{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:KB5VV6IKJ57CCXTHC5PPBRJ5HO","short_pith_number":"pith:KB5VV6IK","canonical_record":{"source":{"id":"2401.13942","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-25T04:53:03Z","cross_cats_sorted":[],"title_canon_sha256":"886d6a63f72567ac47ffff3cf060f2ac549808ee5fb3170de3eaa88d0b1a09e2","abstract_canon_sha256":"d94a191b87f14858e91649738795bf9e3ba33af427440b1d50f9dcdcf0042c0e"},"schema_version":"1.0"},"canonical_sha256":"507b5af90a4f7e215e67175ef0c53d3b98ee4ec72d6a8c55eddbf7352ac89809","source":{"kind":"arxiv","id":"2401.13942","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.13942","created_at":"2026-07-05T08:17:30Z"},{"alias_kind":"arxiv_version","alias_value":"2401.13942v2","created_at":"2026-07-05T08:17:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.13942","created_at":"2026-07-05T08:17:30Z"},{"alias_kind":"pith_short_12","alias_value":"KB5VV6IKJ57C","created_at":"2026-07-05T08:17:30Z"},{"alias_kind":"pith_short_16","alias_value":"KB5VV6IKJ57CCXTH","created_at":"2026-07-05T08:17:30Z"},{"alias_kind":"pith_short_8","alias_value":"KB5VV6IK","created_at":"2026-07-05T08:17:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:KB5VV6IKJ57CCXTHC5PPBRJ5HO","target":"record","payload":{"canonical_record":{"source":{"id":"2401.13942","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-25T04:53:03Z","cross_cats_sorted":[],"title_canon_sha256":"886d6a63f72567ac47ffff3cf060f2ac549808ee5fb3170de3eaa88d0b1a09e2","abstract_canon_sha256":"d94a191b87f14858e91649738795bf9e3ba33af427440b1d50f9dcdcf0042c0e"},"schema_version":"1.0"},"canonical_sha256":"507b5af90a4f7e215e67175ef0c53d3b98ee4ec72d6a8c55eddbf7352ac89809","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:17:30.794729Z","signature_b64":"Xd2d1aIzzrSh88FMSNSA27h9mub1sxinSL7ZaV7UOA0RHD9ysKNYajcJMwjDkC0pB1qSQWTDGhP41MPJbfAhDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"507b5af90a4f7e215e67175ef0c53d3b98ee4ec72d6a8c55eddbf7352ac89809","last_reissued_at":"2026-07-05T08:17:30.794238Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:17:30.794238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.13942","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:17:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mwDsk2fAAiouAnrxW2uNhNQLcRcKPThQHagbiodEU+75GDlXTOuwhaXWMw3lxvyevFZi/KStd4m+jINJ96OTBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T23:05:18.555431Z"},"content_sha256":"38cb9b7700ca526991aab96912808f08c80107f989ec731b107c9b4033e88c9f","schema_version":"1.0","event_id":"sha256:38cb9b7700ca526991aab96912808f08c80107f989ec731b107c9b4033e88c9f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:KB5VV6IKJ57CCXTHC5PPBRJ5HO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mohan Zhou, Qing Yang, Tiejun Zhao, Yalong Bai","submitted_at":"2024-01-25T04:53:03Z","abstract_excerpt":"The ability to fine-tune generative models for text-to-image generation tasks is crucial, particularly facing the complexity involved in accurately interpreting and visualizing textual inputs. While LoRA is efficient for language model adaptation, it often falls short in text-to-image tasks due to the intricate demands of image generation, such as accommodating a broad spectrum of styles and nuances. To bridge this gap, we introduce StyleInject, a specialized fine-tuning approach tailored for text-to-image models. StyleInject comprises multiple parallel low-rank parameter matrices, maintaining"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.13942","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/2401.13942/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":"2026-07-05T08:17:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O86WZokc/alh9P0xphi45fk1bawcMpFGCqR5dEzT6UVfCuV5Njl5QrMbicpRsL6PAp+EsZSCPnuzmziYzvj5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T23:05:18.555930Z"},"content_sha256":"f8d9e459e571516e31b05b9496c62a6844fad935c98afcc75ed278c374fb75d0","schema_version":"1.0","event_id":"sha256:f8d9e459e571516e31b05b9496c62a6844fad935c98afcc75ed278c374fb75d0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KB5VV6IKJ57CCXTHC5PPBRJ5HO/bundle.json","state_url":"https://pith.science/pith/KB5VV6IKJ57CCXTHC5PPBRJ5HO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KB5VV6IKJ57CCXTHC5PPBRJ5HO/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":"2026-08-20T23:05:18Z","links":{"resolver":"https://pith.science/pith/KB5VV6IKJ57CCXTHC5PPBRJ5HO","bundle":"https://pith.science/pith/KB5VV6IKJ57CCXTHC5PPBRJ5HO/bundle.json","state":"https://pith.science/pith/KB5VV6IKJ57CCXTHC5PPBRJ5HO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KB5VV6IKJ57CCXTHC5PPBRJ5HO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:KB5VV6IKJ57CCXTHC5PPBRJ5HO","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":"d94a191b87f14858e91649738795bf9e3ba33af427440b1d50f9dcdcf0042c0e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-25T04:53:03Z","title_canon_sha256":"886d6a63f72567ac47ffff3cf060f2ac549808ee5fb3170de3eaa88d0b1a09e2"},"schema_version":"1.0","source":{"id":"2401.13942","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.13942","created_at":"2026-07-05T08:17:30Z"},{"alias_kind":"arxiv_version","alias_value":"2401.13942v2","created_at":"2026-07-05T08:17:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.13942","created_at":"2026-07-05T08:17:30Z"},{"alias_kind":"pith_short_12","alias_value":"KB5VV6IKJ57C","created_at":"2026-07-05T08:17:30Z"},{"alias_kind":"pith_short_16","alias_value":"KB5VV6IKJ57CCXTH","created_at":"2026-07-05T08:17:30Z"},{"alias_kind":"pith_short_8","alias_value":"KB5VV6IK","created_at":"2026-07-05T08:17:30Z"}],"graph_snapshots":[{"event_id":"sha256:f8d9e459e571516e31b05b9496c62a6844fad935c98afcc75ed278c374fb75d0","target":"graph","created_at":"2026-07-05T08:17:30Z","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/2401.13942/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The ability to fine-tune generative models for text-to-image generation tasks is crucial, particularly facing the complexity involved in accurately interpreting and visualizing textual inputs. While LoRA is efficient for language model adaptation, it often falls short in text-to-image tasks due to the intricate demands of image generation, such as accommodating a broad spectrum of styles and nuances. To bridge this gap, we introduce StyleInject, a specialized fine-tuning approach tailored for text-to-image models. StyleInject comprises multiple parallel low-rank parameter matrices, maintaining","authors_text":"Mohan Zhou, Qing Yang, Tiejun Zhao, Yalong Bai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-25T04:53:03Z","title":"StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.13942","kind":"arxiv","version":2},"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":"sha256:38cb9b7700ca526991aab96912808f08c80107f989ec731b107c9b4033e88c9f","target":"record","created_at":"2026-07-05T08:17:30Z","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":"d94a191b87f14858e91649738795bf9e3ba33af427440b1d50f9dcdcf0042c0e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-25T04:53:03Z","title_canon_sha256":"886d6a63f72567ac47ffff3cf060f2ac549808ee5fb3170de3eaa88d0b1a09e2"},"schema_version":"1.0","source":{"id":"2401.13942","kind":"arxiv","version":2}},"canonical_sha256":"507b5af90a4f7e215e67175ef0c53d3b98ee4ec72d6a8c55eddbf7352ac89809","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"507b5af90a4f7e215e67175ef0c53d3b98ee4ec72d6a8c55eddbf7352ac89809","first_computed_at":"2026-07-05T08:17:30.794238Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:17:30.794238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Xd2d1aIzzrSh88FMSNSA27h9mub1sxinSL7ZaV7UOA0RHD9ysKNYajcJMwjDkC0pB1qSQWTDGhP41MPJbfAhDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:17:30.794729Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.13942","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:38cb9b7700ca526991aab96912808f08c80107f989ec731b107c9b4033e88c9f","sha256:f8d9e459e571516e31b05b9496c62a6844fad935c98afcc75ed278c374fb75d0"],"state_sha256":"d2acac05dc4b917c66f644b30279701991c978766094121538b8fd61d9bfa3be"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W/XBieV7CyeDXIhVqnZs64meWx8Rb2ZTR9OcEG+RckWi99Ksxi3W3ftplsf7egKqJNxD2oVYd2mPp+l/IKarAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T23:05:18.559475Z","bundle_sha256":"93fc79d9a06cefbfae2bcbc74b5f2896bec86dcf4aca082666930abd7f21f901"}}