{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:VCKSM5HZS3S73JVYHIDH34CONZ","short_pith_number":"pith:VCKSM5HZ","canonical_record":{"source":{"id":"2312.12232","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-19T15:18:40Z","cross_cats_sorted":[],"title_canon_sha256":"13d9dc53e98f8bad62ab8d99d02461549f45ae6f245e1440f929a39ff5341ea6","abstract_canon_sha256":"25bb6a2caa4a84521994e3bc3ecc6e258eb5bf2a62d585d3dd7ac2bbafdf967d"},"schema_version":"1.0"},"canonical_sha256":"a8952674f996e5fda6b83a067df04e6e73c23b0a668255e3fda3884b027c8e46","source":{"kind":"arxiv","id":"2312.12232","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.12232","created_at":"2026-07-05T07:26:00Z"},{"alias_kind":"arxiv_version","alias_value":"2312.12232v1","created_at":"2026-07-05T07:26:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.12232","created_at":"2026-07-05T07:26:00Z"},{"alias_kind":"pith_short_12","alias_value":"VCKSM5HZS3S7","created_at":"2026-07-05T07:26:00Z"},{"alias_kind":"pith_short_16","alias_value":"VCKSM5HZS3S73JVY","created_at":"2026-07-05T07:26:00Z"},{"alias_kind":"pith_short_8","alias_value":"VCKSM5HZ","created_at":"2026-07-05T07:26:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:VCKSM5HZS3S73JVYHIDH34CONZ","target":"record","payload":{"canonical_record":{"source":{"id":"2312.12232","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-19T15:18:40Z","cross_cats_sorted":[],"title_canon_sha256":"13d9dc53e98f8bad62ab8d99d02461549f45ae6f245e1440f929a39ff5341ea6","abstract_canon_sha256":"25bb6a2caa4a84521994e3bc3ecc6e258eb5bf2a62d585d3dd7ac2bbafdf967d"},"schema_version":"1.0"},"canonical_sha256":"a8952674f996e5fda6b83a067df04e6e73c23b0a668255e3fda3884b027c8e46","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:26:00.727062Z","signature_b64":"iOattwKxGTF8BtkRdRTFe+o9QRcra8ruXzo4J1ZDqCzh5cnIJETI5B4Sog7JDfQWXD52pviA4b166BB/TsJrCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8952674f996e5fda6b83a067df04e6e73c23b0a668255e3fda3884b027c8e46","last_reissued_at":"2026-07-05T07:26:00.726567Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:26:00.726567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.12232","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":"2026-07-05T07:26:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l3m2ETGpH5Hi/12sR28ZEwpzhEAAV+OqkqRzcaDq66UIP9O4cS/GVKgE8QTHez8n7PSq2N2pmRC4gs7oB7S0Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T21:56:20.561916Z"},"content_sha256":"787ed635b92dd400bfd9c6c300245851d8b23f77d02a3f538fbe9aac29037846","schema_version":"1.0","event_id":"sha256:787ed635b92dd400bfd9c6c300245851d8b23f77d02a3f538fbe9aac29037846"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:VCKSM5HZS3S73JVYHIDH34CONZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lingjun Zhang, Xinyuan Chen, Yaohui Wang, Yue Lu, Yu Qiao","submitted_at":"2023-12-19T15:18:40Z","abstract_excerpt":"Recently, diffusion-based image generation methods are credited for their remarkable text-to-image generation capabilities, while still facing challenges in accurately generating multilingual scene text images. To tackle this problem, we propose Diff-Text, which is a training-free scene text generation framework for any language. Our model outputs a photo-realistic image given a text of any language along with a textual description of a scene. The model leverages rendered sketch images as priors, thus arousing the potential multilingual-generation ability of the pre-trained Stable Diffusion. B"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.12232","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/2312.12232/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":"2026-07-05T07:26:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PSQfgXugL/zm6nUj0XjLpDiBatIVDfLHSbT3hpJYx7qGx4f7Qsm9QWwI8lsMPpqAMLO+VOkJ+7iT7+DPSMo4BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T21:56:20.562413Z"},"content_sha256":"20b09e4610066b45d8d3f2eaed7d4457bc898981204ce02bc310239615954212","schema_version":"1.0","event_id":"sha256:20b09e4610066b45d8d3f2eaed7d4457bc898981204ce02bc310239615954212"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VCKSM5HZS3S73JVYHIDH34CONZ/bundle.json","state_url":"https://pith.science/pith/VCKSM5HZS3S73JVYHIDH34CONZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VCKSM5HZS3S73JVYHIDH34CONZ/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":"2026-08-08T21:56:20Z","links":{"resolver":"https://pith.science/pith/VCKSM5HZS3S73JVYHIDH34CONZ","bundle":"https://pith.science/pith/VCKSM5HZS3S73JVYHIDH34CONZ/bundle.json","state":"https://pith.science/pith/VCKSM5HZS3S73JVYHIDH34CONZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VCKSM5HZS3S73JVYHIDH34CONZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:VCKSM5HZS3S73JVYHIDH34CONZ","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":"25bb6a2caa4a84521994e3bc3ecc6e258eb5bf2a62d585d3dd7ac2bbafdf967d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-19T15:18:40Z","title_canon_sha256":"13d9dc53e98f8bad62ab8d99d02461549f45ae6f245e1440f929a39ff5341ea6"},"schema_version":"1.0","source":{"id":"2312.12232","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.12232","created_at":"2026-07-05T07:26:00Z"},{"alias_kind":"arxiv_version","alias_value":"2312.12232v1","created_at":"2026-07-05T07:26:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.12232","created_at":"2026-07-05T07:26:00Z"},{"alias_kind":"pith_short_12","alias_value":"VCKSM5HZS3S7","created_at":"2026-07-05T07:26:00Z"},{"alias_kind":"pith_short_16","alias_value":"VCKSM5HZS3S73JVY","created_at":"2026-07-05T07:26:00Z"},{"alias_kind":"pith_short_8","alias_value":"VCKSM5HZ","created_at":"2026-07-05T07:26:00Z"}],"graph_snapshots":[{"event_id":"sha256:20b09e4610066b45d8d3f2eaed7d4457bc898981204ce02bc310239615954212","target":"graph","created_at":"2026-07-05T07:26:00Z","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/2312.12232/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, diffusion-based image generation methods are credited for their remarkable text-to-image generation capabilities, while still facing challenges in accurately generating multilingual scene text images. To tackle this problem, we propose Diff-Text, which is a training-free scene text generation framework for any language. Our model outputs a photo-realistic image given a text of any language along with a textual description of a scene. The model leverages rendered sketch images as priors, thus arousing the potential multilingual-generation ability of the pre-trained Stable Diffusion. B","authors_text":"Lingjun Zhang, Xinyuan Chen, Yaohui Wang, Yue Lu, Yu Qiao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-19T15:18:40Z","title":"Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.12232","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":"sha256:787ed635b92dd400bfd9c6c300245851d8b23f77d02a3f538fbe9aac29037846","target":"record","created_at":"2026-07-05T07:26:00Z","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":"25bb6a2caa4a84521994e3bc3ecc6e258eb5bf2a62d585d3dd7ac2bbafdf967d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-19T15:18:40Z","title_canon_sha256":"13d9dc53e98f8bad62ab8d99d02461549f45ae6f245e1440f929a39ff5341ea6"},"schema_version":"1.0","source":{"id":"2312.12232","kind":"arxiv","version":1}},"canonical_sha256":"a8952674f996e5fda6b83a067df04e6e73c23b0a668255e3fda3884b027c8e46","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a8952674f996e5fda6b83a067df04e6e73c23b0a668255e3fda3884b027c8e46","first_computed_at":"2026-07-05T07:26:00.726567Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:26:00.726567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iOattwKxGTF8BtkRdRTFe+o9QRcra8ruXzo4J1ZDqCzh5cnIJETI5B4Sog7JDfQWXD52pviA4b166BB/TsJrCg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:26:00.727062Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.12232","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:787ed635b92dd400bfd9c6c300245851d8b23f77d02a3f538fbe9aac29037846","sha256:20b09e4610066b45d8d3f2eaed7d4457bc898981204ce02bc310239615954212"],"state_sha256":"22fa8aec1e3c4fcc878d31ad16d775d059af94bdd7492e719f61f75f9b1c3171"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N5GRKpDHz9rdxvuYo5o2keP1hT1EjsyW+U2oaloR0Dg97IPRVJqJHBdsDa40KqVyWVxIoGBnaPdVLPavJZi2Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T21:56:20.565693Z","bundle_sha256":"b0a3696a329eacfcb99c8b8e661cd05b6653097ef45f737e2ec2eb477656a179"}}