdOYCGJ1AhGZZiqu+/f2Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:25:24.140371Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.07352","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e7fc13c14538f6c1ebc4aa386760e8531fbec67f216e8ec6ca2dfcb107a48d39","sha256:ed42d7cac7696a4f75a4466d4d2310215aca7f9c629803ec2b3ebe1d3d7c9324"],"state_sha256":"e706de93bf6252c0cfa5ee2bf4179e36186f90681136128ab3cf47d45fe2f9d0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JhmAckPJh1giLLzmE55CxetzZkVnzYI1NoXQNOtgEwybFScPRmzUsF/LwNQnf8gkD9PuNgpatbttC94ke0+TCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T07:10:12.500281Z","bundle_sha256":"78bd7ef067f0c731da1715e8fb8dbb02b098884b993ebf5707ef376619dcb8f4"}}