{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XBRBAFF32J3CJMITWK2BMV3JDR","short_pith_number":"pith:XBRBAFF3","schema_version":"1.0","canonical_sha256":"b8621014bbd27624b113b2b41657691c7f5a95e5c259b6e19966832f2d580b53","source":{"kind":"arxiv","id":"2406.19589","version":1},"attestation_state":"computed","paper":{"title":"Network Bending of Diffusion Models for Audio-Visual Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG","cs.MM","eess.AS"],"primary_cat":"cs.SD","authors_text":"Carmine Emanuele Cella, David Ban, Luke Dzwonczyk","submitted_at":"2024-06-28T00:39:17Z","abstract_excerpt":"In this paper we present the first steps towards the creation of a tool which enables artists to create music visualizations using pre-trained, generative, machine learning models. First, we investigate the application of network bending, the process of applying transforms within the layers of a generative network, to image generation diffusion models by utilizing a range of point-wise, tensor-wise, and morphological operators. We identify a number of visual effects that result from various operators, including some that are not easily recreated with standard image editing tools. We find that "},"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":"2406.19589","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SD","submitted_at":"2024-06-28T00:39:17Z","cross_cats_sorted":["cs.LG","cs.MM","eess.AS"],"title_canon_sha256":"fffa044665346f8f446f7d5201c92b5f99debae008c2dd6673b0a99931d8b29b","abstract_canon_sha256":"e70730cd03b8e77c43e553afb76e401cee6d7090de95bb604f95b8ade0147deb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:37:42.097574Z","signature_b64":"SGjh+ldH3RtfTFTdgaUc4qQ7tl4G3/LDXEZCSCqyxUhh8YMEGZmjUO7/L9QlxFGWNaoaGIDmin8KDqXxTuj9CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8621014bbd27624b113b2b41657691c7f5a95e5c259b6e19966832f2d580b53","last_reissued_at":"2026-07-05T08:37:42.097177Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:37:42.097177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Network Bending of Diffusion Models for Audio-Visual Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG","cs.MM","eess.AS"],"primary_cat":"cs.SD","authors_text":"Carmine Emanuele Cella, David Ban, Luke Dzwonczyk","submitted_at":"2024-06-28T00:39:17Z","abstract_excerpt":"In this paper we present the first steps towards the creation of a tool which enables artists to create music visualizations using pre-trained, generative, machine learning models. First, we investigate the application of network bending, the process of applying transforms within the layers of a generative network, to image generation diffusion models by utilizing a range of point-wise, tensor-wise, and morphological operators. We identify a number of visual effects that result from various operators, including some that are not easily recreated with standard image editing tools. We find that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.19589","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/2406.19589/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":"2406.19589","created_at":"2026-07-05T08:37:42.097229+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.19589v1","created_at":"2026-07-05T08:37:42.097229+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.19589","created_at":"2026-07-05T08:37:42.097229+00:00"},{"alias_kind":"pith_short_12","alias_value":"XBRBAFF32J3C","created_at":"2026-07-05T08:37:42.097229+00:00"},{"alias_kind":"pith_short_16","alias_value":"XBRBAFF32J3CJMIT","created_at":"2026-07-05T08:37:42.097229+00:00"},{"alias_kind":"pith_short_8","alias_value":"XBRBAFF3","created_at":"2026-07-05T08:37:42.097229+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.20936","citing_title":"AttentionBender: Manipulating Cross-Attention in Video Diffusion Transformers as a Creative Probe","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XBRBAFF32J3CJMITWK2BMV3JDR","json":"https://pith.science/pith/XBRBAFF32J3CJMITWK2BMV3JDR.json","graph_json":"https://pith.science/api/pith-number/XBRBAFF32J3CJMITWK2BMV3JDR/graph.json","events_json":"https://pith.science/api/pith-number/XBRBAFF32J3CJMITWK2BMV3JDR/events.json","paper":"https://pith.science/paper/XBRBAFF3"},"agent_actions":{"view_html":"https://pith.science/pith/XBRBAFF32J3CJMITWK2BMV3JDR","download_json":"https://pith.science/pith/XBRBAFF32J3CJMITWK2BMV3JDR.json","view_paper":"https://pith.science/paper/XBRBAFF3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.19589&json=true","fetch_graph":"https://pith.science/api/pith-number/XBRBAFF32J3CJMITWK2BMV3JDR/graph.json","fetch_events":"https://pith.science/api/pith-number/XBRBAFF32J3CJMITWK2BMV3JDR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XBRBAFF32J3CJMITWK2BMV3JDR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XBRBAFF32J3CJMITWK2BMV3JDR/action/storage_attestation","attest_author":"https://pith.science/pith/XBRBAFF32J3CJMITWK2BMV3JDR/action/author_attestation","sign_citation":"https://pith.science/pith/XBRBAFF32J3CJMITWK2BMV3JDR/action/citation_signature","submit_replication":"https://pith.science/pith/XBRBAFF32J3CJMITWK2BMV3JDR/action/replication_record"}},"created_at":"2026-07-05T08:37:42.097229+00:00","updated_at":"2026-07-05T08:37:42.097229+00:00"}