{"as_of":"2026-08-08T07:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8fa0d260de1c82f905ad99335119a4490350393c7284bbfd2d07aabd146ac2c2","coverage":[{"denominator":87,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":87,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:24:40.211794Z","state":"measured"},{"denominator":87,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":87,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.21960/citation-record","integrity":"/paper/2505.21960/integrity","json":"/paper/2505.21960/citation-record.json","paper":"/paper/2505.21960"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:24:32.113305Z","title":"Token merging for fast sta- ble diffusion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:32.113305Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:2667251e118d0792e4300cde11e89d73d10c383fc8f654751c37452e7d406232","observation_id":"c0fed6ad-27f8-48fc-8c64-cadd047bc9b2","resolution":{"observed_at":"2026-08-07T13:24:32.113305Z","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":"2026-08-07T13:24:32.171815Z","title":"Colorpeel: Color prompt learning with diffusion models via color and shape disentanglement","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:32.171815Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:bd8a188345705eb6f7f5ddd3c4e1da4201ccd27abadf920b35f01cd4e3e44016","observation_id":"e4347150-a182-4c31-a554-0b62c7fc45de","resolution":{"observed_at":"2026-08-07T13:24:32.171815Z","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":"2026-08-07T13:24:32.317340Z","title":"Q-dit: Ac- curate post-training quantization for diffusion transformers","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:32.317340Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:85340ba2c97e343679f1d662d70d899b468bc9906a7579cf7e576d4ccd5256d7","observation_id":"d844e0b4-ffd4-46ee-87ec-4a86a7b80e2e","resolution":{"observed_at":"2026-08-07T13:24:32.317340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01125","last_updated":"2024-06-03T09:10:44Z","snapshot_observed_at":"2026-07-06T18:24:17.711867Z","submitted_at":"2024-06-03T09:10:44Z","title":"$\\Delta$-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01125","snapshot_observed_at":"2026-08-07T13:24:32.439041Z","title":"Delta-dit: A training-free acceleration method tailored for diffusion transformers.arXiv preprint arXiv:2406.01125, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:32.439041Z"},"links":{"cited_paper":"/paper/2406.01125","citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:1106fa3517f54fa5b78fb71c8f6101c94eabd468b4fabdc28852612b6f039190","observation_id":"f12936c4-279a-4ccf-854a-b1cc6ad4751a","resolution":{"observed_at":"2026-08-07T13:24:32.439041Z","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":"2026-08-07T13:24:32.524024Z","title":"Asyncdiff: Parallelizing diffusion mod- els by asynchronous denoising.NeurIPS, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:32.524024Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:ab79761ef7ad58758b338ef0041454493f17d84db211041ec9d4617df07f4b41","observation_id":"ecec133b-db4f-4a4a-9e41-5879800992ff","resolution":{"observed_at":"2026-08-07T13:24:32.524024Z","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":"2026-08-07T13:24:32.656137Z","title":"Stargan v2: Diverse image synthesis for multiple domains","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:32.656137Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:c23a12688040571e9b2b85767acb7f76c01202d7b46ecdd0d2dc39420c572a2f","observation_id":"b3ca6abc-aff6-4183-a936-0e509c249bd6","resolution":{"observed_at":"2026-08-07T13:24:32.656137Z","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":"2026-08-07T13:24:32.784220Z","title":"Swiftbrush v2: Make your one-step diffusion model better than its teacher.ECCV,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:32.784220Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:a2fe5b176a1f19fcaae1a36ddeb81b4da1a82c2fff07a37e139213e962375598","observation_id":"2c32a49b-70f8-4a62-9ec2-35a6d85a11d7","resolution":{"observed_at":"2026-08-07T13:24:32.784220Z","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":"2026-08-07T13:24:53.792244Z","title":"DeepFloyd IF.https://www.deepfloyd","venue":null,"work_id":"120b6467-bb50-4c4b-abb3-a3366a0a422e","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:33.026912Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:71a772d864c880e25b482154698ab4e3d0f9284eab184dcf69f54764db3e1880","observation_id":"f76e3f56-6590-498d-afef-f67e504b11bd","resolution":{"observed_at":"2026-08-07T13:24:53.855038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:33.157769Z","title":"Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:33.157769Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:a10f235103ee0b137ee1644f9be3c3881ac6690773aad708c360d21bf4a501f2","observation_id":"6661cb2e-8684-48be-a213-84acdf147465","resolution":{"observed_at":"2026-08-07T13:24:33.157769Z","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":"2026-08-07T13:24:53.537284Z","title":"Structural pruning for diffusion models.Advances in neural informa- tion processing systems, 36, 2024","venue":null,"work_id":"dfea7312-f691-4919-a4b8-81973e0cf619","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:33.296315Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:6198cba6d113a508ffe39cdc23b639c5681d415f60c69eb34eec281f57068908","observation_id":"9c27eb94-c89d-4c4e-8566-749c6de48f10","resolution":{"observed_at":"2026-08-07T13:24:53.639982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:53.422005Z","title":"One- step diffusion distillation via deep equilibrium models","venue":null,"work_id":"6d6306c2-ab87-43c7-b5a4-285581403d77","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:33.462051Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:33ade682d69dc054b1b8ce723c98369e9dbad941d2f9491209512e2587a7f1d2","observation_id":"d58ef1ee-eed6-4c91-acb1-05fde63a1e52","resolution":{"observed_at":"2026-08-07T13:24:53.474179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:53.243570Z","title":"Tokenflow: Consistent diffusion features for consistent video editing.ICLR, 2024","venue":null,"work_id":"a29aa5ac-3a57-494a-98b9-255ac6261812","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:33.557831Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:100b9036bccc7ea3c875ee4a805a55cb423ab54bd8caa57cbc69294c3d3b707a","observation_id":"635601c9-4c5b-4780-bb8a-5bdcb9a36498","resolution":{"observed_at":"2026-08-07T13:24:53.319194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:52.995211Z","title":"Photoswap: Personalized subject swapping in images, 2023","venue":null,"work_id":"12b8ddb1-10b1-4fe2-bc53-0582058c106a","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:33.650025Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:b00996b463f74372d35c1775958c5b7859dc85eb380db1a7017db35ecba01b4e","observation_id":"20c15a73-ee58-497d-929e-1ceb5370f62e","resolution":{"observed_at":"2026-08-07T13:24:53.109572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:52.830761Z","title":"Boot: Data-free distillation of denoising dif- fusion models with bootstrapping.International Conference on Machine Learning, 2023","venue":null,"work_id":"b1f69367-3128-4e21-949c-0a0a2eb21d4e","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:33.757921Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:09a0b1822d964aa366b30720c372e7f8b689aa6eb906fd260c702db9f56e5460","observation_id":"a55871e3-c4d9-47dc-b316-eebc8066fe23","resolution":{"observed_at":"2026-08-07T13:24:52.918209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:52.654512Z","title":"Prompt-to-prompt image editing with cross attention control.ICLR, 2023","venue":null,"work_id":"3820b4f9-da61-42ed-ab1f-413436e6add3","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:33.833769Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:08c930ffe74195f7a5fa325f2b7d68195f30d23439bd40e30e303001a8bd4cab","observation_id":"6c210f76-eee4-4157-99b1-378159facab1","resolution":{"observed_at":"2026-08-07T13:24:52.745969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:52.493230Z","title":"Clipscore: A reference-free evaluation met- ric for image captioning","venue":null,"work_id":"de37df0e-10aa-40ed-9ed6-4caeb68294f3","year":2021},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:33.901494Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:a6c48541d1d53c958ffa5e7d197ab69f6e45a76f7996f73a8fd7172e7327053f","observation_id":"23af3897-66b8-4e61-8165-fcbea467e5bc","resolution":{"observed_at":"2026-08-07T13:24:52.565229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:52.323358Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilib- rium","venue":null,"work_id":"51416720-682e-4e26-8e21-b1e29a421654","year":2017},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:33.976384Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:15071f3cc409b5704f50c8c73c5dc069630107183c52a1491b6422f344776732","observation_id":"63c13df4-3a89-4568-a762-29dca5eb8c0b","resolution":{"observed_at":"2026-08-07T13:24:52.389263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:52.128658Z","title":"Distilling the Knowledge in a Neural Network.NIPS Deep Learning Workshop, 2014","venue":null,"work_id":"85eb4039-376b-4e99-97cc-83e18bb14414","year":2014},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.048100Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:1ef71e35b1959eb87cd5842c01692a83b0706e2f049bf13d99502d67c1818c51","observation_id":"bdc318ac-89b8-4ba2-84b8-8b710129aeab","resolution":{"observed_at":"2026-08-07T13:24:52.213762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:34.134505Z","title":"Denoising diffu- sion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.134505Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:12fcf656da65a6776ad2e540e6bb57d51c743e4f75f35a26f14678343d90b250","observation_id":"57b30a11-0cc2-4e7a-8773-b547d54566d6","resolution":{"observed_at":"2026-08-07T13:24:34.134505Z","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":"2026-08-07T13:24:51.946785Z","title":"Lora: Low-rank adaptation of large language models.ICLR,","venue":null,"work_id":"5a97d896-4f94-40b7-9089-efc87de8469e","year":null},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.225305Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:e5933d8478f21a5f7bc6d03f8c6fea9231ec6095e801b5d539807e59c2ae08ff","observation_id":"bcd02d43-4339-4306-93f8-e60088b0b9cd","resolution":{"observed_at":"2026-08-07T13:24:51.998572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:51.778061Z","title":"Token merging for training- free semantic binding in text-to-image synthesis","venue":null,"work_id":"868c529f-5b8e-4771-b7e7-dae1d60dc958","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.326108Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:583f711c5658d94f69f07ce03c374546a03d164be915784b2a54578625c6aef3","observation_id":"03809374-e248-4a4c-8b3c-4a67a84570f6","resolution":{"observed_at":"2026-08-07T13:24:51.851968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.01723","last_updated":"2025-05-31T17:58:24Z","snapshot_observed_at":"2026-07-06T19:26:22.646942Z","submitted_at":"2024-10-02T16:34:29Z","title":"HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer Acceleration","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.01723","snapshot_observed_at":"2026-08-07T13:24:34.423349Z","title":"Harmonica: Harmonizing training and inference for better feature cache in diffusion transformer acceleration.arXiv preprint arXiv:2410.01723, 2024","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.423349Z"},"links":{"cited_paper":"/paper/2410.01723","citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:e333be7b90d99eaa5d3970df648763082909193aca9421f453a7afeb15a65db9","observation_id":"adea5a53-c7d4-4995-896f-8a5055b2a0fb","resolution":{"observed_at":"2026-08-07T13:24:34.423349Z","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":"2026-08-07T13:24:51.473511Z","title":"Contragan: Contrastive learn- ing for conditional image generation.NeurIPS, 2020","venue":null,"work_id":"99d2f068-2faf-4477-96c1-9caf889f86e8","year":2020},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.504175Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:753ed63b355b7e12f416e351b715c1f40717daead2c98a9bf1121f81a1d7b383","observation_id":"77da9014-59ee-4113-b770-66bd3f062f70","resolution":{"observed_at":"2026-08-07T13:24:51.670711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:51.144989Z","title":"Rebooting acgan: Auxiliary classifier gans with stable training","venue":null,"work_id":"fec3a683-d98e-43f5-96ac-b97d5e39d208","year":2021},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.570197Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:df37374fb60919f2d9fd56367a012afaa6c899e3a89f5fb0dc420543bb3906cd","observation_id":"3d75072f-6c3f-4db5-b362-9162d0b3e747","resolution":{"observed_at":"2026-08-07T13:24:51.325653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:50.901857Z","title":"Studio- gan: a taxonomy and benchmark of gans for image synthesis","venue":null,"work_id":"f253a0e6-deac-4a0d-b1d6-693bd61a1bd3","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.648995Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:9b47337a393d7434e62f372dfc96e8b8fa46a1e0077611303dd1976726f76653","observation_id":"634d7cef-bf31-4026-a920-767409b02488","resolution":{"observed_at":"2026-08-07T13:24:51.024951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:50.620190Z","title":"Scaling up GANs for Text-to-Image Synthesis.CVPR, 2023","venue":null,"work_id":"f88fe834-a15a-4ca7-a806-9eedd1051a70","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.706635Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:e48093515109ad3909df5a7da87149727f2b1bac125d25af159ed9818a961f50","observation_id":"7de43274-23ed-4145-ba95-6e6dfb9551be","resolution":{"observed_at":"2026-08-07T13:24:50.747368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:50.426430Z","title":"Progressive growing of gans for improved quality, stability, and variation.ICLR, 2018","venue":null,"work_id":"809c1140-f773-40c3-919a-b1ee9403522b","year":2018},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.783156Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:ca864f5cd48a35ff8580f08774672571009b646e41482df40503da1651ad84cf","observation_id":"4870a81d-1189-4256-a1ad-48196a60cda3","resolution":{"observed_at":"2026-08-07T13:24:50.523337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:50.154099Z","title":"Text2video-zero: Text- to-image diffusion models are zero-shot video generators","venue":null,"work_id":"469774b6-a676-4182-884d-0bc6c01e5f95","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.832105Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:0cbe85618b96dafbee57273de699a2a0914e8d61d0e224e18509313a002ff508","observation_id":"84361988-e3a7-49bf-adb4-d1b5ac919141","resolution":{"observed_at":"2026-08-07T13:24:50.309618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:34.901596Z","title":"Token fusion: Bridging the gap between token pruning and token merging","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.901596Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:96f1693e518b0d3880c39a61c69b3e3792106295e7860242b95d890c681cf591","observation_id":"5c2e8b5d-e050-4bf3-9922-efd97f57fd65","resolution":{"observed_at":"2026-08-07T13:24:34.901596Z","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":"2026-08-07T13:24:49.850952Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":"353f812b-7513-4142-8dec-d73382b4cfb8","year":2015},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:34.964877Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:eaa7d3ba3d008736a73935be16e90f0521ff8f0d5d62ffc8a547eabb24d4eaad","observation_id":"6883f029-addf-4c24-844c-5057c71e472e","resolution":{"observed_at":"2026-08-07T13:24:50.006819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:35.030213Z","title":"Multi-concept customization of text-to-image diffusion","venue":null,"work_id":null,"year":1931},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:35.030213Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:7903f34b4ee6c1505deec1e36a98d7ed9697045dd5fb849345fcc41cb22c663f","observation_id":"05844fc8-cef8-4f72-9f42-b3a0f14be68b","resolution":{"observed_at":"2026-08-07T13:24:35.030213Z","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":"2026-08-07T13:24:49.414332Z","title":"Improved precision and recall met- ric for assessing generative models.Advances in neural in- formation processing systems, 32, 2019","venue":null,"work_id":"19ca2b67-6779-4276-a05f-fb31d8e23f56","year":2019},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:35.158860Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:d293c7bb57f32e3f228c7e2ee50cd72dc60f896ff8540cb025f609dda853ff63","observation_id":"2f0fb4e6-3915-43ea-9cce-5fe35cc8b38d","resolution":{"observed_at":"2026-08-07T13:24:49.602742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:49.073509Z","title":"Distri- fusion: Distributed parallel inference for high-resolution dif- fusion models","venue":null,"work_id":"ec6691b1-9efa-4031-a17f-47ed7f49a429","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:35.247366Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:bae6b3e810ddf95b91866277d9e3144921969dffdef5a51ca74bd6efc61f8d5a","observation_id":"33fee25d-01d9-4960-849b-772bb21e052d","resolution":{"observed_at":"2026-08-07T13:24:49.297845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:48.863109Z","title":"Faster diffusion: Rethinking the role of unet encoder in diffusion models","venue":null,"work_id":"12befeae-6f3e-442a-8953-9b0f433142ca","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:35.331445Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:fb9754a72741cc6b719837cb265007b940215df0fe163ce087ebdec48c5965f5","observation_id":"80ca6035-12a6-480f-804e-0d0197367de1","resolution":{"observed_at":"2026-08-07T13:24:48.964267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:48.547448Z","title":"Styledif- fusion: Prompt-embedding inversion for text-based editing","venue":null,"work_id":"e6828520-8e91-48c9-aec0-883201acf656","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:35.464170Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:5cbdaffca12e589329645a45d49bb354936560d0fc8485112f49142a2977e521","observation_id":"262c08a6-1401-4a33-b58f-54b85233c2d9","resolution":{"observed_at":"2026-08-07T13:24:48.700211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:48.332438Z","title":"Q-diffusion: Quantizing diffusion models","venue":null,"work_id":"37a89deb-a2e1-4ebf-9f4a-7580fcae557f","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:35.580454Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:d228337f4ba537e33ecea400ef113049cc3642e7b910f21a6077da203ca3d6ed","observation_id":"8e508d25-fd57-4c92-a8c9-dbd345f700f9","resolution":{"observed_at":"2026-08-07T13:24:48.433272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13929","last_updated":"2024-03-02T09:09:32Z","snapshot_observed_at":"2026-07-31T02:56:13.600400Z","submitted_at":"2024-02-21T16:51:05Z","title":"SDXL-Lightning: Progressive Adversarial Diffusion Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13929","snapshot_observed_at":"2026-08-07T13:24:35.711222Z","title":"Sdxl- lightning: Progressive adversarial diffusion distillation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:35.711222Z"},"links":{"cited_paper":"/paper/2402.13929","citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:0f20754c3b4c6d616477db6942360d16c5bd5efffa766117ff10f779e4387eb3","observation_id":"627e9386-6e50-4362-be1c-6aa86befde62","resolution":{"observed_at":"2026-08-07T13:24:35.711222Z","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":"2026-08-07T13:24:35.802723Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:35.802723Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:eb0fbdbcb557f8830b93af3823e9c3303ddf766dc41bf9e3052632a69c1020ee","observation_id":"eab76a6f-1fad-4d3a-aef5-73609b00cd90","resolution":{"observed_at":"2026-08-07T13:24:35.802723Z","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":"2026-08-07T13:24:47.994963Z","title":"One-prompt-one-story: Free-lunch consistent text-to-image generation using a single prompt","venue":null,"work_id":"0087b99d-ceed-409f-8200-5ed4b21f64cb","year":2025},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:35.942182Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:e40a9b505ad41b78cc4b11e5c77bcc804b40689dee30ed3f6451a30c880316bd","observation_id":"ef17bca0-9584-4b7a-bd9b-d5c6be3c45bb","resolution":{"observed_at":"2026-08-07T13:24:48.138787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:47.725126Z","title":"Instaflow: One step is enough for high-quality diffusion-based text-to-image generation.ICLR, 2024","venue":null,"work_id":"dbe687c0-5e29-4926-bea2-d6b21ee32061","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:36.023556Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:00016850693edeb30c6d37a962dd94f0af299e84fbeaf205700c5eaef02f5f50","observation_id":"e7299d84-bb59-4533-9608-5c621e1c1847","resolution":{"observed_at":"2026-08-07T13:24:47.849899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:36.098801Z","title":"Token caching for diffusion transformer accel- eration.arXiv preprint arXiv:2409.18523, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:36.098801Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:77f0dda392388b29c0c22d08228973a66775dd70b0c9afe738f6b552dbb11150","observation_id":"91c7d3e0-1cd9-4a24-8a38-188bd3b61575","resolution":{"observed_at":"2026-08-07T13:24:36.098801Z","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":"2026-08-07T13:24:36.166388Z","title":"Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.Advances in Neural Information Processing Systems, 35:5775–5787,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:36.166388Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:271d129cba89c5d962ce6d13a72e3282c1a5dbf3dc972b29c806c0e874fb8d4e","observation_id":"0f1b65a7-2789-4f00-ad0e-a06fab3a7bb3","resolution":{"observed_at":"2026-08-07T13:24:36.166388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.01095","last_updated":"2025-05-19T07:56:56Z","snapshot_observed_at":"2026-07-29T20:17:21.488997Z","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":"2026-08-07T13:24:36.266773Z","title":"Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models.arXiv preprint arXiv:2211.01095, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:36.266773Z"},"links":{"cited_paper":"/paper/2211.01095","citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:d0ce039ca8c8270251a321a851042199076ee56438c73022c52cf0d553da00c9","observation_id":"de80827a-02f4-4abe-b597-da95abc3020e","resolution":{"observed_at":"2026-08-07T13:24:36.266773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04378","last_updated":"2023-10-06T17:11:58Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-06T17:11:58Z","title":"Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04378","snapshot_observed_at":"2026-08-07T13:24:36.358461Z","title":"Latent consistency models: Synthesizing high- resolution images with few-step inference.arXiv preprint arXiv:2310.04378, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:36.358461Z"},"links":{"cited_paper":"/paper/2310.04378","citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:527abc1c5c9912729283392f0d442f59c79d763a22774e5d3f851dc3cb283286","observation_id":"894dc13a-9e76-4fcb-ba2b-c5ae4b3d94c1","resolution":{"observed_at":"2026-08-07T13:24:36.358461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.05556","last_updated":"2023-11-09T18:04:15Z","snapshot_observed_at":"2026-07-06T16:45:20.005195Z","submitted_at":"2023-11-09T18:04:15Z","title":"LCM-LoRA: A Universal Stable-Diffusion Acceleration Module","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.05556","snapshot_observed_at":"2026-08-07T13:24:36.467540Z","title":"Lcm-lora: A universal stable-diffusion acceleration module.arXiv preprint arXiv:2311.05556, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:36.467540Z"},"links":{"cited_paper":"/paper/2311.05556","citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:472385a0a4e49dbd90bb10d05fafc3c774b2276364abbcf8b2877fbc9595fb86","observation_id":"b35653d3-08f4-48ce-a007-e83299117612","resolution":{"observed_at":"2026-08-07T13:24:36.467540Z","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":"2026-08-07T13:24:47.496163Z","title":"Diff-instruct: A universal approach for transferring knowledge from pre-trained diffu- sion models.NeurIPS, 36, 2023","venue":null,"work_id":"f1d6d7ea-8e4d-48f7-87a2-bd3ece40df9d","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:36.565058Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:924feb945c0a17e361b012ca3d753e0060dcf4f98717de08850a6c7ef6538f7b","observation_id":"0b58079a-7de3-4be7-a13b-1c4d043c5dce","resolution":{"observed_at":"2026-08-07T13:24:47.589841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:47.259265Z","title":"Videofusion: Decomposed diffusion mod- els for high-quality video generation","venue":null,"work_id":"69d360fd-2256-4e94-94bb-56c0978c8583","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:36.661735Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:632b6cdb66d89aa358229453ae103b951d7c81b50e27c5f4fe7527efab338b79","observation_id":"bee1ab19-b7cb-4f53-b56d-841cbfc699ba","resolution":{"observed_at":"2026-08-07T13:24:47.427231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:47.037675Z","title":"Learning-to-cache: Accelerating diffusion trans- former via layer caching.NeurIPS, 2024","venue":null,"work_id":"8319fe3e-1245-4283-b335-e81872d2f027","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:36.792894Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:8ca7a9619568bac490f42e1b4ebb9b751b3b3d7a9d4db65c637199b554bbbd08","observation_id":"56346244-32dc-4ba2-8957-3d5be6d27fc6","resolution":{"observed_at":"2026-08-07T13:24:47.137711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:46.788762Z","title":"Deepcache: Accelerating diffusion models for free.CVPR, 2024","venue":null,"work_id":"91305a62-3376-4c63-8d6d-3b84dda30c92","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:36.884287Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:1f244d30ed5afaa7c00fdae667096cc57cece4bf4db061d0e88dfcc500fef752","observation_id":"0a15b549-7fa6-4076-9e28-a5504e257265","resolution":{"observed_at":"2026-08-07T13:24:46.854812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:36.962465Z","title":"On distillation of guided diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:36.962465Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:43e243ba33dee52937c1d7285794dc12ca827bc53b48423dba592a9354702723","observation_id":"337d1260-7c88-4dc1-a419-fea9ffc36620","resolution":{"observed_at":"2026-08-07T13:24:36.962465Z","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":"2026-08-07T13:24:46.620198Z","title":"T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.AAAI, 2023","venue":null,"work_id":"ac6b6315-f441-45d8-90c5-395e9c3583d2","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:37.050749Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:ff5bb3cf86ddec7ff5a028ede4f92aee3e1752b68a5d70a8d4453bfaf20266d7","observation_id":"a960bf82-7dcf-4110-b123-b5a5f103327c","resolution":{"observed_at":"2026-08-07T13:24:46.679432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:46.390355Z","title":"Reliable fidelity and diversity metrics for generative models","venue":null,"work_id":"4916c0f8-248b-4b3c-af1a-c4c2e0241262","year":2020},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:37.141782Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:826b5595952138306505079e478916a1c67ed32d42814bb4c72a32fbfe77c30e","observation_id":"c3870963-168f-4953-9b48-7ffccdd51f56","resolution":{"observed_at":"2026-08-07T13:24:46.498031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:46.131543Z","title":"Swiftbrush: One-step text-to-image diffusion model with variational score distilla- tion.CVPR, 2024","venue":null,"work_id":"76a818cc-18a4-49bd-aa99-499cb31669f8","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:37.221418Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:a0d8148d049a49c50c8d039993b116822752262abf3d85aed996125d36f6b1c4","observation_id":"6e3dbc80-72b7-4b4c-897b-37ace5b01e06","resolution":{"observed_at":"2026-08-07T13:24:46.238393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:45.801909Z","title":"Jour- neyDB: A Benchmark for Generative Image Understanding","venue":null,"work_id":"95205f77-ede1-44e6-a55c-e2a9685996c6","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:37.336067Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:56afa8044e076bad87999a306635ce0cc013cb4d6bef65f44182f4ca3e9b9287","observation_id":"89ef8fab-7852-4cf8-b6e2-54cbb9c2bc93","resolution":{"observed_at":"2026-08-07T13:24:45.955922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:45.599173Z","title":"Deep equilibrium approaches to diffusion models","venue":null,"work_id":"f3a4d27f-8896-435e-90cf-e66e175f4ac6","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:37.432168Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:5eff7e53f79da0ba4495ce26db44851b065661000c74f2838abf855a88d62f65","observation_id":"9e5ab4f4-0174-43e7-8f93-b416e536ddb7","resolution":{"observed_at":"2026-08-07T13:24:45.717862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:45.282637Z","title":"Barron, and Ben Milden- hall","venue":null,"work_id":"3833d6bb-441b-453b-accf-931487f6f6c0","year":null},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:37.532900Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:ae6de178e772251d71c38c773cee969f7a8cca9d4c4be2a148df0aa39c46b23a","observation_id":"c484aaba-0b92-476c-9f6a-a3e60884e6a6","resolution":{"observed_at":"2026-08-07T13:24:45.428205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:44.950672Z","title":"Hyper-sd: Trajectory segmented consistency model for efficient image synthesis","venue":null,"work_id":"b8ae2785-5104-4924-900e-419548104461","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:37.609686Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:43fba96565b5e741c5665e64ff043686926a40f029d455bea6f46f173aab28a3","observation_id":"d2f41045-ddcb-4046-9adf-b1fbaafc6d1c","resolution":{"observed_at":"2026-08-07T13:24:45.080216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:44.558340Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":"039e7ea1-7ebc-4355-b4c7-406f1a4bbd22","year":2022},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:37.688978Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:d0ddc2f33d709200a858e7718cfa674b6937edef42e89ec69c93277393de02fb","observation_id":"3795ceae-f19d-4b71-9182-08fb3cc22cc1","resolution":{"observed_at":"2026-08-07T13:24:44.723253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:44.217483Z","title":"U- net: Convolutional networks for biomedical image segmen- tation","venue":null,"work_id":"d2669acb-521f-4fcf-9915-ce9871453923","year":2015},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:37.782712Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:f30ac2201f08f537dab072316f76cf26bf96505ff0926bafc6cfe3030ef0d7fb","observation_id":"2e61d66f-7492-4708-99f8-96d07b4ac305","resolution":{"observed_at":"2026-08-07T13:24:44.377661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:37.887801Z","title":"Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:37.887801Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:fe8c9ebc4347d9a51aa07f600324b2ae7dabedb1080fb40892bfa90057ade4d0","observation_id":"602a16d1-cbc3-43f9-a89a-f054af35ea20","resolution":{"observed_at":"2026-08-07T13:24:37.887801Z","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":"2026-08-07T13:24:43.839304Z","title":"Pho- torealistic text-to-image diffusion models with deep lan- guage understanding.NeurIPS, 2022","venue":null,"work_id":"b1299da4-f510-4a87-9d1a-7a5fc50df03e","year":2022},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:38.004499Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:3642aca33e0a44d273f2972f9da87fe7aee057c4431ac606531971965fc624fd","observation_id":"91f9d642-eb95-438e-9c2a-50bbf1170d15","resolution":{"observed_at":"2026-08-07T13:24:44.024596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:43.611358Z","title":"Progressive distillation for fast sampling of diffusion models.ICLR, 2022","venue":null,"work_id":"553848fe-b463-44cb-9c37-49aeeae2442d","year":2022},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:38.113403Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:de4e4d413cfbc53605ee65aad1f7c90045bc6e010423e1b03aa3d6f95236fca1","observation_id":"af592e9c-8edf-4873-a698-a5b0127d6301","resolution":{"observed_at":"2026-08-07T13:24:43.694255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:43.333261Z","title":"StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis.International Conference on Machine Learning, 2023","venue":null,"work_id":"93b9abf7-18d8-40b9-a567-9b69153aeca4","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:38.201805Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:c736cdc3c022c457cff63855c6afaed3035ff5fb4c2c71659149a7c137ba16c2","observation_id":"7211b0bc-bd9f-479a-91a1-5819cb0fd1ea","resolution":{"observed_at":"2026-08-07T13:24:43.581383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:43.201100Z","title":"Adversarial diffusion distillation.ECCV, 2024","venue":null,"work_id":"54183d0d-e22a-4943-9d79-184371363140","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:38.249515Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:4f6f3466df8e114a7ab7e3fb524e4c4ccd1a1ae07c3470bfa39741bd52be677d","observation_id":"0bda07cd-d4f6-461f-86a9-bbded0cddd16","resolution":{"observed_at":"2026-08-07T13:24:43.240708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.01425","last_updated":"2024-07-01T16:14:37Z","snapshot_observed_at":"2026-08-03T16:18:03.005579Z","submitted_at":"2024-07-01T16:14:37Z","title":"FORA: Fast-Forward Caching in Diffusion Transformer Acceleration","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01425","snapshot_observed_at":"2026-08-07T13:24:38.398611Z","title":"Fora: Fast-forward caching in diffusion transformer acceleration.arXiv preprint arXiv:2407.01425,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:38.398611Z"},"links":{"cited_paper":"/paper/2407.01425","citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:5707373d8d0145b38846f4836a9913905301533cb852aee7580dc95bbc9cd801","observation_id":"357bbc20-a65d-4961-be9b-cb221f0f2775","resolution":{"observed_at":"2026-08-07T13:24:38.398611Z","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":"2026-08-07T13:24:43.064375Z","title":"Post-training quantization on diffusion models","venue":null,"work_id":"860346b4-13e9-4775-8dd4-73a51c91eada","year":1972},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:38.502967Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:253d08e29836ff146d4da1d3a5d079e499ae1aa7180830e76d830d355cc71371","observation_id":"34e9ee54-8278-4b6f-a6d9-e9cb9ad3b327","resolution":{"observed_at":"2026-08-07T13:24:43.118777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:42.953216Z","title":"Denois- ing diffusion implicit models","venue":null,"work_id":"1a41e74f-733d-4275-b58c-ecbdc2a51aa0","year":2021},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:38.614784Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:ad53b20676bf74a614bafcd12ebd8406a7f7a58f7ff398bd60971a2957cf6646","observation_id":"7724089c-65b8-4667-b279-b94cf72212de","resolution":{"observed_at":"2026-08-07T13:24:43.018641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:42.832215Z","title":"Score-based generative modeling through stochastic differential equa- tions.ICLR, 2021","venue":null,"work_id":"c981190e-8022-4523-9f8b-f35dbb1240d6","year":2021},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:38.711227Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:4dfc5a98b74f5a9c08733d54133934d36bece03287674994ed63bb93eccba6bc","observation_id":"3ae10735-1eec-4dca-8414-ccf6fa4fad21","resolution":{"observed_at":"2026-08-07T13:24:42.888920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:42.701399Z","title":"Consistency models","venue":null,"work_id":"c92c5c48-fe97-4cec-ac42-c57359522f39","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:38.811385Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:58753de98fa27b05b52b8e1d1993d02a747b13d8bfa001f0614f87578a51bc84","observation_id":"553d6eb5-1456-4ac1-974f-9653b1de1a9e","resolution":{"observed_at":"2026-08-07T13:24:42.752127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:42.563840Z","title":"F3- pruning: A training-free and generalized pruning strategy to- wards faster and finer text-to-video synthesis","venue":null,"work_id":"0766103c-2364-44f3-88f8-314b3dd09519","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:38.913761Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:352f9bce2fc7d24c318eeb239702b6d851c034bd48b00f1ae7a62afa146eb4bb","observation_id":"143acee0-6e09-4139-b4ce-e9e32dfe3774","resolution":{"observed_at":"2026-08-07T13:24:42.618901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:42.444870Z","title":"Emergent correspondence from image diffusion.NeurIPS, 2023","venue":null,"work_id":"d0cea15b-ead0-4901-b46e-48e804391c87","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.038687Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:5d6f68328197832b087f537d4cfed0623725edad2240c7343b2dddd8f213bcef","observation_id":"25b864b8-b075-49e6-9f9f-6d20c70f4c1e","resolution":{"observed_at":"2026-08-07T13:24:42.487977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:42.315491Z","title":"Plug-and-play diffusion features for text-driven image-to-image translation","venue":null,"work_id":"4759a820-eae1-4b4f-bf42-e0f2e3a4bf53","year":1921},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.108186Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:358a082f611bfd1e1734d95591d27a31a200d04dcf8301b37f3b301e9dbbc030","observation_id":"fab4b36c-e18d-4ed7-954d-a11fd3d1c45b","resolution":{"observed_at":"2026-08-07T13:24:42.372797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:42.198197Z","title":"Phased consistency model.NeurIPS, 2024","venue":null,"work_id":"8eb3c450-11c7-4e3b-a6c0-708671de63ba","year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.224731Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:b6e66e55e7be95f623a2173dff2cf7ced5212c8e159bdfbbaacd71c8f2bdf59d","observation_id":"01554a70-78c0-4a78-bc33-1891e4b35391","resolution":{"observed_at":"2026-08-07T13:24:42.250081Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:42.083289Z","title":"Yeh, and Greg Shakhnarovich","venue":null,"work_id":"3e4b988e-1abd-45f9-9d11-836bcf76f36e","year":null},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.295931Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:fd3583ca4182511213df1d96a4e3fe9621fa93137a204e1f1f8283a31f4d4b39","observation_id":"d34edc1d-f51d-44c8-99a6-f1e7deada659","resolution":{"observed_at":"2026-08-07T13:24:42.134439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14430","last_updated":"2025-12-03T10:59:23Z","snapshot_observed_at":"2026-08-04T10:57:26.733885Z","submitted_at":"2024-05-23T11:00:07Z","title":"PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14430","snapshot_observed_at":"2026-08-07T13:24:39.423874Z","title":"Pipefusion: Displaced patch pipeline parallelism for in- ference of diffusion transformer models.arXiv preprint arXiv:2405.14430, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.423874Z"},"links":{"cited_paper":"/paper/2405.14430","citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:91ce8eb7d069a38d60e94a011ea0a18f68029163c50911fe5599690884d9345f","observation_id":"97e504e6-d746-4a73-9b3b-008c97d8c766","resolution":{"observed_at":"2026-08-07T13:24:39.423874Z","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":"2026-08-07T13:24:41.883256Z","title":"Dynamic prompt learning: Ad- dressing cross-attention leakage for text-based image edit- ing.NeurIPS, 2023","venue":null,"work_id":"9e113271-dbad-4694-bc64-c6648c3d5947","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.494410Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:13abe29ccdf1f81c2a7846bf14c6dd14d9fecfee0f4acaf35d8213c125590576","observation_id":"1a2a6309-b992-4c06-8c40-45e40ae13fe9","resolution":{"observed_at":"2026-08-07T13:24:41.969997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:41.691654Z","title":"Multi-class textual-inversion secretly yields a semantic-agnostic classifier","venue":null,"work_id":"10d74d1d-0489-4d57-ab69-e787d87004cb","year":2025},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.589380Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:1c916eebe699ee43719c31f90095a4c2e6c65cd1dd9db73f7d6ce0aa86bff9bb","observation_id":"94ead676-a289-4b6b-bc2f-de8c1b8b2c85","resolution":{"observed_at":"2026-08-07T13:24:41.775773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:41.453249Z","title":"ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation.NeurIPS, 2023","venue":null,"work_id":"1fdf3af7-12f3-4a25-ab82-2125b0684107","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.653811Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:a32e3472740f83b53ac2425cc86fd32b7b51f46ac10cbff496a40468b3c47483","observation_id":"7f36c5ca-2bf1-49b2-b7ca-6d15854495eb","resolution":{"observed_at":"2026-08-07T13:24:41.560509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:41.287340Z","title":"Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation","venue":null,"work_id":"3e62aa2d-09cf-4b95-b71c-a7d4c48fce84","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.705364Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:d267ebb5ff10ba0525acafa08ad5b58911e8216b39978d5b461abf4d15928cab","observation_id":"b870d1b5-f630-4f5b-8abb-2664dbd5e6bd","resolution":{"observed_at":"2026-08-07T13:24:41.370679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:39.806896Z","title":"One-step diffusion with distribution matching distillation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.806896Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:a19451453fdb8ee575ba593c63c665740825ff7ec5048c733b5364edb7a2c63c","observation_id":"0eae30c3-716c-474c-b430-9c66d05d06ef","resolution":{"observed_at":"2026-08-07T13:24:39.806896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.10789","last_updated":"2022-06-22T01:11:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-06-22T01:11:29Z","title":"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.10789","snapshot_observed_at":"2026-08-07T13:24:39.862109Z","title":"Scaling autoregres- sive models for content-rich text-to-image generation.arXiv preprint arXiv:2206.10789, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.862109Z"},"links":{"cited_paper":"/paper/2206.10789","citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:9adb31c32a3e5b0d615a8f6e06aab97ade73cbd950f14708f402a272c39f3ac5","observation_id":"81b0344d-10a9-4bc5-b6e1-085584823e6b","resolution":{"observed_at":"2026-08-07T13:24:39.862109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.11098","last_updated":"2025-03-05T13:28:29Z","snapshot_observed_at":"2026-07-06T18:01:23.297512Z","submitted_at":"2024-04-17T06:32:42Z","title":"LAPTOP-Diff: Layer Pruning and Normalized Distillation for Compressing Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.11098","snapshot_observed_at":"2026-08-07T13:24:39.944356Z","title":"Laptop-diff: Layer pruning and normalized dis- tillation for compressing diffusion models.arXiv preprint arXiv:2404.11098, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:39.944356Z"},"links":{"cited_paper":"/paper/2404.11098","citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:5dbcca41fe148e248c2db3e64ad3d2c91a10e630b76c453d24c61795da807244","observation_id":"8dedb314-c9f8-4fcc-9648-18b8ed4f418a","resolution":{"observed_at":"2026-08-07T13:24:39.944356Z","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":"2026-08-07T13:24:41.121943Z","title":"A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence.NeurIPS, 2023","venue":null,"work_id":"a31ba32a-42ae-4698-ae22-00b4458ff887","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:40.004715Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:7520ec732f1a3a0d5643d040edb6da6d4e654a2b69f517e0c1d651d3d05df03b","observation_id":"002501ad-6197-4a37-9567-844237d62057","resolution":{"observed_at":"2026-08-07T13:24:41.176601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:40.988487Z","title":"Adding conditional control to text-to-image diffusion models, 2023","venue":null,"work_id":"30d9ecad-e19c-434b-9508-4a82668bbd23","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:40.068249Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:db052927951086c4b0604406c03fbd52e7977434eec9c685df8525f5ba73595a","observation_id":"a150d694-beb2-4d57-bf4c-9d3959362e5a","resolution":{"observed_at":"2026-08-07T13:24:41.045871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:24:40.818845Z","title":"Fast sampling of diffusion models via operator learning","venue":null,"work_id":"8aa5fd24-a47a-47de-b492-10f06ca695eb","year":2023},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:40.150874Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:6689a8e4b8efcd83251e46e51f72c5268fa4ccd0134f884319dd2bc015cb75cd","observation_id":"f14f1969-e178-4a55-84bd-01015c6524ae","resolution":{"observed_at":"2026-08-07T13:24:40.901035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19159","last_updated":"2024-04-15T13:51:17Z","snapshot_observed_at":"2026-08-04T12:42:29.134022Z","submitted_at":"2024-02-29T13:44:14Z","title":"Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19159","snapshot_observed_at":"2026-08-07T13:24:40.211794Z","title":"StudioGAN","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:40.211794Z"},"links":{"cited_paper":"/paper/2402.19159","citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:244010b53b2ae06a54fef528da5187c0de51eb42e8140450e428099575a6b2c9","observation_id":"ec3e79bf-8a10-434e-9440-ffbd30454579","resolution":{"observed_at":"2026-08-07T13:24:40.211794Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-07T13:24:32.934853Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T13:24:32.934853Z"},"links":{"citing_paper":"/paper/2505.21960"},"observation_digest":"sha256:590fd60f9f6d99859e311e957f4cc6cca50ccca4e62aa4a5399c2e3cf3c99c6b","observation_id":"836c852d-78d4-4f34-904e-4114cd0488a1","resolution":{"observed_at":"2026-08-07T13:24:32.934853Z","resolver_source":null,"status":"parse_uncertain"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.21960","last_updated":"2025-05-28T04:23:22Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T13:16:22.197005Z","submitted_at":"2025-05-28T04:23:22Z","title":"One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models"},"reference_resolution":{"displayed":87,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":26,"verified_exact":0,"verified_fuzzy":59},"total_outbound_references":87},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 0 inbound Pith citation observations for arXiv:2505.21960."}