{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:JKSSVTN3KWFW7EY4X4IJL4GDHT","short_pith_number":"pith:JKSSVTN3","canonical_record":{"source":{"id":"2403.04014","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-03-06T19:55:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"43ff011d8db7ae7e37068d7d356640ab6eb6b1bc186fc5287cee6f6a7da218f7","abstract_canon_sha256":"30b3bc6c2d927434461c072d20aa626f1bcaddfbf549d364c4e38925d601c9a9"},"schema_version":"1.0"},"canonical_sha256":"4aa52acdbb558b6f931cbf1095f0c33cd806e351fcb3630a6c11808da761d0c5","source":{"kind":"arxiv","id":"2403.04014","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.04014","created_at":"2026-07-05T07:53:17Z"},{"alias_kind":"arxiv_version","alias_value":"2403.04014v1","created_at":"2026-07-05T07:53:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.04014","created_at":"2026-07-05T07:53:17Z"},{"alias_kind":"pith_short_12","alias_value":"JKSSVTN3KWFW","created_at":"2026-07-05T07:53:17Z"},{"alias_kind":"pith_short_16","alias_value":"JKSSVTN3KWFW7EY4","created_at":"2026-07-05T07:53:17Z"},{"alias_kind":"pith_short_8","alias_value":"JKSSVTN3","created_at":"2026-07-05T07:53:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:JKSSVTN3KWFW7EY4X4IJL4GDHT","target":"record","payload":{"canonical_record":{"source":{"id":"2403.04014","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-03-06T19:55:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"43ff011d8db7ae7e37068d7d356640ab6eb6b1bc186fc5287cee6f6a7da218f7","abstract_canon_sha256":"30b3bc6c2d927434461c072d20aa626f1bcaddfbf549d364c4e38925d601c9a9"},"schema_version":"1.0"},"canonical_sha256":"4aa52acdbb558b6f931cbf1095f0c33cd806e351fcb3630a6c11808da761d0c5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:53:17.097176Z","signature_b64":"4/QNyIv0ZQfUyWeye4BqFAvxUcOIVwyI5u6cP+pCaarUbaZJVJztAb8hbtJM2csAhjw9H2DJy/wTElzYjUcGCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4aa52acdbb558b6f931cbf1095f0c33cd806e351fcb3630a6c11808da761d0c5","last_reissued_at":"2026-07-05T07:53:17.096580Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:53:17.096580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.04014","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":"2026-07-05T07:53:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sDJRBuI7LEQZA06daIZ7o4TxWr/xflFlyzaL7rF5VGtlkxxUYCJA2qsfQhdsKv1LZ/OopCX4CAKOLiuPuY/mCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T13:34:35.311138Z"},"content_sha256":"5b334023c3570dad7a322486f009e7250dd28bb6ab3e74a3f7533efc52bcf0c9","schema_version":"1.0","event_id":"sha256:5b334023c3570dad7a322486f009e7250dd28bb6ab3e74a3f7533efc52bcf0c9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:JKSSVTN3KWFW7EY4X4IJL4GDHT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Da Song, Lei Ma, Tianyi Zhang, Yuheng Huang, Zhijie Wang","submitted_at":"2024-03-06T19:55:01Z","abstract_excerpt":"The recent advancements in Generative AI have significantly advanced the field of text-to-image generation. The state-of-the-art text-to-image model, Stable Diffusion, is now capable of synthesizing high-quality images with a strong sense of aesthetics. Crafting text prompts that align with the model's interpretation and the user's intent thus becomes crucial. However, prompting remains challenging for novice users due to the complexity of the stable diffusion model and the non-trivial efforts required for iteratively editing and refining the text prompts. To address these challenges, we propo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.04014","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/2403.04014/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":"2026-07-05T07:53:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CwaAwhjJni48BGkpaKYPxMafP4OlJWR9O1/Seyyq7D2FIqe/jTFIdawtihT+Y95pyQuNxaoU35ozAs+xWOvGBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T13:34:35.311470Z"},"content_sha256":"764f2a78b94c2ca903cab5ae2f74d7d1fa3869005f171e488a75a6b70cabe677","schema_version":"1.0","event_id":"sha256:764f2a78b94c2ca903cab5ae2f74d7d1fa3869005f171e488a75a6b70cabe677"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JKSSVTN3KWFW7EY4X4IJL4GDHT/bundle.json","state_url":"https://pith.science/pith/JKSSVTN3KWFW7EY4X4IJL4GDHT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JKSSVTN3KWFW7EY4X4IJL4GDHT/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":"2026-08-22T13:34:35Z","links":{"resolver":"https://pith.science/pith/JKSSVTN3KWFW7EY4X4IJL4GDHT","bundle":"https://pith.science/pith/JKSSVTN3KWFW7EY4X4IJL4GDHT/bundle.json","state":"https://pith.science/pith/JKSSVTN3KWFW7EY4X4IJL4GDHT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JKSSVTN3KWFW7EY4X4IJL4GDHT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JKSSVTN3KWFW7EY4X4IJL4GDHT","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":"30b3bc6c2d927434461c072d20aa626f1bcaddfbf549d364c4e38925d601c9a9","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-03-06T19:55:01Z","title_canon_sha256":"43ff011d8db7ae7e37068d7d356640ab6eb6b1bc186fc5287cee6f6a7da218f7"},"schema_version":"1.0","source":{"id":"2403.04014","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.04014","created_at":"2026-07-05T07:53:17Z"},{"alias_kind":"arxiv_version","alias_value":"2403.04014v1","created_at":"2026-07-05T07:53:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.04014","created_at":"2026-07-05T07:53:17Z"},{"alias_kind":"pith_short_12","alias_value":"JKSSVTN3KWFW","created_at":"2026-07-05T07:53:17Z"},{"alias_kind":"pith_short_16","alias_value":"JKSSVTN3KWFW7EY4","created_at":"2026-07-05T07:53:17Z"},{"alias_kind":"pith_short_8","alias_value":"JKSSVTN3","created_at":"2026-07-05T07:53:17Z"}],"graph_snapshots":[{"event_id":"sha256:764f2a78b94c2ca903cab5ae2f74d7d1fa3869005f171e488a75a6b70cabe677","target":"graph","created_at":"2026-07-05T07:53:17Z","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/2403.04014/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The recent advancements in Generative AI have significantly advanced the field of text-to-image generation. The state-of-the-art text-to-image model, Stable Diffusion, is now capable of synthesizing high-quality images with a strong sense of aesthetics. Crafting text prompts that align with the model's interpretation and the user's intent thus becomes crucial. However, prompting remains challenging for novice users due to the complexity of the stable diffusion model and the non-trivial efforts required for iteratively editing and refining the text prompts. To address these challenges, we propo","authors_text":"Da Song, Lei Ma, Tianyi Zhang, Yuheng Huang, Zhijie Wang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-03-06T19:55:01Z","title":"PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.04014","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":"sha256:5b334023c3570dad7a322486f009e7250dd28bb6ab3e74a3f7533efc52bcf0c9","target":"record","created_at":"2026-07-05T07:53:17Z","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":"30b3bc6c2d927434461c072d20aa626f1bcaddfbf549d364c4e38925d601c9a9","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-03-06T19:55:01Z","title_canon_sha256":"43ff011d8db7ae7e37068d7d356640ab6eb6b1bc186fc5287cee6f6a7da218f7"},"schema_version":"1.0","source":{"id":"2403.04014","kind":"arxiv","version":1}},"canonical_sha256":"4aa52acdbb558b6f931cbf1095f0c33cd806e351fcb3630a6c11808da761d0c5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4aa52acdbb558b6f931cbf1095f0c33cd806e351fcb3630a6c11808da761d0c5","first_computed_at":"2026-07-05T07:53:17.096580Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:53:17.096580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4/QNyIv0ZQfUyWeye4BqFAvxUcOIVwyI5u6cP+pCaarUbaZJVJztAb8hbtJM2csAhjw9H2DJy/wTElzYjUcGCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:53:17.097176Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.04014","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5b334023c3570dad7a322486f009e7250dd28bb6ab3e74a3f7533efc52bcf0c9","sha256:764f2a78b94c2ca903cab5ae2f74d7d1fa3869005f171e488a75a6b70cabe677"],"state_sha256":"d9927b606a8571a91fdb0e4366dfd1acf3530367871a8cd6ce7e371c08bcdb33"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mtmqMwIvr13dyEIb7RttvDMb81sNY+sm32J0sWjLaXDH5ztb0sAEaJsPSlICo5qBTfFZdxYAHaoPjBIfTrGaDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T13:34:35.314510Z","bundle_sha256":"6a42acef0e3bc9f32305a15951dabb70ba7065f0c36a7052fe8e40f8fef2539d"}}