{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OIYY2DAPHMJXP5HNAZ5ZXJ6YDD","short_pith_number":"pith:OIYY2DAP","schema_version":"1.0","canonical_sha256":"72318d0c0f3b1377f4ed067b9ba7d818f7ad47c1f60b6210185e5d4b285bb217","source":{"kind":"arxiv","id":"2305.18259","version":2},"attestation_state":"computed","paper":{"title":"GlyphControl: Glyph Conditional Control for Visual Text Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dongnan Gui, Haisong Ding, Han Hu, Kai Chen, Weicong Liang, Yuhui Yuan, Yukang Yang","submitted_at":"2023-05-29T17:27:59Z","abstract_excerpt":"Recently, there has been an increasing interest in developing diffusion-based text-to-image generative models capable of generating coherent and well-formed visual text. In this paper, we propose a novel and efficient approach called GlyphControl to address this task. Unlike existing methods that rely on character-aware text encoders like ByT5 and require retraining of text-to-image models, our approach leverages additional glyph conditional information to enhance the performance of the off-the-shelf Stable-Diffusion model in generating accurate visual text. By incorporating glyph instructions"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2305.18259","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-29T17:27:59Z","cross_cats_sorted":[],"title_canon_sha256":"148267bb9db73011ab061b9832f0c971d0aeb08ff2edef1cc6f0dbaf04fe7555","abstract_canon_sha256":"ce20f4c15d544465d90034888a13c5b1efd0783c04c752b67bb2a04a754f4e58"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:11:35.079196Z","signature_b64":"zsi+tWz7eZojJL4K2W8eVaIq5zkiwUiqs0kBEb4w+qu6vJc2ipAIdBiLwWrOQuSHngJcXw2IXrBO2jBx9vf5Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72318d0c0f3b1377f4ed067b9ba7d818f7ad47c1f60b6210185e5d4b285bb217","last_reissued_at":"2026-07-05T07:11:35.078693Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:11:35.078693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GlyphControl: Glyph Conditional Control for Visual Text Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dongnan Gui, Haisong Ding, Han Hu, Kai Chen, Weicong Liang, Yuhui Yuan, Yukang Yang","submitted_at":"2023-05-29T17:27:59Z","abstract_excerpt":"Recently, there has been an increasing interest in developing diffusion-based text-to-image generative models capable of generating coherent and well-formed visual text. In this paper, we propose a novel and efficient approach called GlyphControl to address this task. Unlike existing methods that rely on character-aware text encoders like ByT5 and require retraining of text-to-image models, our approach leverages additional glyph conditional information to enhance the performance of the off-the-shelf Stable-Diffusion model in generating accurate visual text. By incorporating glyph instructions"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.18259","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/2305.18259/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2305.18259","created_at":"2026-07-05T07:11:35.078757+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.18259v2","created_at":"2026-07-05T07:11:35.078757+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.18259","created_at":"2026-07-05T07:11:35.078757+00:00"},{"alias_kind":"pith_short_12","alias_value":"OIYY2DAPHMJX","created_at":"2026-07-05T07:11:35.078757+00:00"},{"alias_kind":"pith_short_16","alias_value":"OIYY2DAPHMJXP5HN","created_at":"2026-07-05T07:11:35.078757+00:00"},{"alias_kind":"pith_short_8","alias_value":"OIYY2DAP","created_at":"2026-07-05T07:11:35.078757+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.14708","citing_title":"StyleTextGen: Style-Conditioned Multilingual Scene Text Generation","ref_index":52,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD","json":"https://pith.science/pith/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD.json","graph_json":"https://pith.science/api/pith-number/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD/graph.json","events_json":"https://pith.science/api/pith-number/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD/events.json","paper":"https://pith.science/paper/OIYY2DAP"},"agent_actions":{"view_html":"https://pith.science/pith/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD","download_json":"https://pith.science/pith/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD.json","view_paper":"https://pith.science/paper/OIYY2DAP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.18259&json=true","fetch_graph":"https://pith.science/api/pith-number/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD/graph.json","fetch_events":"https://pith.science/api/pith-number/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD/action/storage_attestation","attest_author":"https://pith.science/pith/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD/action/author_attestation","sign_citation":"https://pith.science/pith/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD/action/citation_signature","submit_replication":"https://pith.science/pith/OIYY2DAPHMJXP5HNAZ5ZXJ6YDD/action/replication_record"}},"created_at":"2026-07-05T07:11:35.078757+00:00","updated_at":"2026-07-05T07:11:35.078757+00:00"}