{"as_of":"2026-08-13T08:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3392450d34619170b22238558cc64499ab80cf6ab7a1121110e1ea2bbf541d89","coverage":[{"denominator":76,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":76,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T10:28:07.718327Z","state":"measured"},{"denominator":78,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":78,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-18T19:02:01.962726Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-18T19:02:48.668466Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"cited_work":{"arxiv_id":"2411.19261","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.19261","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Improving multi-subject consistency in open-domain image genera- tion with isolation and reposition attention","venue":null,"work_id":"0f842ca7-9937-4072-9f73-6397b6b31f1d","year":2024},"citing_paper":{"arxiv_id":"2509.04123","last_updated":"2026-04-11T07:57:17Z","snapshot_observed_at":"2026-07-06T22:23:59.676849Z","submitted_at":"2025-09-04T11:37:06Z","title":"TaleDiffusion: Multi-Character Story Generation with Dialogue Rendering","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-18T19:02:01.962726Z"},"links":{"cited_paper":"/paper/2411.19261","citing_paper":"/paper/2509.04123"},"observation_digest":"sha256:9ce84164627509959d67c87464b96d23c79441aa06a46c304b7f52885b00df91","observation_id":"e2f81323-d273-46c3-8e60-651f0e556f76","resolution":{"observed_at":"2026-05-18T19:02:48.671374Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"cited_work":{"arxiv_id":"2411.19261","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.19261","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Improving multi-subject consistency in open-domain image genera- tion with isolation and reposition attention","venue":null,"work_id":"0f842ca7-9937-4072-9f73-6397b6b31f1d","year":2024},"citing_paper":{"arxiv_id":"2512.08477","last_updated":"2026-04-04T10:21:30Z","snapshot_observed_at":"2026-08-07T17:50:24.560557Z","submitted_at":"2025-12-09T10:51:45Z","title":"ContextDrag: Precise Drag-Based Image Editing via Context-Preserving Token Injection and Position-Aligned Attention","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-17T00:04:14.478054Z"},"links":{"cited_paper":"/paper/2411.19261","citing_paper":"/paper/2512.08477"},"observation_digest":"sha256:27045bf167407a1fb187c7406be0925c6c8a2ccb289da310b865e519452cc144","observation_id":"73519a3b-ca54-42ce-a7e0-83694f02dd09","resolution":{"observed_at":"2026-05-17T00:08:43.688488Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.19261/citation-record","integrity":"/paper/2411.19261/integrity","json":"/paper/2411.19261/citation-record.json","paper":"/paper/2411.19261"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:07.321078Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.321078Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:4bdc69a50661334ee8dd9ea0ea8e14b48fa607dff4fa717ebae9976402dc4896","observation_id":"53c0b224-1058-400b-9893-73414e61ada5","resolution":{"observed_at":"2026-08-12T10:28:07.321078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.896213Z","title":"Wasser- stein generative adversarial networks","venue":null,"work_id":"c49532cd-0952-487e-a8c8-8701bf5e9f15","year":2017},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.326606Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:d475b22a022b99cc6488966cdb89d85c91faea7e55ab05b0930df04f0d50f57c","observation_id":"b2e488cd-e7b2-495c-b956-f25ab9be8aef","resolution":{"observed_at":"2026-08-12T10:28:08.900847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.881712Z","title":"The chosen one: Consistent characters in text-to-image diffusion models","venue":null,"work_id":"042e6ddd-7434-4973-88ff-8a2669b1ad1b","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.332029Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:d801fd6adb7718d9fd3a7819f05373967efe078c5ba19ad3df06749822cee5e9","observation_id":"21a04ab4-092f-4195-8111-a7b71abda0f2","resolution":{"observed_at":"2026-08-12T10:28:08.886446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.866477Z","title":"MasaCtrl: Tuning-free mu- tual self-attention control for consistent image synthesis and editing","venue":null,"work_id":"fc5ef20e-20b7-453d-a0c7-05a82a97c616","year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.337038Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:94a57447a90127ae5c5884338179af44aec47d74f141ac14b459fa2efe582965","observation_id":"0ac9ce18-b33f-44cb-adae-445ba8f2fd60","resolution":{"observed_at":"2026-08-12T10:28:08.871482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.851192Z","title":"Character-centric story visualization via visual planning and token alignment","venue":null,"work_id":"5f7237de-ad7c-44d6-bc26-ecf16d6ccc47","year":null},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.342013Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:a5d921842dc23646e479e16c921b307e8d67c1df8da85162d1da1cfa7fc28bf3","observation_id":"470e8c4b-5a7a-4534-aeec-5932dd256882","resolution":{"observed_at":"2026-08-12T10:28:08.856297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00426","last_updated":"2023-12-29T16:42:08Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-30T16:18:00Z","title":"PixArt-$\\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00426","snapshot_observed_at":"2026-08-12T10:28:07.347203Z","title":"PixArt- α: Fast training of diffusion transformer for photorealistic text-to-image synthesis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.347203Z"},"links":{"cited_paper":"/paper/2310.00426","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:90ecdb696dd4e2e57630c34b2d8b68b6d69d0b0ea222413b79e45571a698cf36","observation_id":"4191b648-8fa6-4009-a6fb-07b66464a15c","resolution":{"observed_at":"2026-08-12T10:28:07.347203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.835453Z","title":"AnyDoor: Zero-shot object-level image customization","venue":null,"work_id":"ed33ee9c-ddb2-4468-a203-743f5666514d","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.352731Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:a40b427583ea56d5924597fd46de44d605d5215c03154592a887b46c5e4d15cf","observation_id":"be7d6101-da05-44d4-9bd2-0e516dced0e4","resolution":{"observed_at":"2026-08-12T10:28:08.840730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01388","last_updated":"2025-05-30T13:55:44Z","snapshot_observed_at":"2026-08-12T23:51:30.138669Z","submitted_at":"2024-06-03T14:51:24Z","title":"AutoStudio: Crafting Consistent Subjects in Multi-turn Interactive Image Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01388","snapshot_observed_at":"2026-08-12T10:28:07.357591Z","title":"Au- toStudio: Crafting consistent subjects in multi-turn interactive image generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.357591Z"},"links":{"cited_paper":"/paper/2406.01388","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:e0d6afe7d4d6b81395e889f64e2dfd9b132922c33651751e235a1c710f058c49","observation_id":"54804892-2456-440d-9b03-71e3244a4478","resolution":{"observed_at":"2026-08-12T10:28:07.357591Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18919","last_updated":"2025-05-30T13:52:39Z","snapshot_observed_at":"2026-08-13T00:19:14.224753Z","submitted_at":"2024-04-29T17:58:14Z","title":"TheaterGen: Character Management with LLM for Consistent Multi-turn Image Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.18919","snapshot_observed_at":"2026-08-12T10:28:07.363152Z","title":"TheaterGen: Character management with llm for consistent multi-turn image generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.363152Z"},"links":{"cited_paper":"/paper/2404.18919","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:d7485d0ee7ed1e53f6f4ae34abac478b963f9707b1a5b01734579bff5577df8e","observation_id":"32722c71-9b93-4bb9-be28-a1a748bf2e32","resolution":{"observed_at":"2026-08-12T10:28:07.363152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.820536Z","title":"IDAdapter: Learning mixed features for tuning-free personalization of text-to-image mod- els","venue":null,"work_id":"97b615a0-fe79-4e4f-95b0-222110fa2a8d","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.368231Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:d95e76b77d82d91702bd5b50082a5a577d4be43514ed737605ecdfda73061b40","observation_id":"84a4de94-b307-4022-a2b9-2468456b4d13","resolution":{"observed_at":"2026-08-12T10:28:08.825152Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.805564Z","title":"DreamSim: Learning new dimensions of human visual similarity using synthetic data","venue":null,"work_id":"58c236b0-a6f3-4718-accf-f61b36f6856b","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.373270Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:ca598bec539993b3ab9fa8efb7e26c523d7f14395e28e09da6c8de9ae21ad3af","observation_id":"0d2a07a2-ac15-481c-852c-def8fe177644","resolution":{"observed_at":"2026-08-12T10:28:08.810335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.790185Z","title":"TeViS: Translating text synopses to video storyboards","venue":null,"work_id":"4878b3b8-0ee4-472b-86b7-b2c6877dd708","year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.378124Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:09afa3011cb0ffd7a3db0c5da5b0f9ac92ab68813933da5dad5637c5963b2ab9","observation_id":"e7585736-a511-46b4-a742-dd18ab443446","resolution":{"observed_at":"2026-08-12T10:28:08.794929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.773477Z","title":"Improved training of Wasserstein GANs","venue":null,"work_id":"8a7a16a4-73f9-447e-a3c9-061ea52a7fb0","year":2017},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.383467Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:ce1e1b4ecaad98a8c263e16f06493f53019bf0e13f21922d6298393a65d9a993","observation_id":"c830d15b-3000-4d7e-bf39-956e72bf566f","resolution":{"observed_at":"2026-08-12T10:28:08.779199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.756567Z","title":"Imagine this! scripts to composi- tions to videos","venue":null,"work_id":"d0dee1bf-80eb-47a7-a319-a4e24f403d1b","year":2018},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.389001Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:aaa8900d10b1f9b1f774507ee90fbd5a2a7170bafe449fc057b8df826c184a31","observation_id":"cdae9647-4063-4df2-87fa-17cc2ff8ec95","resolution":{"observed_at":"2026-08-12T10:28:08.761630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.740880Z","title":"Learning profitable NFT image diffusions via multiple visual- policy guided reinforcement learning","venue":null,"work_id":"11d55a8b-566c-4831-8848-132638a55fda","year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.394477Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:1a6d11b028478596a131d78306b1724cf3bf7cdc7230e78220897d195c27d99e","observation_id":"94af6647-2c5f-4ecb-a396-a25a154cdfda","resolution":{"observed_at":"2026-08-12T10:28:08.745907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.724496Z","title":"DreamStory: Open-domain story visualization by LLM-guided multi-subject consistent diffusion, 2024","venue":null,"work_id":"f1b31cbf-80ef-495b-95af-af1adf393528","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.399870Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:3b101962b98298dc5e15e3a7e94c7b77aef0cdf95f4841785b5012b0c5ce8968","observation_id":"7b00548a-cb16-425f-b357-bbfc2af197ab","resolution":{"observed_at":"2026-08-12T10:28:08.729939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.707973Z","title":"Rotary position embedding for vision transformer","venue":null,"work_id":"d460a416-cdf1-41ac-96b1-53cce9832d58","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.405331Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:9dc9902a6572143faac87dd5e053a93f74016bbc07793c8ee6e33d902fe38657","observation_id":"43c381bb-fd82-4aba-8663-73ce0b96865e","resolution":{"observed_at":"2026-08-12T10:28:08.713425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.690388Z","title":"Classifier-free diffusion guidance","venue":null,"work_id":"ea5a8915-abd6-45cb-8ce2-cbbfda2eabf2","year":2021},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.410846Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:baf8ad3f75b29d58e374616101e2d3f1f0e40ae1aac577f1fa2d81f0167f11c1","observation_id":"0c136b2b-5331-43f5-8a46-b78ec9e03357","resolution":{"observed_at":"2026-08-12T10:28:08.695618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:07.416700Z","title":"Denoising dif- fusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.416700Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:3778eb9f17a908a7cd6c43c2041812320442ee8280fca9cd8394a19bb931ac6b","observation_id":"bce6da0a-2ea0-46d8-b673-04be65d45e05","resolution":{"observed_at":"2026-08-12T10:28:07.416700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.662220Z","title":"LoRA: Low-rank adaptation of large language models","venue":null,"work_id":"1d670d73-9ab8-4c54-b19c-02db7ad7ff8c","year":2021},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.422306Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:493b3dbe867de0e9115d2b8303c38f138fbdb3b563a2c8de6d28d2c0cd54cdee","observation_id":"2b1c6d71-b16f-444d-89ab-48180e8af0c7","resolution":{"observed_at":"2026-08-12T10:28:08.667785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.645229Z","title":"How much po- sition information do convolutional neural networks encode? In ICLR, 2020","venue":null,"work_id":"e185bb09-ca66-4894-a9c0-2f7dd91d4185","year":2020},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.427810Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:affe0f5b1c603d7e40454bbdaf9be43896efd2bdd56816812fae08ffb52194d2","observation_id":"80a11c5b-5846-4a34-b5ea-7bd4c66c3068","resolution":{"observed_at":"2026-08-12T10:28:08.650469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.629895Z","title":"Position, padding and predic- tions: A deeper look at position information in cnns","venue":null,"work_id":"0e1af1bd-2301-43d5-8b65-2cfdd0940d87","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.433133Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:fb91acd195c001ee6b8811906c80da902f75b6686fb317c552012e1e4bab97b8","observation_id":"db15c263-30fb-4ae8-b755-9a40c1115293","resolution":{"observed_at":"2026-08-12T10:28:08.634960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.614236Z","title":"Identity decoupling for multi-subject personalization of text- to-image models, 2024","venue":null,"work_id":"d3c2da1b-c2bd-4407-9f6f-2285f32c8e12","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.438439Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:f79b85135504b801a3993327cd73d7ee4d664ca8ea22a09d896a2f0d62ea9e52","observation_id":"140f2528-c6fa-4fc9-a38d-6b42acea6163","resolution":{"observed_at":"2026-08-12T10:28:08.619298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19427","last_updated":"2024-04-30T10:16:21Z","snapshot_observed_at":"2026-08-13T00:18:42.246965Z","submitted_at":"2024-04-30T10:16:21Z","title":"InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19427","snapshot_observed_at":"2026-08-12T10:28:07.443721Z","title":"InstantFamily: Masked attention for zero-shot multi-id image generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.443721Z"},"links":{"cited_paper":"/paper/2404.19427","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:331d46c0b273ed7d60800a9b4cc41472e51951d57b78ad9b5a625271d054ae43","observation_id":"2974fada-9138-4740-adec-49abc56d9d41","resolution":{"observed_at":"2026-08-12T10:28:07.443721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-12T10:28:07.449411Z","title":"Auto-encoding varia- tional bayes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.449411Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:fc0c7a2c85f6bb983f5a649e7bc51a5efc3a50bdf400ee53d97fff80e68c3ca6","observation_id":"8e859a29-fcf7-4b1a-8aa5-119ea2976ede","resolution":{"observed_at":"2026-08-12T10:28:07.449411Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.598334Z","title":"OMG: Occlusion-friendly personalized multi-concept generation in diffusion models","venue":null,"work_id":"6f80eab9-f8b6-4151-aec0-8d1b8ab9d8e4","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.455102Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:28979b93bc03bcd53966af8dcc767555643d4d93ec001df7a389c28640e3c9e4","observation_id":"f5777e1b-e368-47c8-a9f2-28995cbac58c","resolution":{"observed_at":"2026-08-12T10:28:08.603745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.583074Z","title":null,"venue":null,"work_id":"bcc6d99c-d47e-42b2-a1bd-7b51f8e35001","year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.460797Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:02349eb83e9451175ca56f298bd61f84afe01a80c77871f9bf6da964c0fa8542","observation_id":"1079a4fb-5cb4-4901-8e4c-afa795112615","resolution":{"observed_at":"2026-08-12T10:28:08.588023Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12004","last_updated":"2024-12-12T05:02:42Z","snapshot_observed_at":"2026-08-13T04:15:35.158222Z","submitted_at":"2024-02-19T09:52:41Z","title":"Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12004","snapshot_observed_at":"2026-08-12T10:28:07.466041Z","title":"Direct consistency optimization for compositional text- to-image personalization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.466041Z"},"links":{"cited_paper":"/paper/2402.12004","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:de5aebdbf7956cf16eb802af48351bfbefc277bcdb01ce0fc7c98bdecd1b89e9","observation_id":"0ec4fa2f-1f49-4ee9-8531-36eb143831e5","resolution":{"observed_at":"2026-08-12T10:28:07.466041Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:07.472631Z","title":"Playground v2.5: Three insights to- wards enhancing aesthetic quality in text-to-image generation,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.472631Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:9a9bd8757c5676ce97cd9d8fbc72212e4da12134747f8d2a88187d055e6f8c6b","observation_id":"e13df5e3-f074-47db-97a9-0b2701bc7384","resolution":{"observed_at":"2026-08-12T10:28:07.472631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.556007Z","title":"BLIP-diffusion: Pre- trained subject representation for controllable text-to-image generation and editing","venue":null,"work_id":"6dcb7cdc-23a7-4b1a-83aa-9fcc56375628","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.478097Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:8acde61d8c7fb7e54d4c8974fca29e17c33a89b93a5bd91a05d84b80bda88312","observation_id":"e57f8b29-bf20-4930-8f33-68b85da30fdf","resolution":{"observed_at":"2026-08-12T10:28:08.561427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.540539Z","title":"BLIP: bootstrapping language-image pre-training for unified vision- language understanding and generation","venue":null,"work_id":"8d98a891-3176-47fd-8c51-9b403c51a628","year":2022},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.483558Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:614171de15bcabb3a3bf96a491c9a5d933e9b1f6f1adc5d883d27c07a7f17158","observation_id":"090eb7e2-30b5-43d0-a239-206ab6539959","resolution":{"observed_at":"2026-08-12T10:28:08.545501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.526295Z","title":"BLIP- 2: bootstrapping language-image pre-training with frozen image encoders and large language models","venue":null,"work_id":"dd3720b7-0f0f-4a10-acda-b008b987ead3","year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.488866Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:6683021fb54c430e304a1d064e6fcc8adcba5e79c7f173b223355f010130d05d","observation_id":"eb9dbcd5-dca6-406b-833a-6a8fe88503aa","resolution":{"observed_at":"2026-08-12T10:28:08.531067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.511031Z","title":"StoryGAN: A sequential conditional gan for story visu- alization","venue":null,"work_id":"5c55d0da-7198-4571-b1a2-8e2d34692778","year":2019},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.494477Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:cbb62012974ef963a2519085a382335b0b13b925c8f9f886b4b240dc7a2cfb9e","observation_id":"e7fb4caa-20e6-42d8-80dd-e6ed6c8b2a5d","resolution":{"observed_at":"2026-08-12T10:28:08.516017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.495473Z","title":"PhotoMaker: Customizing realistic human photos via stacked id embedding","venue":null,"work_id":"8543c889-f875-405e-b976-3a3e66d18f65","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.499860Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:adc314188c84980027062847d464971acb14b979712f16cba3e2c7edf30b1b02","observation_id":"10958af1-df54-4b0b-ab9c-40073dd5a913","resolution":{"observed_at":"2026-08-12T10:28:08.500584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.480982Z","title":"Unveiling the mask of position-information pattern through the mist of im- age features","venue":null,"work_id":"0f0ae064-423b-4726-a97b-1eb82dec1132","year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.505473Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:44bf95e616c30b6787d9fa18f7fa1d5e9eb11d177309a66ef833d169188d674e","observation_id":"82769b12-fa4e-4305-8d3c-7a43c53d0907","resolution":{"observed_at":"2026-08-12T10:28:08.485826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.465420Z","title":"Intelligent grimm-open-ended visual storytelling via latent diffusion models","venue":null,"work_id":"125c1169-eb57-4114-a191-1d464e473c38","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.511149Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:94ca0981e698299e26f033a67de3e0091e533ec270353145361e171d80709e9a","observation_id":"017cdda8-1a0c-4cd4-83b9-b14e58e07131","resolution":{"observed_at":"2026-08-12T10:28:08.470698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.449868Z","title":"One-Prompt-One-Story: Free-lunch consistent text-to-image generation using a single prompt","venue":null,"work_id":"be4f5e5e-118b-4f28-9566-54ea534570fe","year":2025},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.516446Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:3930e31114220e1ce41330670795f1fd7d0fc9b2cc028fc264ac970a414ae4d3","observation_id":"b37abf56-c867-445a-8b71-1bbf4d9045f7","resolution":{"observed_at":"2026-08-12T10:28:08.455164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.01095","last_updated":"2025-05-19T07:56:56Z","snapshot_observed_at":"2026-08-13T05:49:42.133862Z","submitted_at":"2022-11-02T13:14:30Z","title":"DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.01095","snapshot_observed_at":"2026-08-12T10:28:07.521913Z","title":"DPM-solver++: Fast solver for guided sampling of diffusion probabilistic models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.521913Z"},"links":{"cited_paper":"/paper/2211.01095","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:253085d6237976ad5e96b26a00349fa8aeb8a79f01cd0adf74531e6f66b151c1","observation_id":"fe9025c9-4597-4391-9c7d-a3f7b0004258","resolution":{"observed_at":"2026-08-12T10:28:07.521913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.433563Z","title":"Subject- Diffusion: Open domain personalized text-to-image gener- ation without test-time fine-tuning","venue":null,"work_id":"92e8e984-854e-4055-9c03-12dad5e663c0","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.527357Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:dd9ae02c3819213c278b54fb9472d8957711f4ae941fcb41466f1990c39dd0b8","observation_id":"b150656f-f703-4892-a2f5-aa8d3c37b371","resolution":{"observed_at":"2026-08-12T10:28:08.438469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.417834Z","title":"AI illustrator: Translating raw descriptions into images by prompt-based cross-modal generation","venue":null,"work_id":"3c48a6ce-d0a6-45f2-a7ab-bb210c87fc40","year":2022},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.532773Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:65e19a1177474ce8b6d1b4ac3db0071bd919ea87c23ab46a05885136071564a4","observation_id":"0bd3a606-db96-4064-b3f1-1e1dd2ae42b7","resolution":{"observed_at":"2026-08-12T10:28:08.423107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.402324Z","title":"Integrating visuospa- tial, linguistic, and commonsense structure into story visual- ization","venue":null,"work_id":"5136d6eb-45b7-4de9-b1e8-f1813fbe90d2","year":2021},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.538031Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:7ecfdd230cc931d6dc7d57d26dd0a79748c9ed9f7e89a3c8fb034f62bbc4d9e7","observation_id":"1c9d0833-af86-41fa-a69b-1a24fd456219","resolution":{"observed_at":"2026-08-12T10:28:08.407357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.386309Z","title":"Im- proving generation and evaluation of visual stories via seman- tic consistency","venue":null,"work_id":"9ceb5265-b0c5-4902-bf59-61bb794a8ca5","year":2021},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.544281Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:83d2a7ab400afb0bc380a48ea0c637f4b5483c80373e5d796c35d41b21ba1a86","observation_id":"3a3b6bfc-9c32-4730-a2a9-2ecbcd3b2385","resolution":{"observed_at":"2026-08-12T10:28:08.391735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.370840Z","title":"StoryDALL-E: Adapting pretrained text-to-image transform- ers for story continuation","venue":null,"work_id":"7f1eb814-9c22-46b2-99b0-98c3bd3271da","year":null},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.551340Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:86d36ce300c5e8280b1682827af51d6c7e06cbca0905169a4a5a82e0c613001e","observation_id":"8e31393a-3c87-42d8-adfd-aaa56cd0f941","resolution":{"observed_at":"2026-08-12T10:28:08.375865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.356563Z","title":"Improved denoising diffusion probabilistic models","venue":null,"work_id":"3ac06a95-5378-4ac2-a168-7d889fb92c0e","year":2021},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.559071Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:7e1ec7877807bcbc1c0d7c2d8fa31b780685a4f8336d106e02c29dd046c0b3a1","observation_id":"abd7752e-5937-4e09-9ddd-b0b19d132f7d","resolution":{"observed_at":"2026-08-12T10:28:08.361091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.341859Z","title":"Synthesizing coherent story with auto-regressive latent diffusion models","venue":null,"work_id":"582d8011-34bc-4b8a-8795-e896c2fb885e","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.564859Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:889efac5bd5550c937d5917f0801aab53e17ee69e8db1009d0e6ca8359a80f20","observation_id":"807fb1a4-54a6-46b7-b6ef-0eca8f40dc12","resolution":{"observed_at":"2026-08-12T10:28:08.346774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.327029Z","title":"PortraitBooth: A versatile portrait model for fast identity-preserved personalization","venue":null,"work_id":"d3c0e866-5c6a-4b5a-9fda-d27c51ef9a76","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.570975Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:d4f42f1777636f5d26d85b832d4f5724d99b4d1a7adf680a2581b0d03e7c9a81","observation_id":"5515e56b-2a72-4cb2-adfd-22e59ce19975","resolution":{"observed_at":"2026-08-12T10:28:08.332005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01952","last_updated":"2023-07-04T23:04:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-04T23:04:57Z","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01952","snapshot_observed_at":"2026-08-12T10:28:07.577580Z","title":"SDXL: Improving latent diffusion models for high-resolution image synthesis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.577580Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:6ed261dfe70b6a2e043b0665093161380f75539829f343ddf6a2b9071259f4bf","observation_id":"dd169718-4788-401e-b5bc-004269fa7cef","resolution":{"observed_at":"2026-08-12T10:28:07.577580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.312570Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":"b773db28-a56e-4cc1-9b4f-7bf2c0bcec20","year":2021},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.583704Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:18a7beba3176e6a60ba5a968facfe0965b1bd4ca9bb51de715b1cd6844992ac7","observation_id":"be80f2eb-5f3c-4ec5-98ce-0c05ec08bb2f","resolution":{"observed_at":"2026-08-12T10:28:08.317237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.297956Z","title":"Make-a-Story: Visual memory conditioned consistent story generation","venue":null,"work_id":"b58f595b-2ec0-4e2f-aa8c-d9b0476429d8","year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.589337Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:dee9cd0928b45b2f517cac0e8399fb83bc4b64862728426a532143a6f3e095d1","observation_id":"b0df295b-97c3-4d2e-b66e-56998154c2a9","resolution":{"observed_at":"2026-08-12T10:28:08.302683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14159","last_updated":"2024-01-25T13:12:09Z","snapshot_observed_at":"2026-07-06T17:20:25.138890Z","submitted_at":"2024-01-25T13:12:09Z","title":"Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14159","snapshot_observed_at":"2026-08-12T10:28:07.595466Z","title":"Grounded SAM: Assembling open-world models for diverse visual tasks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.595466Z"},"links":{"cited_paper":"/paper/2401.14159","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:24068839b8303b3609aa3de92e6f6854526fb9fa2b07b3ba17783678a3c0a27c","observation_id":"8b64795f-ab6c-422f-b28e-2cbb2fa90a70","resolution":{"observed_at":"2026-08-12T10:28:07.595466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.283313Z","title":"Image-based video game asset generation and evaluation using deep learning: a systematic review of meth- ods and applications","venue":null,"work_id":"68e885a4-7e12-4844-a953-1ff8fe73368a","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.600659Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:01d268e0d6d6d8a09510b1791210f5c33f22e263094a598f7b922f1efdc9211b","observation_id":"b67d7295-ba24-42f1-9e55-403c2ccb7e72","resolution":{"observed_at":"2026-08-12T10:28:08.288204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.268555Z","title":"High-resolution image syn- thesis with latent diffusion models","venue":null,"work_id":"92fdcddb-d5f2-4d29-92f1-594ac08b111d","year":2022},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.605904Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:e187798501d6f60ef432130b259f725fb029b13e916e409d17c204d8517a5d3a","observation_id":"5f5b9aa8-4db8-49ee-acf4-55e701654db0","resolution":{"observed_at":"2026-08-12T10:28:08.273282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.253344Z","title":"U-net: Convolutional networks for biomedical image segmentation","venue":null,"work_id":"1251648e-2b40-4a8d-9d99-65e5fa1878f5","year":2015},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.610339Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:aeb49d396d55442c623fc60ce9ebcd16ab6f8b1a94e380d92e71f1ff199d335b","observation_id":"d498df8e-9a5c-44b9-8a51-f8766a691e87","resolution":{"observed_at":"2026-08-12T10:28:08.258520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.238927Z","title":"DreamBooth: Fine tuning text-to-image diffusion models for subject-driven gen- eration","venue":null,"work_id":"d5350b54-dc22-4969-b938-96fc1c1455c4","year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.614920Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:11f773d6e71b46169e3ec2ff0b1915394f128085845e670a61a2192c4751f1a4","observation_id":"44ba9f19-51b2-4338-9dd5-36a5419a9333","resolution":{"observed_at":"2026-08-12T10:28:08.243752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.08402","last_updated":"2022-10-16T00:08:18Z","snapshot_observed_at":"2026-07-29T21:51:47.064287Z","submitted_at":"2022-10-16T00:08:18Z","title":"LAION-5B: An open large-scale dataset for training next generation image-text models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.08402","snapshot_observed_at":"2026-08-12T10:28:07.619388Z","title":"LAION-5B: An open large-scale dataset for training next gen- eration image-text models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.619388Z"},"links":{"cited_paper":"/paper/2210.08402","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:b343e0a5a7f964b08badec4ec2bcf90f3d786fbd0c518f2d57de96561a8450f4","observation_id":"74a68153-adc6-4b97-be52-83170d8be1a4","resolution":{"observed_at":"2026-08-12T10:28:07.619388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.224033Z","title":"Denoising diffusion implicit models","venue":null,"work_id":"5622eff2-7a69-4b69-86c8-060072f8695b","year":2020},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.625012Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:d04f704ecb0998ca98e62480dddab6b301bea5e451c3f13a428e608bb92eb103","observation_id":"7d0c9ff2-7c45-4bc9-9bfa-55d870379e3e","resolution":{"observed_at":"2026-08-12T10:28:08.229084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.209391Z","title":"Score-based generative modeling through stochastic differential equations","venue":null,"work_id":"31e6cd55-7aab-4ad9-9081-51eb96516a40","year":2020},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.629860Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:2dd2a449584a7e849293f21c2d51fe6f39035fe0c934a43c87b099996e0a4225","observation_id":"90fdd870-b890-48ed-a0e0-9cc7ad095dc1","resolution":{"observed_at":"2026-08-12T10:28:08.214172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.193459Z","title":"Character-preserving coherent story visualization","venue":null,"work_id":"bdab604f-634e-490c-9050-df9414c877a5","year":2020},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.634208Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:12528cb2a57e637256247cb8ca3bdd97ba00168fe154265d2c115663d19a75da","observation_id":"23b63b59-7010-4746-a151-6b945db18211","resolution":{"observed_at":"2026-08-12T10:28:08.198902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.176964Z","title":"Create your world: Lifelong text-to- image diffusion","venue":null,"work_id":"d9ef4c15-0c2c-4bfe-8d8d-1b8cf8c8ebf3","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.639276Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:410da8eb782b639996ff00b091133e1c1489a679525d86fc0ac64bc770fcafed","observation_id":"381de995-bb1c-4cb4-808d-41471e30febe","resolution":{"observed_at":"2026-08-12T10:28:08.182146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.161465Z","title":"Kolors: Effective training of diffusion model for photorealistic text-to-image synthesis","venue":null,"work_id":"989a2396-b0e5-45ef-8980-a588471a816a","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.643754Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:f69d8c8656a3e0820aff5cdd92f3c19ab97658b19f79b268009ba8a2dbd4cf20","observation_id":"7685712b-774f-49c7-be18-e63d0f090644","resolution":{"observed_at":"2026-08-12T10:28:08.166519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.146198Z","title":"Training-free consistent text-to-image generation","venue":null,"work_id":"c949cd0e-c53e-427a-80bd-15f478625150","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.648377Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:142c980329f03621dff7777b055b091ead3568272a6305e6382f455b2cc276d1","observation_id":"96df95cd-1245-414f-87b3-80a463face34","resolution":{"observed_at":"2026-08-12T10:28:08.151065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.129281Z","title":"Storytelling and visualization: An extended survey","venue":null,"work_id":"81f782bb-4036-4a04-b548-744f76ebe0bc","year":2018},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.652892Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:fd386f8a9c18efbb0d1c274ee01edd8466f62d5b6c59901f70df2e9db3246ad5","observation_id":"ab8d6ba0-95b1-4fa8-bba7-14cc15b3d988","resolution":{"observed_at":"2026-08-12T10:28:08.134910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.111950Z","title":"OneActor: Consistent subject generation via cluster- conditioned guidance","venue":null,"work_id":"8489462b-f472-4a07-bb23-d35876a592f6","year":2025},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.657081Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:13fafda79d7755e1a77e8b642578ab93d6cc90c07f1ec49fc1e8fcb02560ae77","observation_id":"862525d9-af51-4ace-8ce6-4da3dfa51e69","resolution":{"observed_at":"2026-08-12T10:28:08.117464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10874","last_updated":"2024-04-24T11:22:00Z","snapshot_observed_at":"2026-08-08T15:15:33.423007Z","submitted_at":"2023-05-18T11:06:15Z","title":"Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10874","snapshot_observed_at":"2026-08-12T10:28:07.661616Z","title":"Videofactory: Swap atten- tion in spatiotemporal diffusions for text-to-video generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.661616Z"},"links":{"cited_paper":"/paper/2305.10874","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:4ea44de117c0ce5b6dba80464bff11dd5370d4c9faf88dc8c6a4112e37bd5a49","observation_id":"203d4ee9-6bf2-441b-9a17-d75a069808db","resolution":{"observed_at":"2026-08-12T10:28:07.661616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.095788Z","title":"MS-Diffusion: Multi-subject zero-shot image personalization with layout guidance","venue":null,"work_id":"5b48cf8b-99d5-437a-874d-af86a3529b18","year":2025},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.666536Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:ef5cc283d3c8dfc33582d38ea11f8280a83f6aa1274adbcf8d9bfa91a6345738","observation_id":"66804eb6-e3dd-47ca-bf06-e2e071bdd6bc","resolution":{"observed_at":"2026-08-12T10:28:08.101186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.079855Z","title":"High-fidelity person-centric subject-to-image synthesis","venue":null,"work_id":"c645130c-915d-4b39-865f-82d4070ddcdd","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.670991Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:c065e40bf39d701aa7a551d6ba9886348a7337e8798319581d2797558a5cf590","observation_id":"daf98722-cd0c-42e9-93f3-6dac30f9014c","resolution":{"observed_at":"2026-08-12T10:28:08.084878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.06721","last_updated":"2023-08-13T08:34:51Z","snapshot_observed_at":"2026-07-06T16:05:39.158819Z","submitted_at":"2023-08-13T08:34:51Z","title":"IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.06721","snapshot_observed_at":"2026-08-12T10:28:07.676143Z","title":"IP- Adapter: Text compatible image prompt adapter for text-to- image diffusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.676143Z"},"links":{"cited_paper":"/paper/2308.06721","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:1b6da69e89b1a9f78c5577e80f2434e15bdd47e57abd86011b9994373c33829d","observation_id":"3f658e01-5f1f-4c48-b7cb-ad10ab983199","resolution":{"observed_at":"2026-08-12T10:28:07.676143Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.064323Z","title":"LaPE: Layer- adaptive position embedding for vision transformers with independent layer normalization","venue":null,"work_id":"9126955a-18e7-46af-83cc-18e03761b6fc","year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.680886Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:1fd07f6ed85072194e707865530abb24b6927bcb223ca65cd4ab1ff8428083a5","observation_id":"f00ae2b5-4797-48ed-9fe7-e4f47e89202f","resolution":{"observed_at":"2026-08-12T10:28:08.069198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.048300Z","title":"Jedi: Joint- image diffusion models for finetuning-free personalized text- to-image generation","venue":null,"work_id":"ce4f9205-a165-4cb0-87b5-322eb1e72a7d","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.685615Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:da06dc542f807ee487084488ce5d3dce6005785b4b26f15ba1ac7bf09b48dc08","observation_id":"76cc6391-fd9f-4025-975c-68283e1c4d54","resolution":{"observed_at":"2026-08-12T10:28:08.053421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:07.690224Z","title":"Adding conditional control to text-to-image diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.690224Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:4cccc6dd7726e960bca89145c96bc77d1886c0320ecd9eb954cea69c8c60b6b5","observation_id":"bd496979-f4a0-4b62-9cda-9fe562df1274","resolution":{"observed_at":"2026-08-12T10:28:07.690224Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.022004Z","title":"SSR-Encoder: Encoding selective subject representation for subject-driven generation","venue":null,"work_id":"24a57f07-cb1c-4605-8730-4c49fc18cb52","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.694767Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:4c7e20f0182da848bc7a51b39fb3b761bd900d4f409790b85fb1db8bb8eed82e","observation_id":"7aa28bc7-1e24-4ba0-8f18-eebe3205cfc4","resolution":{"observed_at":"2026-08-12T10:28:08.027108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:08.006114Z","title":"Pia: Your personalized image animator via plug-and-play modules in text-to-image models","venue":null,"work_id":"7c860ca6-e25b-4392-a718-e47a18c0b141","year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.699339Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:2d2272310cc7ec8976e9cb577d462c8d653fa474d376f29c1b939fce43236996","observation_id":"7baced54-881e-49ff-b4c5-31d629b7dcdb","resolution":{"observed_at":"2026-08-12T10:28:08.011126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:07.990224Z","title":"StoryDiffusion: Consistent self-attention for long-range image and video generation","venue":null,"work_id":"33bd6155-ace7-46cf-95ce-45c2361471a5","year":2025},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.703869Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:0a0d323ca7c2cad958e07893222d632b5a7cd509a63846631e163d0532656f6f","observation_id":"0fe040ef-7167-49f7-8572-68b89b99dafe","resolution":{"observed_at":"2026-08-12T10:28:07.995307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12576","last_updated":"2024-09-19T08:53:06Z","snapshot_observed_at":"2026-08-12T22:41:54.763076Z","submitted_at":"2024-09-19T08:53:06Z","title":"StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12576","snapshot_observed_at":"2026-08-12T10:28:07.708293Z","title":"StoryMaker: Towards holistic consistent characters in text-to-image generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.708293Z"},"links":{"cited_paper":"/paper/2409.12576","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:1fa859e1c221420725cd940877f5f2e5dfb310607511bba74788d3a958f25acb","observation_id":"7525eb89-5a21-4d38-9fa4-107b828a168c","resolution":{"observed_at":"2026-08-12T10:28:07.708293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14239","last_updated":"2025-03-31T06:30:53Z","snapshot_observed_at":"2026-08-13T00:24:36.789155Z","submitted_at":"2024-04-22T14:47:54Z","title":"MultiBooth: Towards Generating All Your Concepts in an Image from Text","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14239","snapshot_observed_at":"2026-08-12T10:28:07.713436Z","title":"MultiBooth: Towards generating all your concepts in an im- age from text","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.713436Z"},"links":{"cited_paper":"/paper/2404.14239","citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:a31a99a6a94693329e6ef27fa79087c831b9af4b7e7a903a6062434b2e6f5817","observation_id":"6ffd4618-790e-4808-9a76-08dd23462869","resolution":{"observed_at":"2026-08-12T10:28:07.713436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:28:07.972114Z","title":"Moviefactory: Automatic movie creation from text using large generative models for language and images","venue":null,"work_id":"e31635a3-2b97-4f32-904a-1b6dfee2b3b8","year":2023},"citing_paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T10:28:07.718327Z"},"links":{"citing_paper":"/paper/2411.19261"},"observation_digest":"sha256:2be90e5bb7a9ebabdc848ee03adbde26d399c446354eca1daa5935437ffa90d2","observation_id":"c467bf39-ddd1-4c36-ab22-4cb1a6c0a8fd","resolution":{"observed_at":"2026-08-12T10:28:07.979560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.19261","last_updated":"2025-03-09T13:39:55Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T11:54:33.400468Z","submitted_at":"2024-11-28T16:50:30Z","title":"Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention"},"reference_resolution":{"displayed":76,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":0,"verified_fuzzy":57},"total_outbound_references":76},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 2 inbound Pith citation observations for arXiv:2411.19261."}