{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:WAJJT4JRZGJNORRQ2M42P2ZCJ2","short_pith_number":"pith:WAJJT4JR","canonical_record":{"source":{"id":"2311.10794","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-17T03:00:29Z","cross_cats_sorted":[],"title_canon_sha256":"e26b4e572b0b6e34db6d88fee629e466668692025f237bd1bd2acbfde4740146","abstract_canon_sha256":"23e9af6fcaafd0f0f86df171ec1b8bd239139e1d6b935d2c63d1784db47187d6"},"schema_version":"1.0"},"canonical_sha256":"b01299f131c992d74630d339a7eb224e91f8c770e235061384feec7416f6d7d8","source":{"kind":"arxiv","id":"2311.10794","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.10794","created_at":"2026-07-05T09:15:07Z"},{"alias_kind":"arxiv_version","alias_value":"2311.10794v2","created_at":"2026-07-05T09:15:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.10794","created_at":"2026-07-05T09:15:07Z"},{"alias_kind":"pith_short_12","alias_value":"WAJJT4JRZGJN","created_at":"2026-07-05T09:15:07Z"},{"alias_kind":"pith_short_16","alias_value":"WAJJT4JRZGJNORRQ","created_at":"2026-07-05T09:15:07Z"},{"alias_kind":"pith_short_8","alias_value":"WAJJT4JR","created_at":"2026-07-05T09:15:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:WAJJT4JRZGJNORRQ2M42P2ZCJ2","target":"record","payload":{"canonical_record":{"source":{"id":"2311.10794","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-17T03:00:29Z","cross_cats_sorted":[],"title_canon_sha256":"e26b4e572b0b6e34db6d88fee629e466668692025f237bd1bd2acbfde4740146","abstract_canon_sha256":"23e9af6fcaafd0f0f86df171ec1b8bd239139e1d6b935d2c63d1784db47187d6"},"schema_version":"1.0"},"canonical_sha256":"b01299f131c992d74630d339a7eb224e91f8c770e235061384feec7416f6d7d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:07.310766Z","signature_b64":"ybDYmycL7mtJkhbbIjYCFTDtS2ZxjeEsFyrje3DuzOHas/ts2RQP++5ZMVg0qvgABfPpcQfF5u2Y4DxGQ0z9CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b01299f131c992d74630d339a7eb224e91f8c770e235061384feec7416f6d7d8","last_reissued_at":"2026-07-05T09:15:07.310293Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:07.310293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.10794","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":"2026-07-05T09:15:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AwQCxiG415A0AuyD928RkSZGkOWG0681UJbZLLW2JRTezZsfNg9728uGIAK0fRqKXgG1RWatmvrI1aogoBMGBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T10:03:57.213625Z"},"content_sha256":"fd3c3e2b49be0c225a86133d641f3e24760466c3a4162d03e02e95f2a89112e6","schema_version":"1.0","event_id":"sha256:fd3c3e2b49be0c225a86133d641f3e24760466c3a4162d03e02e95f2a89112e6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:WAJJT4JRZGJNORRQ2M42P2ZCJ2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amy Bearman, Animesh Sinha, Ankit Ramchandani, Anmol Kalia, Arantxa Casanova, Bo Sun, David Yan, Dhruv Mahajan, Elliot Blanchard, Hardik Shah, Jiahui Chen, Licheng Yu, Maziar Sanjabi, Mitesh Kumar Singh, Sonal Gupta, Tony Nelli, Winnie Zhang","submitted_at":"2023-11-17T03:00:29Z","abstract_excerpt":"We introduce Style Tailoring, a recipe to finetune Latent Diffusion Models (LDMs) in a distinct domain with high visual quality, prompt alignment and scene diversity. We choose sticker image generation as the target domain, as the images significantly differ from photorealistic samples typically generated by large-scale LDMs. We start with a competent text-to-image model, like Emu, and show that relying on prompt engineering with a photorealistic model to generate stickers leads to poor prompt alignment and scene diversity. To overcome these drawbacks, we first finetune Emu on millions of stic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.10794","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/2311.10794/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":"2026-07-05T09:15:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NguGtEpmgOTPtobZbt4ak6YbYrztfYQ3F63TnuBmQmhuJG44NSqQ/IDNOmOC9zpkA4q0JHnUGG6YvXghukugAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T10:03:57.213997Z"},"content_sha256":"f799793d6d6c2fe82a506f4c23c29887d91045aec3bbc372fe24dc6516ff8ef0","schema_version":"1.0","event_id":"sha256:f799793d6d6c2fe82a506f4c23c29887d91045aec3bbc372fe24dc6516ff8ef0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WAJJT4JRZGJNORRQ2M42P2ZCJ2/bundle.json","state_url":"https://pith.science/pith/WAJJT4JRZGJNORRQ2M42P2ZCJ2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WAJJT4JRZGJNORRQ2M42P2ZCJ2/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":"2026-07-06T10:03:57Z","links":{"resolver":"https://pith.science/pith/WAJJT4JRZGJNORRQ2M42P2ZCJ2","bundle":"https://pith.science/pith/WAJJT4JRZGJNORRQ2M42P2ZCJ2/bundle.json","state":"https://pith.science/pith/WAJJT4JRZGJNORRQ2M42P2ZCJ2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WAJJT4JRZGJNORRQ2M42P2ZCJ2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:WAJJT4JRZGJNORRQ2M42P2ZCJ2","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":"23e9af6fcaafd0f0f86df171ec1b8bd239139e1d6b935d2c63d1784db47187d6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-17T03:00:29Z","title_canon_sha256":"e26b4e572b0b6e34db6d88fee629e466668692025f237bd1bd2acbfde4740146"},"schema_version":"1.0","source":{"id":"2311.10794","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.10794","created_at":"2026-07-05T09:15:07Z"},{"alias_kind":"arxiv_version","alias_value":"2311.10794v2","created_at":"2026-07-05T09:15:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.10794","created_at":"2026-07-05T09:15:07Z"},{"alias_kind":"pith_short_12","alias_value":"WAJJT4JRZGJN","created_at":"2026-07-05T09:15:07Z"},{"alias_kind":"pith_short_16","alias_value":"WAJJT4JRZGJNORRQ","created_at":"2026-07-05T09:15:07Z"},{"alias_kind":"pith_short_8","alias_value":"WAJJT4JR","created_at":"2026-07-05T09:15:07Z"}],"graph_snapshots":[{"event_id":"sha256:f799793d6d6c2fe82a506f4c23c29887d91045aec3bbc372fe24dc6516ff8ef0","target":"graph","created_at":"2026-07-05T09:15:07Z","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/2311.10794/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce Style Tailoring, a recipe to finetune Latent Diffusion Models (LDMs) in a distinct domain with high visual quality, prompt alignment and scene diversity. We choose sticker image generation as the target domain, as the images significantly differ from photorealistic samples typically generated by large-scale LDMs. We start with a competent text-to-image model, like Emu, and show that relying on prompt engineering with a photorealistic model to generate stickers leads to poor prompt alignment and scene diversity. To overcome these drawbacks, we first finetune Emu on millions of stic","authors_text":"Amy Bearman, Animesh Sinha, Ankit Ramchandani, Anmol Kalia, Arantxa Casanova, Bo Sun, David Yan, Dhruv Mahajan, Elliot Blanchard, Hardik Shah, Jiahui Chen, Licheng Yu, Maziar Sanjabi, Mitesh Kumar Singh, Sonal Gupta, Tony Nelli, Winnie Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-17T03:00:29Z","title":"Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.10794","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:fd3c3e2b49be0c225a86133d641f3e24760466c3a4162d03e02e95f2a89112e6","target":"record","created_at":"2026-07-05T09:15:07Z","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":"23e9af6fcaafd0f0f86df171ec1b8bd239139e1d6b935d2c63d1784db47187d6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-17T03:00:29Z","title_canon_sha256":"e26b4e572b0b6e34db6d88fee629e466668692025f237bd1bd2acbfde4740146"},"schema_version":"1.0","source":{"id":"2311.10794","kind":"arxiv","version":2}},"canonical_sha256":"b01299f131c992d74630d339a7eb224e91f8c770e235061384feec7416f6d7d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b01299f131c992d74630d339a7eb224e91f8c770e235061384feec7416f6d7d8","first_computed_at":"2026-07-05T09:15:07.310293Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:15:07.310293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ybDYmycL7mtJkhbbIjYCFTDtS2ZxjeEsFyrje3DuzOHas/ts2RQP++5ZMVg0qvgABfPpcQfF5u2Y4DxGQ0z9CA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:15:07.310766Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.10794","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fd3c3e2b49be0c225a86133d641f3e24760466c3a4162d03e02e95f2a89112e6","sha256:f799793d6d6c2fe82a506f4c23c29887d91045aec3bbc372fe24dc6516ff8ef0"],"state_sha256":"d727d77be42c2b14149a7a95da2c65574d197eca2d175715906df362a72b2451"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Su/f6/Q8xZnHrUgsG6eBvR6FA16yjjhACE6NNcj/hXo4c72BeCRPKjQqaIzG/csWM1m58BFY36A6WJruhGqRCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T10:03:57.215983Z","bundle_sha256":"6e82491bfc282689284e693f5dfc9712e2c9a0524d1d58f8caa0e09c389c469f"}}