{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2IVZ7NDVJ5EXQRLLSRAUUZX4OE","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":"e77b5a9cce567dec827c96549a5843c7f4872b9c86c866bd52e1f47c370043aa","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-15T23:08:52Z","title_canon_sha256":"eb8048e35dcece31f7c664abb53f0957bc4ffbd23c5004fd2ea2a86464f8ab84"},"schema_version":"1.0","source":{"id":"2505.10743","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.10743","created_at":"2026-07-05T11:04:04Z"},{"alias_kind":"arxiv_version","alias_value":"2505.10743v1","created_at":"2026-07-05T11:04:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10743","created_at":"2026-07-05T11:04:04Z"},{"alias_kind":"pith_short_12","alias_value":"2IVZ7NDVJ5EX","created_at":"2026-07-05T11:04:04Z"},{"alias_kind":"pith_short_16","alias_value":"2IVZ7NDVJ5EXQRLL","created_at":"2026-07-05T11:04:04Z"},{"alias_kind":"pith_short_8","alias_value":"2IVZ7NDV","created_at":"2026-07-05T11:04:04Z"}],"graph_snapshots":[{"event_id":"sha256:133a696e2d8f11f911910ceea0eae0474ebee29b4c407bd783345da1a9c4a256","target":"graph","created_at":"2026-07-05T11:04:04Z","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/2505.10743/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in text-to-image diffusion models, particularly Stable Diffusion, have enabled the generation of highly detailed and semantically rich images. However, personalizing these models to represent novel subjects based on a few reference images remains challenging. This often leads to catastrophic forgetting, overfitting, or large computational overhead.We propose a two-stage pipeline that addresses these limitations by leveraging LoRA-based fine-tuning on the attention weights within the U-Net of the Stable Diffusion XL (SDXL) model. First, we use the unmodified SDXL to generate a g","authors_text":"Amritanshu Tiwari, Cherish Puniani, Kaustubh Sharma, Ojasva Nema","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-15T23:08:52Z","title":"IMAGE-ALCHEMY: Advancing subject fidelity in personalised text-to-image generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10743","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":"sha256:1ba381e31ec97d5f66f8760f9cdf54f6aed51f15e10092b46c2a6fb422306661","target":"record","created_at":"2026-07-05T11:04:04Z","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":"e77b5a9cce567dec827c96549a5843c7f4872b9c86c866bd52e1f47c370043aa","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-15T23:08:52Z","title_canon_sha256":"eb8048e35dcece31f7c664abb53f0957bc4ffbd23c5004fd2ea2a86464f8ab84"},"schema_version":"1.0","source":{"id":"2505.10743","kind":"arxiv","version":1}},"canonical_sha256":"d22b9fb4754f4978456b94414a66fc710931a188e7d046c6dab3a0866941ca34","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d22b9fb4754f4978456b94414a66fc710931a188e7d046c6dab3a0866941ca34","first_computed_at":"2026-07-05T11:04:04.736028Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:04:04.736028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PG6zzEkciFhkfknAkD9taQ2NRZaKK1TLC9PuEackGwcdaNNZjmwn6NW/wQJMLnapIi6MzQqnDNxQjIkUCQLXBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:04:04.736485Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.10743","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1ba381e31ec97d5f66f8760f9cdf54f6aed51f15e10092b46c2a6fb422306661","sha256:133a696e2d8f11f911910ceea0eae0474ebee29b4c407bd783345da1a9c4a256"],"state_sha256":"961fd25a01d71900560cf383361b8be068f1a6107656a964bb94d1e7eae101f9"}