{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:TJPHZEXHEQINRVFLIDRZIUQ37B","short_pith_number":"pith:TJPHZEXH","canonical_record":{"source":{"id":"2305.01115","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-01T23:03:37Z","cross_cats_sorted":[],"title_canon_sha256":"4093a729c002fbf699b9a23f285e8e248022318fa0529ecb13e55a6e5f2319c1","abstract_canon_sha256":"68b63608a4267547a90efa24b0f119727c0b012cf0842e403a046ba733b5073c"},"schema_version":"1.0"},"canonical_sha256":"9a5e7c92e72410d8d4ab40e394521bf861981000383c9763d626fa5284ef8480","source":{"kind":"arxiv","id":"2305.01115","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.01115","created_at":"2026-07-05T07:02:28Z"},{"alias_kind":"arxiv_version","alias_value":"2305.01115v2","created_at":"2026-07-05T07:02:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.01115","created_at":"2026-07-05T07:02:28Z"},{"alias_kind":"pith_short_12","alias_value":"TJPHZEXHEQIN","created_at":"2026-07-05T07:02:28Z"},{"alias_kind":"pith_short_16","alias_value":"TJPHZEXHEQINRVFL","created_at":"2026-07-05T07:02:28Z"},{"alias_kind":"pith_short_8","alias_value":"TJPHZEXH","created_at":"2026-07-05T07:02:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:TJPHZEXHEQINRVFLIDRZIUQ37B","target":"record","payload":{"canonical_record":{"source":{"id":"2305.01115","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-01T23:03:37Z","cross_cats_sorted":[],"title_canon_sha256":"4093a729c002fbf699b9a23f285e8e248022318fa0529ecb13e55a6e5f2319c1","abstract_canon_sha256":"68b63608a4267547a90efa24b0f119727c0b012cf0842e403a046ba733b5073c"},"schema_version":"1.0"},"canonical_sha256":"9a5e7c92e72410d8d4ab40e394521bf861981000383c9763d626fa5284ef8480","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:02:28.573053Z","signature_b64":"Pd9HhtIejEjzG4UDisCSNqFZmzI3RkIq/g8LnmfMvhZPg3iShYPfr68qOHKgcdlgK6e9JziGS+u5hAov+MCJCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a5e7c92e72410d8d4ab40e394521bf861981000383c9763d626fa5284ef8480","last_reissued_at":"2026-07-05T07:02:28.572547Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:02:28.572547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.01115","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:02:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A7tddizRj0KJrZm+en8HBEmQw8ER9yKXoctKPaGpewNM2rhGgHqzBpfl6wbNo7zWhrQkJiZYzYVHBbgMr5MLCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:05:46.208268Z"},"content_sha256":"35790b9355811b2bac17912c28d0a3c0038ce181d41b1419f84ef59a80c1e69c","schema_version":"1.0","event_id":"sha256:35790b9355811b2bac17912c28d0a3c0038ce181d41b1419f84ef59a80c1e69c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:TJPHZEXHEQINRVFLIDRZIUQ37B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"In-Context Learning Unlocked for Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mingyuan Zhou, Pengcheng He, Weizhu Chen, Yadong Lu, Yelong Shen, Yifan Jiang, Zhangyang Wang, Zhendong Wang","submitted_at":"2023-05-01T23:03:37Z","abstract_excerpt":"We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on a new query image following the text guidance. To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The diffusion model is trained jointly over six different tasks using these prom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.01115","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/2305.01115/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:02:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dl6Tazajb5shi5gNRapwYNFCSdis0OjFHirZLaAkhNP7lVsnfVUOXLQKdJRWOvL05ju2deOdsfn4gR/fbt2PBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:05:46.208762Z"},"content_sha256":"35216f63f11da71e151477e80232ac4a771cfb59c6b928c99299569728d00fc1","schema_version":"1.0","event_id":"sha256:35216f63f11da71e151477e80232ac4a771cfb59c6b928c99299569728d00fc1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TJPHZEXHEQINRVFLIDRZIUQ37B/bundle.json","state_url":"https://pith.science/pith/TJPHZEXHEQINRVFLIDRZIUQ37B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TJPHZEXHEQINRVFLIDRZIUQ37B/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T21:05:46Z","links":{"resolver":"https://pith.science/pith/TJPHZEXHEQINRVFLIDRZIUQ37B","bundle":"https://pith.science/pith/TJPHZEXHEQINRVFLIDRZIUQ37B/bundle.json","state":"https://pith.science/pith/TJPHZEXHEQINRVFLIDRZIUQ37B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TJPHZEXHEQINRVFLIDRZIUQ37B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:TJPHZEXHEQINRVFLIDRZIUQ37B","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"68b63608a4267547a90efa24b0f119727c0b012cf0842e403a046ba733b5073c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-01T23:03:37Z","title_canon_sha256":"4093a729c002fbf699b9a23f285e8e248022318fa0529ecb13e55a6e5f2319c1"},"schema_version":"1.0","source":{"id":"2305.01115","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.01115","created_at":"2026-07-05T07:02:28Z"},{"alias_kind":"arxiv_version","alias_value":"2305.01115v2","created_at":"2026-07-05T07:02:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.01115","created_at":"2026-07-05T07:02:28Z"},{"alias_kind":"pith_short_12","alias_value":"TJPHZEXHEQIN","created_at":"2026-07-05T07:02:28Z"},{"alias_kind":"pith_short_16","alias_value":"TJPHZEXHEQINRVFL","created_at":"2026-07-05T07:02:28Z"},{"alias_kind":"pith_short_8","alias_value":"TJPHZEXH","created_at":"2026-07-05T07:02:28Z"}],"graph_snapshots":[{"event_id":"sha256:35216f63f11da71e151477e80232ac4a771cfb59c6b928c99299569728d00fc1","target":"graph","created_at":"2026-07-05T07:02:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2305.01115/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on a new query image following the text guidance. To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The diffusion model is trained jointly over six different tasks using these prom","authors_text":"Mingyuan Zhou, Pengcheng He, Weizhu Chen, Yadong Lu, Yelong Shen, Yifan Jiang, Zhangyang Wang, Zhendong Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-01T23:03:37Z","title":"In-Context Learning Unlocked for Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.01115","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:35790b9355811b2bac17912c28d0a3c0038ce181d41b1419f84ef59a80c1e69c","target":"record","created_at":"2026-07-05T07:02:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"68b63608a4267547a90efa24b0f119727c0b012cf0842e403a046ba733b5073c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-01T23:03:37Z","title_canon_sha256":"4093a729c002fbf699b9a23f285e8e248022318fa0529ecb13e55a6e5f2319c1"},"schema_version":"1.0","source":{"id":"2305.01115","kind":"arxiv","version":2}},"canonical_sha256":"9a5e7c92e72410d8d4ab40e394521bf861981000383c9763d626fa5284ef8480","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9a5e7c92e72410d8d4ab40e394521bf861981000383c9763d626fa5284ef8480","first_computed_at":"2026-07-05T07:02:28.572547Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:02:28.572547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pd9HhtIejEjzG4UDisCSNqFZmzI3RkIq/g8LnmfMvhZPg3iShYPfr68qOHKgcdlgK6e9JziGS+u5hAov+MCJCg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:02:28.573053Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.01115","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:35790b9355811b2bac17912c28d0a3c0038ce181d41b1419f84ef59a80c1e69c","sha256:35216f63f11da71e151477e80232ac4a771cfb59c6b928c99299569728d00fc1"],"state_sha256":"c55f0b1a2e17d8bb98bd9941b6e9b2f85d02fb1abaae5d3ae158c804baff5ddd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UsNrBXK3boKJt/DVc143rfhlfd3ODsclDXCQ1hCaTlWxAztkhThhiUuQNIFD4ntrZumgtAVE2zoUGb3RGsQlAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T21:05:46.212955Z","bundle_sha256":"b8dcd6a3317eedc524c06c250a03a874f039b478aadaca7cf1d4b624295c4717"}}