{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ODEU2THFNCGLROLJV74VH3JYKA","short_pith_number":"pith:ODEU2THF","schema_version":"1.0","canonical_sha256":"70c94d4ce5688cb8b969aff953ed385008fec209db94bef8390f8baf383d123b","source":{"kind":"arxiv","id":"2312.00858","version":2},"attestation_state":"computed","paper":{"title":"DeepCache: Accelerating Diffusion Models for Free","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gongfan Fang, Xinchao Wang, Xinyin Ma","submitted_at":"2023-12-01T17:01:06Z","abstract_excerpt":"Diffusion models have recently gained unprecedented attention in the field of image synthesis due to their remarkable generative capabilities. Notwithstanding their prowess, these models often incur substantial computational costs, primarily attributed to the sequential denoising process and cumbersome model size. Traditional methods for compressing diffusion models typically involve extensive retraining, presenting cost and feasibility challenges. In this paper, we introduce DeepCache, a novel training-free paradigm that accelerates diffusion models from the perspective of model architecture."},"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":"2312.00858","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-01T17:01:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"dbde0c12c036f9ae6f24a7220960381fa2cb0390318129591d5e927197119099","abstract_canon_sha256":"a1526a817e8d807e82f5e2552dfea621ae383ed3692a378e2e3c6d486ab520b7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:21:46.581018Z","signature_b64":"6z6rdiy1sJMi9RzCI/57HiL8dRO+LUlrQZHwMRxhYnooWRofYmdK9aB8SfYiBLz+Wih1Gl9HTpUkRvN8ZDBSAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70c94d4ce5688cb8b969aff953ed385008fec209db94bef8390f8baf383d123b","last_reissued_at":"2026-07-05T07:21:46.580446Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:21:46.580446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepCache: Accelerating Diffusion Models for Free","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gongfan Fang, Xinchao Wang, Xinyin Ma","submitted_at":"2023-12-01T17:01:06Z","abstract_excerpt":"Diffusion models have recently gained unprecedented attention in the field of image synthesis due to their remarkable generative capabilities. Notwithstanding their prowess, these models often incur substantial computational costs, primarily attributed to the sequential denoising process and cumbersome model size. Traditional methods for compressing diffusion models typically involve extensive retraining, presenting cost and feasibility challenges. In this paper, we introduce DeepCache, a novel training-free paradigm that accelerates diffusion models from the perspective of model architecture."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.00858","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/2312.00858/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":"2312.00858","created_at":"2026-07-05T07:21:46.580527+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.00858v2","created_at":"2026-07-05T07:21:46.580527+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.00858","created_at":"2026-07-05T07:21:46.580527+00:00"},{"alias_kind":"pith_short_12","alias_value":"ODEU2THFNCGL","created_at":"2026-07-05T07:21:46.580527+00:00"},{"alias_kind":"pith_short_16","alias_value":"ODEU2THFNCGLROLJ","created_at":"2026-07-05T07:21:46.580527+00:00"},{"alias_kind":"pith_short_8","alias_value":"ODEU2THF","created_at":"2026-07-05T07:21:46.580527+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12280","citing_title":"Holding the FP8 Quality Ceiling at 8-Bit Weights and Activations: INT8 and GGUF Post-Training Quantization of Ideogram 4.0 for Consumer GPUs","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10046","citing_title":"Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31026","citing_title":"OTCache: Optimal Transport for Geometry-Aware Caching in Diffusion Models","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26632","citing_title":"RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models","ref_index":41,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ODEU2THFNCGLROLJV74VH3JYKA","json":"https://pith.science/pith/ODEU2THFNCGLROLJV74VH3JYKA.json","graph_json":"https://pith.science/api/pith-number/ODEU2THFNCGLROLJV74VH3JYKA/graph.json","events_json":"https://pith.science/api/pith-number/ODEU2THFNCGLROLJV74VH3JYKA/events.json","paper":"https://pith.science/paper/ODEU2THF"},"agent_actions":{"view_html":"https://pith.science/pith/ODEU2THFNCGLROLJV74VH3JYKA","download_json":"https://pith.science/pith/ODEU2THFNCGLROLJV74VH3JYKA.json","view_paper":"https://pith.science/paper/ODEU2THF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.00858&json=true","fetch_graph":"https://pith.science/api/pith-number/ODEU2THFNCGLROLJV74VH3JYKA/graph.json","fetch_events":"https://pith.science/api/pith-number/ODEU2THFNCGLROLJV74VH3JYKA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ODEU2THFNCGLROLJV74VH3JYKA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ODEU2THFNCGLROLJV74VH3JYKA/action/storage_attestation","attest_author":"https://pith.science/pith/ODEU2THFNCGLROLJV74VH3JYKA/action/author_attestation","sign_citation":"https://pith.science/pith/ODEU2THFNCGLROLJV74VH3JYKA/action/citation_signature","submit_replication":"https://pith.science/pith/ODEU2THFNCGLROLJV74VH3JYKA/action/replication_record"}},"created_at":"2026-07-05T07:21:46.580527+00:00","updated_at":"2026-07-05T07:21:46.580527+00:00"}