{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4MBOMRGU7UJGJTVAEIRRAUD4XU","short_pith_number":"pith:4MBOMRGU","schema_version":"1.0","canonical_sha256":"e302e644d4fd1264cea0222310507cbd00547151f0ed8020bb0597d8425cdba9","source":{"kind":"arxiv","id":"2607.17634","version":1},"attestation_state":"computed","paper":{"title":"MixDiffusion: Mixing Diffusion-based Uni-condition Text-to-Image Generation Models for Multi-condition Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bowen Xiao, Liang Han, Lin Xu, Liqiang Nie, Pengcheng Wan","submitted_at":"2026-07-20T07:40:56Z","abstract_excerpt":"Recent advances in text-to-image (T2I) generation have enabled controllable image synthesis by incorporating conditions beyond text. However, most existing diffusion-based methods are limited to a single type of control condition (e.g., bounding boxes or keypoints), which restricts their flexibility. To address this limitation, we propose MixDiffusion, a training-free diffusion framework for multi-condition T2I generation. MixDiffusion theoretically supports an arbitrary number of control conditions, including bounding boxes, keypoints, sketches, depth maps, reference images, and text, by coll"},"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":"2607.17634","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-20T07:40:56Z","cross_cats_sorted":[],"title_canon_sha256":"db8aec4909ad53b6570991e40a41146c2a7b1c0fe0ad2c235665db32dacade15","abstract_canon_sha256":"a5d1bd3f16de9df1814ed1ba108c9c752118e71a22faf540629c6465a8149e75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T01:21:56.435106Z","signature_b64":"UdMZ/4dB0XDpd67MpnBiOmI7Vh2iJ0dhhOsVJs1rIKvEz1CBRXWM2JAD5qPQxjVe2NY2OI7D8TBcEepBCq+ICQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e302e644d4fd1264cea0222310507cbd00547151f0ed8020bb0597d8425cdba9","last_reissued_at":"2026-07-21T01:21:56.434295Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T01:21:56.434295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MixDiffusion: Mixing Diffusion-based Uni-condition Text-to-Image Generation Models for Multi-condition Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bowen Xiao, Liang Han, Lin Xu, Liqiang Nie, Pengcheng Wan","submitted_at":"2026-07-20T07:40:56Z","abstract_excerpt":"Recent advances in text-to-image (T2I) generation have enabled controllable image synthesis by incorporating conditions beyond text. However, most existing diffusion-based methods are limited to a single type of control condition (e.g., bounding boxes or keypoints), which restricts their flexibility. To address this limitation, we propose MixDiffusion, a training-free diffusion framework for multi-condition T2I generation. MixDiffusion theoretically supports an arbitrary number of control conditions, including bounding boxes, keypoints, sketches, depth maps, reference images, and text, by coll"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17634","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/2607.17634/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":"2607.17634","created_at":"2026-07-21T01:21:56.434716+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.17634v1","created_at":"2026-07-21T01:21:56.434716+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17634","created_at":"2026-07-21T01:21:56.434716+00:00"},{"alias_kind":"pith_short_12","alias_value":"4MBOMRGU7UJG","created_at":"2026-07-21T01:21:56.434716+00:00"},{"alias_kind":"pith_short_16","alias_value":"4MBOMRGU7UJGJTVA","created_at":"2026-07-21T01:21:56.434716+00:00"},{"alias_kind":"pith_short_8","alias_value":"4MBOMRGU","created_at":"2026-07-21T01:21:56.434716+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/4MBOMRGU7UJGJTVAEIRRAUD4XU","json":"https://pith.science/pith/4MBOMRGU7UJGJTVAEIRRAUD4XU.json","graph_json":"https://pith.science/api/pith-number/4MBOMRGU7UJGJTVAEIRRAUD4XU/graph.json","events_json":"https://pith.science/api/pith-number/4MBOMRGU7UJGJTVAEIRRAUD4XU/events.json","paper":"https://pith.science/paper/4MBOMRGU"},"agent_actions":{"view_html":"https://pith.science/pith/4MBOMRGU7UJGJTVAEIRRAUD4XU","download_json":"https://pith.science/pith/4MBOMRGU7UJGJTVAEIRRAUD4XU.json","view_paper":"https://pith.science/paper/4MBOMRGU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.17634&json=true","fetch_graph":"https://pith.science/api/pith-number/4MBOMRGU7UJGJTVAEIRRAUD4XU/graph.json","fetch_events":"https://pith.science/api/pith-number/4MBOMRGU7UJGJTVAEIRRAUD4XU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4MBOMRGU7UJGJTVAEIRRAUD4XU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4MBOMRGU7UJGJTVAEIRRAUD4XU/action/storage_attestation","attest_author":"https://pith.science/pith/4MBOMRGU7UJGJTVAEIRRAUD4XU/action/author_attestation","sign_citation":"https://pith.science/pith/4MBOMRGU7UJGJTVAEIRRAUD4XU/action/citation_signature","submit_replication":"https://pith.science/pith/4MBOMRGU7UJGJTVAEIRRAUD4XU/action/replication_record"}},"created_at":"2026-07-21T01:21:56.434716+00:00","updated_at":"2026-07-21T01:21:56.434716+00:00"}