{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:FTW3KB6HM4DPI5AMNGPJWR5VKO","short_pith_number":"pith:FTW3KB6H","canonical_record":{"source":{"id":"2312.14216","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-21T12:11:00Z","cross_cats_sorted":[],"title_canon_sha256":"a8ab8cfe6b1fc0ae8be98145dbac0f65b7e2ced0fa6fc302ee266093d8d7e7c2","abstract_canon_sha256":"47cb280a6b21930f2fe10d55ea8b2913f48cb6f3797ac0b645385cea83d5c5f1"},"schema_version":"1.0"},"canonical_sha256":"2cedb507c76706f4740c699e9b47b553956fc9ba46739bba3e8ffe417ff03249","source":{"kind":"arxiv","id":"2312.14216","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.14216","created_at":"2026-07-05T10:51:19Z"},{"alias_kind":"arxiv_version","alias_value":"2312.14216v2","created_at":"2026-07-05T10:51:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.14216","created_at":"2026-07-05T10:51:19Z"},{"alias_kind":"pith_short_12","alias_value":"FTW3KB6HM4DP","created_at":"2026-07-05T10:51:19Z"},{"alias_kind":"pith_short_16","alias_value":"FTW3KB6HM4DPI5AM","created_at":"2026-07-05T10:51:19Z"},{"alias_kind":"pith_short_8","alias_value":"FTW3KB6H","created_at":"2026-07-05T10:51:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:FTW3KB6HM4DPI5AMNGPJWR5VKO","target":"record","payload":{"canonical_record":{"source":{"id":"2312.14216","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-21T12:11:00Z","cross_cats_sorted":[],"title_canon_sha256":"a8ab8cfe6b1fc0ae8be98145dbac0f65b7e2ced0fa6fc302ee266093d8d7e7c2","abstract_canon_sha256":"47cb280a6b21930f2fe10d55ea8b2913f48cb6f3797ac0b645385cea83d5c5f1"},"schema_version":"1.0"},"canonical_sha256":"2cedb507c76706f4740c699e9b47b553956fc9ba46739bba3e8ffe417ff03249","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:19.819791Z","signature_b64":"6n+te/45mDTIAxpvv5Wjq0OwAF7LQtPbJnsheftqriLbh70+h2MWl+SZ7JQfBWjx0A1oUKv0mB5irpWaprPoCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cedb507c76706f4740c699e9b47b553956fc9ba46739bba3e8ffe417ff03249","last_reissued_at":"2026-07-05T10:51:19.819293Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:19.819293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.14216","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":"2026-07-05T10:51:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ugc9KUAXD7gqNYaxc9dltvZpBO8pPS1pnXHZpskYkapVnrG5SgZkXsOWb3BGcv7KzWxXJqFViBpN0craKFrMCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:22:00.737900Z"},"content_sha256":"50b38dd588ead94f3f3cbc28ae05312af7433afd066f6d700c0fe285eb49d7a6","schema_version":"1.0","event_id":"sha256:50b38dd588ead94f3f3cbc28ae05312af7433afd066f6d700c0fe285eb49d7a6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:FTW3KB6HM4DPI5AMNGPJWR5VKO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DreamDistribution: Learning Prompt Distribution for Diverse In-distribution Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Brian Nlong Zhao, Dongsheng Li, Jiashu Xu, Laurent Itti, Vibhav Vineet, Xinyang Jiang, Yifan Yang, Yuhang Xiao, Yunhao Ge","submitted_at":"2023-12-21T12:11:00Z","abstract_excerpt":"The popularization of Text-to-Image (T2I) diffusion models enables the generation of high-quality images from text descriptions. However, generating diverse customized images with reference visual attributes remains challenging. This work focuses on personalizing T2I diffusion models at a more abstract concept or category level, adapting commonalities from a set of reference images while creating new instances with sufficient variations. We introduce a solution that allows a pretrained T2I diffusion model to learn a set of soft prompts, enabling the generation of novel images by sampling promp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.14216","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/2312.14216/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":"2026-07-05T10:51:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j+QXgJLw/7CGmGiHA6+FhpQB07HTHWEzsVagSjmZp1sO/JxgoPmZvYGy8wJaqmjFlrPPQX0f9kZ3Iu17fNowAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:22:00.738848Z"},"content_sha256":"c8c1ead785d5470607f53eb54b536bdfdfccfb8f16d1b3e570e5c41571fb3692","schema_version":"1.0","event_id":"sha256:c8c1ead785d5470607f53eb54b536bdfdfccfb8f16d1b3e570e5c41571fb3692"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FTW3KB6HM4DPI5AMNGPJWR5VKO/bundle.json","state_url":"https://pith.science/pith/FTW3KB6HM4DPI5AMNGPJWR5VKO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FTW3KB6HM4DPI5AMNGPJWR5VKO/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":"2026-08-16T14:22:00Z","links":{"resolver":"https://pith.science/pith/FTW3KB6HM4DPI5AMNGPJWR5VKO","bundle":"https://pith.science/pith/FTW3KB6HM4DPI5AMNGPJWR5VKO/bundle.json","state":"https://pith.science/pith/FTW3KB6HM4DPI5AMNGPJWR5VKO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FTW3KB6HM4DPI5AMNGPJWR5VKO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:FTW3KB6HM4DPI5AMNGPJWR5VKO","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":"47cb280a6b21930f2fe10d55ea8b2913f48cb6f3797ac0b645385cea83d5c5f1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-21T12:11:00Z","title_canon_sha256":"a8ab8cfe6b1fc0ae8be98145dbac0f65b7e2ced0fa6fc302ee266093d8d7e7c2"},"schema_version":"1.0","source":{"id":"2312.14216","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.14216","created_at":"2026-07-05T10:51:19Z"},{"alias_kind":"arxiv_version","alias_value":"2312.14216v2","created_at":"2026-07-05T10:51:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.14216","created_at":"2026-07-05T10:51:19Z"},{"alias_kind":"pith_short_12","alias_value":"FTW3KB6HM4DP","created_at":"2026-07-05T10:51:19Z"},{"alias_kind":"pith_short_16","alias_value":"FTW3KB6HM4DPI5AM","created_at":"2026-07-05T10:51:19Z"},{"alias_kind":"pith_short_8","alias_value":"FTW3KB6H","created_at":"2026-07-05T10:51:19Z"}],"graph_snapshots":[{"event_id":"sha256:c8c1ead785d5470607f53eb54b536bdfdfccfb8f16d1b3e570e5c41571fb3692","target":"graph","created_at":"2026-07-05T10:51:19Z","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/2312.14216/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The popularization of Text-to-Image (T2I) diffusion models enables the generation of high-quality images from text descriptions. However, generating diverse customized images with reference visual attributes remains challenging. This work focuses on personalizing T2I diffusion models at a more abstract concept or category level, adapting commonalities from a set of reference images while creating new instances with sufficient variations. We introduce a solution that allows a pretrained T2I diffusion model to learn a set of soft prompts, enabling the generation of novel images by sampling promp","authors_text":"Brian Nlong Zhao, Dongsheng Li, Jiashu Xu, Laurent Itti, Vibhav Vineet, Xinyang Jiang, Yifan Yang, Yuhang Xiao, Yunhao Ge","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-21T12:11:00Z","title":"DreamDistribution: Learning Prompt Distribution for Diverse In-distribution Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.14216","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":"sha256:50b38dd588ead94f3f3cbc28ae05312af7433afd066f6d700c0fe285eb49d7a6","target":"record","created_at":"2026-07-05T10:51:19Z","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":"47cb280a6b21930f2fe10d55ea8b2913f48cb6f3797ac0b645385cea83d5c5f1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-21T12:11:00Z","title_canon_sha256":"a8ab8cfe6b1fc0ae8be98145dbac0f65b7e2ced0fa6fc302ee266093d8d7e7c2"},"schema_version":"1.0","source":{"id":"2312.14216","kind":"arxiv","version":2}},"canonical_sha256":"2cedb507c76706f4740c699e9b47b553956fc9ba46739bba3e8ffe417ff03249","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2cedb507c76706f4740c699e9b47b553956fc9ba46739bba3e8ffe417ff03249","first_computed_at":"2026-07-05T10:51:19.819293Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:51:19.819293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6n+te/45mDTIAxpvv5Wjq0OwAF7LQtPbJnsheftqriLbh70+h2MWl+SZ7JQfBWjx0A1oUKv0mB5irpWaprPoCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:51:19.819791Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.14216","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:50b38dd588ead94f3f3cbc28ae05312af7433afd066f6d700c0fe285eb49d7a6","sha256:c8c1ead785d5470607f53eb54b536bdfdfccfb8f16d1b3e570e5c41571fb3692"],"state_sha256":"3b2c28bbbd505689e25f9c52e55fb124a3ae0e572b1d77a9203340f0b77f253f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vErCWOUtDx7sKcR7No5YOtHsqRcHUxWZYG3zT+hFVRlMGlhmjgzYEsYseZZSRfmzTvPC72S9U0i6GVGUd/IjAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T14:22:00.745328Z","bundle_sha256":"49d156fe0aa82ee7cc9d07de0ed0d105fb026baefe6bdb07b29cfeb49a5193b9"}}