{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YTFCIHKBQGDETH5C6ZPCZYXEZ2","short_pith_number":"pith:YTFCIHKB","schema_version":"1.0","canonical_sha256":"c4ca241d418186499fa2f65e2ce2e4ce9d0d950f0985e875d79ad9d9996ab631","source":{"kind":"arxiv","id":"2607.29122","version":1},"attestation_state":"computed","paper":{"title":"A Frozen Pixel-Space Diffusion Model Can Guide Itself with Its Own Samples","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bihan Wen, Chong Wang, Jiahao Nie, Kailai Zhou, Lanqing Guo, Zixuan Fu","submitted_at":"2026-07-31T07:52:08Z","abstract_excerpt":"Pixel-space diffusion models aim to learn an end-to-end generator directly over raw pixels. This is challenging because a single model must capture both global structure and local texture in the same high-dimensional space. While recent work improves pixel diffusion through alternative prediction targets, training objectives, and architectures, these advances typically require training a new model from scratch. We show there is a cheaper, complementary strategy: \\textbf{a frozen, pretrained pixel diffusion model can guide itself}. Our key observation is that intermediate layers of a pretrained"},"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":"2607.29122","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-31T07:52:08Z","cross_cats_sorted":[],"title_canon_sha256":"4b2457ac3ce08efd7fd8b3c5bcfc4045a61ca22c9ed44627d4a78337e5712989","abstract_canon_sha256":"87379d337c285a2dfe7269b3989455e874dda2f7582565dcf47f2818e6588741"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-03T01:19:24.422681Z","signature_b64":"xFMvjQ7NFtZlfoDj88vLuODFYCJ/OTs3kzO+HRftk0JH9iUEXVxTKEd5+7M0ACWzFDVWD7YDAAEKI5YMjPRPAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4ca241d418186499fa2f65e2ce2e4ce9d0d950f0985e875d79ad9d9996ab631","last_reissued_at":"2026-08-03T01:19:24.421249Z","signature_status":"signed_v1","first_computed_at":"2026-08-03T01:19:24.421249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Frozen Pixel-Space Diffusion Model Can Guide Itself with Its Own Samples","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bihan Wen, Chong Wang, Jiahao Nie, Kailai Zhou, Lanqing Guo, Zixuan Fu","submitted_at":"2026-07-31T07:52:08Z","abstract_excerpt":"Pixel-space diffusion models aim to learn an end-to-end generator directly over raw pixels. This is challenging because a single model must capture both global structure and local texture in the same high-dimensional space. While recent work improves pixel diffusion through alternative prediction targets, training objectives, and architectures, these advances typically require training a new model from scratch. We show there is a cheaper, complementary strategy: \\textbf{a frozen, pretrained pixel diffusion model can guide itself}. Our key observation is that intermediate layers of a pretrained"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.29122","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/2607.29122/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":"2607.29122","created_at":"2026-08-03T01:19:24.422156+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.29122v1","created_at":"2026-08-03T01:19:24.422156+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.29122","created_at":"2026-08-03T01:19:24.422156+00:00"},{"alias_kind":"pith_short_12","alias_value":"YTFCIHKBQGDE","created_at":"2026-08-03T01:19:24.422156+00:00"},{"alias_kind":"pith_short_16","alias_value":"YTFCIHKBQGDETH5C","created_at":"2026-08-03T01:19:24.422156+00:00"},{"alias_kind":"pith_short_8","alias_value":"YTFCIHKB","created_at":"2026-08-03T01:19:24.422156+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/YTFCIHKBQGDETH5C6ZPCZYXEZ2","json":"https://pith.science/pith/YTFCIHKBQGDETH5C6ZPCZYXEZ2.json","graph_json":"https://pith.science/api/pith-number/YTFCIHKBQGDETH5C6ZPCZYXEZ2/graph.json","events_json":"https://pith.science/api/pith-number/YTFCIHKBQGDETH5C6ZPCZYXEZ2/events.json","paper":"https://pith.science/paper/YTFCIHKB"},"agent_actions":{"view_html":"https://pith.science/pith/YTFCIHKBQGDETH5C6ZPCZYXEZ2","download_json":"https://pith.science/pith/YTFCIHKBQGDETH5C6ZPCZYXEZ2.json","view_paper":"https://pith.science/paper/YTFCIHKB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.29122&json=true","fetch_graph":"https://pith.science/api/pith-number/YTFCIHKBQGDETH5C6ZPCZYXEZ2/graph.json","fetch_events":"https://pith.science/api/pith-number/YTFCIHKBQGDETH5C6ZPCZYXEZ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YTFCIHKBQGDETH5C6ZPCZYXEZ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YTFCIHKBQGDETH5C6ZPCZYXEZ2/action/storage_attestation","attest_author":"https://pith.science/pith/YTFCIHKBQGDETH5C6ZPCZYXEZ2/action/author_attestation","sign_citation":"https://pith.science/pith/YTFCIHKBQGDETH5C6ZPCZYXEZ2/action/citation_signature","submit_replication":"https://pith.science/pith/YTFCIHKBQGDETH5C6ZPCZYXEZ2/action/replication_record"}},"created_at":"2026-08-03T01:19:24.422156+00:00","updated_at":"2026-08-03T01:19:24.422156+00:00"}