{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:V57Z5KH4RVWFMFR4NRG7IAOYTQ","short_pith_number":"pith:V57Z5KH4","schema_version":"1.0","canonical_sha256":"af7f9ea8fc8d6c56163c6c4df401d89c329f77b4fdb9667d57fbee5b9e813be1","source":{"kind":"arxiv","id":"2409.16535","version":1},"attestation_state":"computed","paper":{"title":"Prompt Sliders for Fine-Grained Control, Editing and Erasing of Concepts in Diffusion Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Deepak Sridhar, Nuno Vasconcelos","submitted_at":"2024-09-25T01:02:30Z","abstract_excerpt":"Diffusion models have recently surpassed GANs in image synthesis and editing, offering superior image quality and diversity. However, achieving precise control over attributes in generated images remains a challenge. Concept Sliders introduced a method for fine-grained image control and editing by learning concepts (attributes/objects). However, this approach adds parameters and increases inference time due to the loading and unloading of Low-Rank Adapters (LoRAs) used for learning concepts. These adapters are model-specific and require retraining for different architectures, such as Stable Di"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2409.16535","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-25T01:02:30Z","cross_cats_sorted":[],"title_canon_sha256":"144f5f6468d34e9b435140a70b96bf4788da317fa8e30f4680822f1e78c0a2e6","abstract_canon_sha256":"8d3605af9bafb247f21519a19197491269865d3daf1b10fec9c7fe9d0504dc0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:11:35.492714Z","signature_b64":"v0BupikLYp6jtMFeWvS6/HukpOUFu6QLu+gibjznV1PWxuSavsWQUFO35u/AOL+u5+5m2LcTyB4hfOoc3x60BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af7f9ea8fc8d6c56163c6c4df401d89c329f77b4fdb9667d57fbee5b9e813be1","last_reissued_at":"2026-07-05T09:11:35.492247Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:11:35.492247Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prompt Sliders for Fine-Grained Control, Editing and Erasing of Concepts in Diffusion Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Deepak Sridhar, Nuno Vasconcelos","submitted_at":"2024-09-25T01:02:30Z","abstract_excerpt":"Diffusion models have recently surpassed GANs in image synthesis and editing, offering superior image quality and diversity. However, achieving precise control over attributes in generated images remains a challenge. Concept Sliders introduced a method for fine-grained image control and editing by learning concepts (attributes/objects). However, this approach adds parameters and increases inference time due to the loading and unloading of Low-Rank Adapters (LoRAs) used for learning concepts. These adapters are model-specific and require retraining for different architectures, such as Stable Di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.16535","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/2409.16535/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2409.16535","created_at":"2026-07-05T09:11:35.492304+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.16535v1","created_at":"2026-07-05T09:11:35.492304+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.16535","created_at":"2026-07-05T09:11:35.492304+00:00"},{"alias_kind":"pith_short_12","alias_value":"V57Z5KH4RVWF","created_at":"2026-07-05T09:11:35.492304+00:00"},{"alias_kind":"pith_short_16","alias_value":"V57Z5KH4RVWFMFR4","created_at":"2026-07-05T09:11:35.492304+00:00"},{"alias_kind":"pith_short_8","alias_value":"V57Z5KH4","created_at":"2026-07-05T09:11:35.492304+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01658","citing_title":"CoreUnlearn: Rethinking Concept Unlearning through Disentangled Component-Level Erasure in Text-guided Diffusion Models","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V57Z5KH4RVWFMFR4NRG7IAOYTQ","json":"https://pith.science/pith/V57Z5KH4RVWFMFR4NRG7IAOYTQ.json","graph_json":"https://pith.science/api/pith-number/V57Z5KH4RVWFMFR4NRG7IAOYTQ/graph.json","events_json":"https://pith.science/api/pith-number/V57Z5KH4RVWFMFR4NRG7IAOYTQ/events.json","paper":"https://pith.science/paper/V57Z5KH4"},"agent_actions":{"view_html":"https://pith.science/pith/V57Z5KH4RVWFMFR4NRG7IAOYTQ","download_json":"https://pith.science/pith/V57Z5KH4RVWFMFR4NRG7IAOYTQ.json","view_paper":"https://pith.science/paper/V57Z5KH4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.16535&json=true","fetch_graph":"https://pith.science/api/pith-number/V57Z5KH4RVWFMFR4NRG7IAOYTQ/graph.json","fetch_events":"https://pith.science/api/pith-number/V57Z5KH4RVWFMFR4NRG7IAOYTQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V57Z5KH4RVWFMFR4NRG7IAOYTQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V57Z5KH4RVWFMFR4NRG7IAOYTQ/action/storage_attestation","attest_author":"https://pith.science/pith/V57Z5KH4RVWFMFR4NRG7IAOYTQ/action/author_attestation","sign_citation":"https://pith.science/pith/V57Z5KH4RVWFMFR4NRG7IAOYTQ/action/citation_signature","submit_replication":"https://pith.science/pith/V57Z5KH4RVWFMFR4NRG7IAOYTQ/action/replication_record"}},"created_at":"2026-07-05T09:11:35.492304+00:00","updated_at":"2026-07-05T09:11:35.492304+00:00"}