{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JMIVPW7LUWYUYOOQ3K3OFKJ74J","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":"3393ab17e068050b1bb269351ff944ca36e45700757fa5c7a8be64ed0df9d300","cross_cats_sorted":["cs.CL","cs.GR"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-02T16:43:24Z","title_canon_sha256":"86418fe705989b8c5e29b6c4bcd6da0309908b999723122c13cc6404db21f5ec"},"schema_version":"1.0","source":{"id":"2410.01731","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.01731","created_at":"2026-07-05T09:14:51Z"},{"alias_kind":"arxiv_version","alias_value":"2410.01731v1","created_at":"2026-07-05T09:14:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.01731","created_at":"2026-07-05T09:14:51Z"},{"alias_kind":"pith_short_12","alias_value":"JMIVPW7LUWYU","created_at":"2026-07-05T09:14:51Z"},{"alias_kind":"pith_short_16","alias_value":"JMIVPW7LUWYUYOOQ","created_at":"2026-07-05T09:14:51Z"},{"alias_kind":"pith_short_8","alias_value":"JMIVPW7L","created_at":"2026-07-05T09:14:51Z"}],"graph_snapshots":[{"event_id":"sha256:fbd73bd312c91b4d37de57cfde2301db80386b053aba04a4eed8c25666572ebb","target":"graph","created_at":"2026-07-05T09:14:51Z","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/2410.01731/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The practical use of text-to-image generation has evolved from simple, monolithic models to complex workflows that combine multiple specialized components. While workflow-based approaches can lead to improved image quality, crafting effective workflows requires significant expertise, owing to the large number of available components, their complex inter-dependence, and their dependence on the generation prompt. Here, we introduce the novel task of prompt-adaptive workflow generation, where the goal is to automatically tailor a workflow to each user prompt. We propose two LLM-based approaches t","authors_text":"Adi Haviv, Amit H. Bermano, Daniel Cohen-Or, Gal Chechik, Rinon Gal, Yuval Alaluf","cross_cats":["cs.CL","cs.GR"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-02T16:43:24Z","title":"ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.01731","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:fa4dceffdb2a3c326303ff1b66e1ebd2c5cfe3638f61414c8ff345b2aa2f1cc3","target":"record","created_at":"2026-07-05T09:14:51Z","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":"3393ab17e068050b1bb269351ff944ca36e45700757fa5c7a8be64ed0df9d300","cross_cats_sorted":["cs.CL","cs.GR"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-02T16:43:24Z","title_canon_sha256":"86418fe705989b8c5e29b6c4bcd6da0309908b999723122c13cc6404db21f5ec"},"schema_version":"1.0","source":{"id":"2410.01731","kind":"arxiv","version":1}},"canonical_sha256":"4b1157dbeba5b14c39d0dab6e2a93fe27ae7bd67c17b0f37ab46229c8065a565","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4b1157dbeba5b14c39d0dab6e2a93fe27ae7bd67c17b0f37ab46229c8065a565","first_computed_at":"2026-07-05T09:14:51.353296Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:14:51.353296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RAiQ+1gaJbH+1vlxe7C6s2WrJHJ8VA+yp6MDfcfRqG6Z1h6SgjBEEGZptKOvqw7d+kIEU8AOxzpDyxK6nNAHBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:14:51.353782Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.01731","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fa4dceffdb2a3c326303ff1b66e1ebd2c5cfe3638f61414c8ff345b2aa2f1cc3","sha256:fbd73bd312c91b4d37de57cfde2301db80386b053aba04a4eed8c25666572ebb"],"state_sha256":"6f7879f6b1abf5cab89a0f09eeca07bb2d29f6b36d3692b773f9852dd845f4ae"}