{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:UT7D7AIHD5MKYHXRRKHW27AUCR","short_pith_number":"pith:UT7D7AIH","canonical_record":{"source":{"id":"2507.00789","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-01T14:24:40Z","cross_cats_sorted":[],"title_canon_sha256":"dad2c08920d127d932f3ec5cfae3a0c39043c142703358026f6dc5e71a9e005b","abstract_canon_sha256":"9c0e3bd1d2103e5792bea9921caabce4f877f874e7baeafb38cd94d26c24ac9e"},"schema_version":"1.0"},"canonical_sha256":"a4fe3f81071f58ac1ef18a8f6d7c141453ef7fecdc2bddb78ea6962b9cc84667","source":{"kind":"arxiv","id":"2507.00789","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.00789","created_at":"2026-07-05T11:30:14Z"},{"alias_kind":"arxiv_version","alias_value":"2507.00789v1","created_at":"2026-07-05T11:30:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.00789","created_at":"2026-07-05T11:30:14Z"},{"alias_kind":"pith_short_12","alias_value":"UT7D7AIHD5MK","created_at":"2026-07-05T11:30:14Z"},{"alias_kind":"pith_short_16","alias_value":"UT7D7AIHD5MKYHXR","created_at":"2026-07-05T11:30:14Z"},{"alias_kind":"pith_short_8","alias_value":"UT7D7AIH","created_at":"2026-07-05T11:30:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:UT7D7AIHD5MKYHXRRKHW27AUCR","target":"record","payload":{"canonical_record":{"source":{"id":"2507.00789","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-01T14:24:40Z","cross_cats_sorted":[],"title_canon_sha256":"dad2c08920d127d932f3ec5cfae3a0c39043c142703358026f6dc5e71a9e005b","abstract_canon_sha256":"9c0e3bd1d2103e5792bea9921caabce4f877f874e7baeafb38cd94d26c24ac9e"},"schema_version":"1.0"},"canonical_sha256":"a4fe3f81071f58ac1ef18a8f6d7c141453ef7fecdc2bddb78ea6962b9cc84667","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:30:14.467161Z","signature_b64":"AHlUFVsHh8gG44TvwcUBxoL32xt19kLvaZeak0g9nXiyRKls/9B1uNmF6xzS2wR0DjYSPH0H5GCDdqXG4nraAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4fe3f81071f58ac1ef18a8f6d7c141453ef7fecdc2bddb78ea6962b9cc84667","last_reissued_at":"2026-07-05T11:30:14.466021Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:30:14.466021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.00789","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":"2026-07-05T11:30:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sfSO0ZfNUSTlT0m2sDC2GGO7QjD4uKiIV8h9n9xK8C73OC8vZPb/vNKyxo1KHjvrzLlbUsJjHIbEpxO91C+ICA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:56:28.451106Z"},"content_sha256":"2bbb7a60480750ece147c264cb303f95f8a13ff2b2604e365d18e6f854a1af9b","schema_version":"1.0","event_id":"sha256:2bbb7a60480750ece147c264cb303f95f8a13ff2b2604e365d18e6f854a1af9b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:UT7D7AIHD5MKYHXRRKHW27AUCR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"OptiPrune: Boosting Prompt-Image Consistency with Attention-Guided Noise and Dynamic Token Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ziji Lu","submitted_at":"2025-07-01T14:24:40Z","abstract_excerpt":"Text-to-image diffusion models often struggle to achieve accurate semantic alignment between generated images and text prompts while maintaining efficiency for deployment on resource-constrained hardware. Existing approaches either incur substantial computational overhead through noise optimization or compromise semantic fidelity by aggressively pruning tokens. In this work, we propose OptiPrune, a unified framework that combines distribution-aware initial noise optimization with similarity-based token pruning to address both challenges simultaneously. Specifically, (1) we introduce a distribu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.00789","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/2507.00789/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":"2026-07-05T11:30:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BhrtLf6I8bAcb2FLgiOqwfZXeAlAI3wPDUQbmWpAIIJr529LPNfWcCTKne1caYDt7dr4l1r5KRfKUxV4HeySBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:56:28.451625Z"},"content_sha256":"afee1819c023af10ad56901863c069d4e21ec1474e1645889377bd827aaa87f8","schema_version":"1.0","event_id":"sha256:afee1819c023af10ad56901863c069d4e21ec1474e1645889377bd827aaa87f8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UT7D7AIHD5MKYHXRRKHW27AUCR/bundle.json","state_url":"https://pith.science/pith/UT7D7AIHD5MKYHXRRKHW27AUCR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UT7D7AIHD5MKYHXRRKHW27AUCR/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":"2026-08-09T07:56:28Z","links":{"resolver":"https://pith.science/pith/UT7D7AIHD5MKYHXRRKHW27AUCR","bundle":"https://pith.science/pith/UT7D7AIHD5MKYHXRRKHW27AUCR/bundle.json","state":"https://pith.science/pith/UT7D7AIHD5MKYHXRRKHW27AUCR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UT7D7AIHD5MKYHXRRKHW27AUCR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:UT7D7AIHD5MKYHXRRKHW27AUCR","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":"9c0e3bd1d2103e5792bea9921caabce4f877f874e7baeafb38cd94d26c24ac9e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-01T14:24:40Z","title_canon_sha256":"dad2c08920d127d932f3ec5cfae3a0c39043c142703358026f6dc5e71a9e005b"},"schema_version":"1.0","source":{"id":"2507.00789","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.00789","created_at":"2026-07-05T11:30:14Z"},{"alias_kind":"arxiv_version","alias_value":"2507.00789v1","created_at":"2026-07-05T11:30:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.00789","created_at":"2026-07-05T11:30:14Z"},{"alias_kind":"pith_short_12","alias_value":"UT7D7AIHD5MK","created_at":"2026-07-05T11:30:14Z"},{"alias_kind":"pith_short_16","alias_value":"UT7D7AIHD5MKYHXR","created_at":"2026-07-05T11:30:14Z"},{"alias_kind":"pith_short_8","alias_value":"UT7D7AIH","created_at":"2026-07-05T11:30:14Z"}],"graph_snapshots":[{"event_id":"sha256:afee1819c023af10ad56901863c069d4e21ec1474e1645889377bd827aaa87f8","target":"graph","created_at":"2026-07-05T11:30:14Z","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/2507.00789/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text-to-image diffusion models often struggle to achieve accurate semantic alignment between generated images and text prompts while maintaining efficiency for deployment on resource-constrained hardware. Existing approaches either incur substantial computational overhead through noise optimization or compromise semantic fidelity by aggressively pruning tokens. In this work, we propose OptiPrune, a unified framework that combines distribution-aware initial noise optimization with similarity-based token pruning to address both challenges simultaneously. Specifically, (1) we introduce a distribu","authors_text":"Ziji Lu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-01T14:24:40Z","title":"OptiPrune: Boosting Prompt-Image Consistency with Attention-Guided Noise and Dynamic Token Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.00789","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":"sha256:2bbb7a60480750ece147c264cb303f95f8a13ff2b2604e365d18e6f854a1af9b","target":"record","created_at":"2026-07-05T11:30:14Z","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":"9c0e3bd1d2103e5792bea9921caabce4f877f874e7baeafb38cd94d26c24ac9e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-01T14:24:40Z","title_canon_sha256":"dad2c08920d127d932f3ec5cfae3a0c39043c142703358026f6dc5e71a9e005b"},"schema_version":"1.0","source":{"id":"2507.00789","kind":"arxiv","version":1}},"canonical_sha256":"a4fe3f81071f58ac1ef18a8f6d7c141453ef7fecdc2bddb78ea6962b9cc84667","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a4fe3f81071f58ac1ef18a8f6d7c141453ef7fecdc2bddb78ea6962b9cc84667","first_computed_at":"2026-07-05T11:30:14.466021Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:30:14.466021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AHlUFVsHh8gG44TvwcUBxoL32xt19kLvaZeak0g9nXiyRKls/9B1uNmF6xzS2wR0DjYSPH0H5GCDdqXG4nraAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:30:14.467161Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.00789","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2bbb7a60480750ece147c264cb303f95f8a13ff2b2604e365d18e6f854a1af9b","sha256:afee1819c023af10ad56901863c069d4e21ec1474e1645889377bd827aaa87f8"],"state_sha256":"f2c2365fb1069ca23f6828ed3d1e9f54eaac06a0fcc7c3da6faa43ddeb6bbc61"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qt5yoXp/kSSWE3d/RjY8oKGX4PVNau5J80PUmZ/Ep8sal8/WNFfg0v3YyZZafTa6p5UppcQ6rM4cQ41h6u5tDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T07:56:28.456656Z","bundle_sha256":"1beeabb6c96af63abf4f9e622a215de170c7cf91a834168b089faa167a591dde"}}