{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:PNJX6PJMJ7SDMRR2FTL7DY4KCD","short_pith_number":"pith:PNJX6PJM","canonical_record":{"source":{"id":"2508.03735","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-31T11:24:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2b4efebb81866a145fb4aa9df6260b50e6734f83a58e9b496c7549b44953e383","abstract_canon_sha256":"f25b7a3b71a90e44bd409fe881c0749267eef6857d674c8a78d7c6769ed1703d"},"schema_version":"1.0"},"canonical_sha256":"7b537f3d2c4fe436463a2cd7f1e38a10c4853c5134a322ff83bda7383d5e0758","source":{"kind":"arxiv","id":"2508.03735","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.03735","created_at":"2026-07-05T11:49:14Z"},{"alias_kind":"arxiv_version","alias_value":"2508.03735v1","created_at":"2026-07-05T11:49:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.03735","created_at":"2026-07-05T11:49:14Z"},{"alias_kind":"pith_short_12","alias_value":"PNJX6PJMJ7SD","created_at":"2026-07-05T11:49:14Z"},{"alias_kind":"pith_short_16","alias_value":"PNJX6PJMJ7SDMRR2","created_at":"2026-07-05T11:49:14Z"},{"alias_kind":"pith_short_8","alias_value":"PNJX6PJM","created_at":"2026-07-05T11:49:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:PNJX6PJMJ7SDMRR2FTL7DY4KCD","target":"record","payload":{"canonical_record":{"source":{"id":"2508.03735","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-31T11:24:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2b4efebb81866a145fb4aa9df6260b50e6734f83a58e9b496c7549b44953e383","abstract_canon_sha256":"f25b7a3b71a90e44bd409fe881c0749267eef6857d674c8a78d7c6769ed1703d"},"schema_version":"1.0"},"canonical_sha256":"7b537f3d2c4fe436463a2cd7f1e38a10c4853c5134a322ff83bda7383d5e0758","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:14.089434Z","signature_b64":"L5q3a6z+41++iGcknaske8PsbhEgCM22pXMzqWFyNkCkCrCuWf4qDCzKB3FbP/PC1I3TSfdZJjE/NWsIh/qfBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7b537f3d2c4fe436463a2cd7f1e38a10c4853c5134a322ff83bda7383d5e0758","last_reissued_at":"2026-07-05T11:49:14.088845Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:14.088845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.03735","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":"2026-07-05T11:49:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r1+XsqhtchbV6HzuI7dTW7v1eyuDnFO/JjNNlxPQ+/zHmwMfbvtughgro0c02J4WPyweAxEFdE4wkKnM5NYFAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T18:03:49.778411Z"},"content_sha256":"f7d01d27774d26a7629433a56d230d5dba75fbee8e6af0006df0ba17b6d42417","schema_version":"1.0","event_id":"sha256:f7d01d27774d26a7629433a56d230d5dba75fbee8e6af0006df0ba17b6d42417"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:PNJX6PJMJ7SDMRR2FTL7DY4KCD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"StorySync: Training-Free Subject Consistency in Text-to-Image Generation via Region Harmonization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gopalji Gaur, Mohammadreza Zolfaghari, Thomas Brox","submitted_at":"2025-07-31T11:24:40Z","abstract_excerpt":"Generating a coherent sequence of images that tells a visual story, using text-to-image diffusion models, often faces the critical challenge of maintaining subject consistency across all story scenes. Existing approaches, which typically rely on fine-tuning or retraining models, are computationally expensive, time-consuming, and often interfere with the model's pre-existing capabilities. In this paper, we follow a training-free approach and propose an efficient consistent-subject-generation method. This approach works seamlessly with pre-trained diffusion models by introducing masked cross-ima"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.03735","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/2508.03735/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":"2026-07-05T11:49:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fJHtY7NsI2I3kEAA+TxRxXRadIuxZZDD2gf86tz+8qspEvKc9FlkUuLfyedOlQcGtsNDapDY009Nergyo/9lAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T18:03:49.778930Z"},"content_sha256":"5672892943f126f4dd9f4feade57f2301826b0f4479251100ffd9a73bb3201be","schema_version":"1.0","event_id":"sha256:5672892943f126f4dd9f4feade57f2301826b0f4479251100ffd9a73bb3201be"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PNJX6PJMJ7SDMRR2FTL7DY4KCD/bundle.json","state_url":"https://pith.science/pith/PNJX6PJMJ7SDMRR2FTL7DY4KCD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PNJX6PJMJ7SDMRR2FTL7DY4KCD/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":"2026-08-12T18:03:49Z","links":{"resolver":"https://pith.science/pith/PNJX6PJMJ7SDMRR2FTL7DY4KCD","bundle":"https://pith.science/pith/PNJX6PJMJ7SDMRR2FTL7DY4KCD/bundle.json","state":"https://pith.science/pith/PNJX6PJMJ7SDMRR2FTL7DY4KCD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PNJX6PJMJ7SDMRR2FTL7DY4KCD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:PNJX6PJMJ7SDMRR2FTL7DY4KCD","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":"f25b7a3b71a90e44bd409fe881c0749267eef6857d674c8a78d7c6769ed1703d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-31T11:24:40Z","title_canon_sha256":"2b4efebb81866a145fb4aa9df6260b50e6734f83a58e9b496c7549b44953e383"},"schema_version":"1.0","source":{"id":"2508.03735","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.03735","created_at":"2026-07-05T11:49:14Z"},{"alias_kind":"arxiv_version","alias_value":"2508.03735v1","created_at":"2026-07-05T11:49:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.03735","created_at":"2026-07-05T11:49:14Z"},{"alias_kind":"pith_short_12","alias_value":"PNJX6PJMJ7SD","created_at":"2026-07-05T11:49:14Z"},{"alias_kind":"pith_short_16","alias_value":"PNJX6PJMJ7SDMRR2","created_at":"2026-07-05T11:49:14Z"},{"alias_kind":"pith_short_8","alias_value":"PNJX6PJM","created_at":"2026-07-05T11:49:14Z"}],"graph_snapshots":[{"event_id":"sha256:5672892943f126f4dd9f4feade57f2301826b0f4479251100ffd9a73bb3201be","target":"graph","created_at":"2026-07-05T11:49:14Z","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/2508.03735/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generating a coherent sequence of images that tells a visual story, using text-to-image diffusion models, often faces the critical challenge of maintaining subject consistency across all story scenes. Existing approaches, which typically rely on fine-tuning or retraining models, are computationally expensive, time-consuming, and often interfere with the model's pre-existing capabilities. In this paper, we follow a training-free approach and propose an efficient consistent-subject-generation method. This approach works seamlessly with pre-trained diffusion models by introducing masked cross-ima","authors_text":"Gopalji Gaur, Mohammadreza Zolfaghari, Thomas Brox","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-31T11:24:40Z","title":"StorySync: Training-Free Subject Consistency in Text-to-Image Generation via Region Harmonization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.03735","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:f7d01d27774d26a7629433a56d230d5dba75fbee8e6af0006df0ba17b6d42417","target":"record","created_at":"2026-07-05T11:49:14Z","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":"f25b7a3b71a90e44bd409fe881c0749267eef6857d674c8a78d7c6769ed1703d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-31T11:24:40Z","title_canon_sha256":"2b4efebb81866a145fb4aa9df6260b50e6734f83a58e9b496c7549b44953e383"},"schema_version":"1.0","source":{"id":"2508.03735","kind":"arxiv","version":1}},"canonical_sha256":"7b537f3d2c4fe436463a2cd7f1e38a10c4853c5134a322ff83bda7383d5e0758","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7b537f3d2c4fe436463a2cd7f1e38a10c4853c5134a322ff83bda7383d5e0758","first_computed_at":"2026-07-05T11:49:14.088845Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:49:14.088845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L5q3a6z+41++iGcknaske8PsbhEgCM22pXMzqWFyNkCkCrCuWf4qDCzKB3FbP/PC1I3TSfdZJjE/NWsIh/qfBg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:49:14.089434Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.03735","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f7d01d27774d26a7629433a56d230d5dba75fbee8e6af0006df0ba17b6d42417","sha256:5672892943f126f4dd9f4feade57f2301826b0f4479251100ffd9a73bb3201be"],"state_sha256":"5a156e9cab6811eefdfecb82919e9c3e264248bc10758adc2adb356ad05eb779"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C9h+soNJlFlGEj7zNNjjohxl6d/1WdDw7T2Wirs5MQMpNoY59akI3IMAyfH8YU0F+2ovqDtMdNjBRfUTkL5GCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T18:03:49.783133Z","bundle_sha256":"521da086a5d71145c012a56f79c5cc214485f3b83e371e4c4822a00d9688a5dc"}}