{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ZTP6LTMNHSD5VFIAPDXX7IIMFF","short_pith_number":"pith:ZTP6LTMN","canonical_record":{"source":{"id":"2209.04747","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-09-10T22:00:30Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"0289f2c5519553bb9d954b9365ce07bf35385f7048852f4a8b9fabce42ee7f97","abstract_canon_sha256":"eb9643c24226dd6045bf526cae9ffc7d11153cb201ffe6326a52dc0603f2f5a0"},"schema_version":"1.0"},"canonical_sha256":"ccdfe5cd8d3c87da950078ef7fa10c294f85edd7ca9763e05d13c9bfbe973951","source":{"kind":"arxiv","id":"2209.04747","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.04747","created_at":"2026-07-05T10:01:30Z"},{"alias_kind":"arxiv_version","alias_value":"2209.04747v6","created_at":"2026-07-05T10:01:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.04747","created_at":"2026-07-05T10:01:30Z"},{"alias_kind":"pith_short_12","alias_value":"ZTP6LTMNHSD5","created_at":"2026-07-05T10:01:30Z"},{"alias_kind":"pith_short_16","alias_value":"ZTP6LTMNHSD5VFIA","created_at":"2026-07-05T10:01:30Z"},{"alias_kind":"pith_short_8","alias_value":"ZTP6LTMN","created_at":"2026-07-05T10:01:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ZTP6LTMNHSD5VFIAPDXX7IIMFF","target":"record","payload":{"canonical_record":{"source":{"id":"2209.04747","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-09-10T22:00:30Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"0289f2c5519553bb9d954b9365ce07bf35385f7048852f4a8b9fabce42ee7f97","abstract_canon_sha256":"eb9643c24226dd6045bf526cae9ffc7d11153cb201ffe6326a52dc0603f2f5a0"},"schema_version":"1.0"},"canonical_sha256":"ccdfe5cd8d3c87da950078ef7fa10c294f85edd7ca9763e05d13c9bfbe973951","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:01:30.142048Z","signature_b64":"j/bu8aUh/n6Gsxd/5qULG/zB5mYyNEn9uHkteaiwgwZ6T9zYN3D3p7UWhrYq5twDc8lJEuqMq1/Pa8ZKZLDXCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ccdfe5cd8d3c87da950078ef7fa10c294f85edd7ca9763e05d13c9bfbe973951","last_reissued_at":"2026-07-05T10:01:30.141629Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:01:30.141629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.04747","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:01:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4D9/bf/YgiOT6/XyPfK5fvivD1cENPmJ36JgxgRuDFFekF4BmMQe9HhEEKBDl/nDgiMDGw17+1swyeiN5qKDDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:55:38.615884Z"},"content_sha256":"ce55dd18df1368f359571adc11a8891b46ed0e90b098aa71529216c2d9239ba8","schema_version":"1.0","event_id":"sha256:ce55dd18df1368f359571adc11a8891b46ed0e90b098aa71529216c2d9239ba8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ZTP6LTMNHSD5VFIAPDXX7IIMFF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Diffusion Models in Vision: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Florinel-Alin Croitoru, Mubarak Shah, Radu Tudor Ionescu, Vlad Hondru","submitted_at":"2022-09-10T22:00:30Z","abstract_excerpt":"Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.04747","kind":"arxiv","version":6},"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/2209.04747/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":"2026-07-05T10:01:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c0W3JvzFMj94MbCb6SwODmLVsM+etbADARCdWzlxdRk4lHb3j2BETrSbrPr8KAeCPQeoAmhxWOEpBewcAPO+Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:55:38.616543Z"},"content_sha256":"9b4ff4c49381477ae0c4defd45117dc01117641337cb6244aec1384c0df4bd7c","schema_version":"1.0","event_id":"sha256:9b4ff4c49381477ae0c4defd45117dc01117641337cb6244aec1384c0df4bd7c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZTP6LTMNHSD5VFIAPDXX7IIMFF/bundle.json","state_url":"https://pith.science/pith/ZTP6LTMNHSD5VFIAPDXX7IIMFF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZTP6LTMNHSD5VFIAPDXX7IIMFF/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":"2026-07-07T07:55:38Z","links":{"resolver":"https://pith.science/pith/ZTP6LTMNHSD5VFIAPDXX7IIMFF","bundle":"https://pith.science/pith/ZTP6LTMNHSD5VFIAPDXX7IIMFF/bundle.json","state":"https://pith.science/pith/ZTP6LTMNHSD5VFIAPDXX7IIMFF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZTP6LTMNHSD5VFIAPDXX7IIMFF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ZTP6LTMNHSD5VFIAPDXX7IIMFF","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":"eb9643c24226dd6045bf526cae9ffc7d11153cb201ffe6326a52dc0603f2f5a0","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-09-10T22:00:30Z","title_canon_sha256":"0289f2c5519553bb9d954b9365ce07bf35385f7048852f4a8b9fabce42ee7f97"},"schema_version":"1.0","source":{"id":"2209.04747","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.04747","created_at":"2026-07-05T10:01:30Z"},{"alias_kind":"arxiv_version","alias_value":"2209.04747v6","created_at":"2026-07-05T10:01:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.04747","created_at":"2026-07-05T10:01:30Z"},{"alias_kind":"pith_short_12","alias_value":"ZTP6LTMNHSD5","created_at":"2026-07-05T10:01:30Z"},{"alias_kind":"pith_short_16","alias_value":"ZTP6LTMNHSD5VFIA","created_at":"2026-07-05T10:01:30Z"},{"alias_kind":"pith_short_8","alias_value":"ZTP6LTMN","created_at":"2026-07-05T10:01:30Z"}],"graph_snapshots":[{"event_id":"sha256:9b4ff4c49381477ae0c4defd45117dc01117641337cb6244aec1384c0df4bd7c","target":"graph","created_at":"2026-07-05T10:01:30Z","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/2209.04747/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality ","authors_text":"Florinel-Alin Croitoru, Mubarak Shah, Radu Tudor Ionescu, Vlad Hondru","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-09-10T22:00:30Z","title":"Diffusion Models in Vision: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.04747","kind":"arxiv","version":6},"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:ce55dd18df1368f359571adc11a8891b46ed0e90b098aa71529216c2d9239ba8","target":"record","created_at":"2026-07-05T10:01:30Z","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":"eb9643c24226dd6045bf526cae9ffc7d11153cb201ffe6326a52dc0603f2f5a0","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-09-10T22:00:30Z","title_canon_sha256":"0289f2c5519553bb9d954b9365ce07bf35385f7048852f4a8b9fabce42ee7f97"},"schema_version":"1.0","source":{"id":"2209.04747","kind":"arxiv","version":6}},"canonical_sha256":"ccdfe5cd8d3c87da950078ef7fa10c294f85edd7ca9763e05d13c9bfbe973951","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ccdfe5cd8d3c87da950078ef7fa10c294f85edd7ca9763e05d13c9bfbe973951","first_computed_at":"2026-07-05T10:01:30.141629Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:01:30.141629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j/bu8aUh/n6Gsxd/5qULG/zB5mYyNEn9uHkteaiwgwZ6T9zYN3D3p7UWhrYq5twDc8lJEuqMq1/Pa8ZKZLDXCA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:01:30.142048Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.04747","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ce55dd18df1368f359571adc11a8891b46ed0e90b098aa71529216c2d9239ba8","sha256:9b4ff4c49381477ae0c4defd45117dc01117641337cb6244aec1384c0df4bd7c"],"state_sha256":"3541db72350fdf979319705d5b50a77795bfe335fc027b1be0562c90ef2f6f27"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fkvh0sExJH9BxNHVREw+S2XuHaEAUghEjopgHrXkZ85meFZs/VoW8oUO9iVEAUgmV/awhlzRzpBJY2tChWfwDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T07:55:38.619701Z","bundle_sha256":"99c4a1be901616afe459331d1a08be416264477f31b7eca10b6cfd08f77a2624"}}