{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZASBYUPBHYG35FNDUZHJ57D4UV","short_pith_number":"pith:ZASBYUPB","schema_version":"1.0","canonical_sha256":"c8241c51e13e0dbe95a3a64e9efc7ca55130124af4660f91b1fbf15758e235e7","source":{"kind":"arxiv","id":"2406.09416","version":2},"attestation_state":"computed","paper":{"title":"Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ju He, Liang-Chieh Chen, Qihang Yu, Qihao Liu, Xiaohui Shen, Zhanpeng Zeng","submitted_at":"2024-06-13T17:59:58Z","abstract_excerpt":"This paper presents innovative enhancements to diffusion models by integrating a novel multi-resolution network and time-dependent layer normalization. Diffusion models have gained prominence for their effectiveness in high-fidelity image generation. While conventional approaches rely on convolutional U-Net architectures, recent Transformer-based designs have demonstrated superior performance and scalability. However, Transformer architectures, which tokenize input data (via \"patchification\"), face a trade-off between visual fidelity and computational complexity due to the quadratic nature of "},"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":"2406.09416","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-13T17:59:58Z","cross_cats_sorted":[],"title_canon_sha256":"5ef5772e1a9bbde403da788dac97a6dc7540cf54dd387035d3b8d3eb0184c051","abstract_canon_sha256":"fb6da71e54cd5469910d2d5ec1f2fdc3cddedd9e19654851692a07edca6fd862"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:41:37.941830Z","signature_b64":"TFdVqfTYSXEurJ5EqwEiiJM5jSa8WkeNSbUECPZskXrKDzBGdFcBbvS+YdfvY4aUuA2OsrVsPQSVhSRUf/xVAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c8241c51e13e0dbe95a3a64e9efc7ca55130124af4660f91b1fbf15758e235e7","last_reissued_at":"2026-07-05T09:41:37.941418Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:41:37.941418Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ju He, Liang-Chieh Chen, Qihang Yu, Qihao Liu, Xiaohui Shen, Zhanpeng Zeng","submitted_at":"2024-06-13T17:59:58Z","abstract_excerpt":"This paper presents innovative enhancements to diffusion models by integrating a novel multi-resolution network and time-dependent layer normalization. Diffusion models have gained prominence for their effectiveness in high-fidelity image generation. While conventional approaches rely on convolutional U-Net architectures, recent Transformer-based designs have demonstrated superior performance and scalability. However, Transformer architectures, which tokenize input data (via \"patchification\"), face a trade-off between visual fidelity and computational complexity due to the quadratic nature of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.09416","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/2406.09416/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":"2406.09416","created_at":"2026-07-05T09:41:37.941474+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.09416v2","created_at":"2026-07-05T09:41:37.941474+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.09416","created_at":"2026-07-05T09:41:37.941474+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZASBYUPBHYG3","created_at":"2026-07-05T09:41:37.941474+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZASBYUPBHYG35FND","created_at":"2026-07-05T09:41:37.941474+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZASBYUPB","created_at":"2026-07-05T09:41:37.941474+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZASBYUPBHYG35FNDUZHJ57D4UV","json":"https://pith.science/pith/ZASBYUPBHYG35FNDUZHJ57D4UV.json","graph_json":"https://pith.science/api/pith-number/ZASBYUPBHYG35FNDUZHJ57D4UV/graph.json","events_json":"https://pith.science/api/pith-number/ZASBYUPBHYG35FNDUZHJ57D4UV/events.json","paper":"https://pith.science/paper/ZASBYUPB"},"agent_actions":{"view_html":"https://pith.science/pith/ZASBYUPBHYG35FNDUZHJ57D4UV","download_json":"https://pith.science/pith/ZASBYUPBHYG35FNDUZHJ57D4UV.json","view_paper":"https://pith.science/paper/ZASBYUPB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.09416&json=true","fetch_graph":"https://pith.science/api/pith-number/ZASBYUPBHYG35FNDUZHJ57D4UV/graph.json","fetch_events":"https://pith.science/api/pith-number/ZASBYUPBHYG35FNDUZHJ57D4UV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZASBYUPBHYG35FNDUZHJ57D4UV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZASBYUPBHYG35FNDUZHJ57D4UV/action/storage_attestation","attest_author":"https://pith.science/pith/ZASBYUPBHYG35FNDUZHJ57D4UV/action/author_attestation","sign_citation":"https://pith.science/pith/ZASBYUPBHYG35FNDUZHJ57D4UV/action/citation_signature","submit_replication":"https://pith.science/pith/ZASBYUPBHYG35FNDUZHJ57D4UV/action/replication_record"}},"created_at":"2026-07-05T09:41:37.941474+00:00","updated_at":"2026-07-05T09:41:37.941474+00:00"}