{"as_of":"2026-08-13T11:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b561e223b906fc8c4a47ae80ff6d28d9ba2d9502b290ed6e65cd86cc84ed32f9","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T12:34:42.896602Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2411.17106/citation-record","integrity":"/paper/2411.17106/integrity","json":"/paper/2411.17106/citation-record.json","paper":"/paper/2411.17106"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.466935Z","title":"Ntire 2017 chal- lenge on single image super-resolution: Dataset and study","venue":null,"work_id":"bf5064bf-32dd-4bf7-b060-e1e63f88047a","year":2017},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.713902Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:9bbd8ffd192f5e5126b45d081742e268abf1970d9f0a495de0778f9434201b7b","observation_id":"17124fb4-5307-4e4a-a0bf-1aea41593b8d","resolution":{"observed_at":"2026-08-12T12:34:43.470731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.456715Z","title":"Cross aggregation transformer for image restoration","venue":null,"work_id":"97505f7e-55a0-4a74-8a5a-ab7ea35828fe","year":2022},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.717867Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:ee2bc64c40da608219bb45fc5b16ed932064a5b889d28d35a08092584606c309","observation_id":"2a8afa5f-79a2-4b79-8b10-78271932c434","resolution":{"observed_at":"2026-08-12T12:34:43.460619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.446709Z","title":"Cross aggregation transformer for image restoration","venue":null,"work_id":"4783d12e-0d0b-4089-b2d6-c0e02f45d06a","year":2022},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.721574Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:2488afa0efb16a2b39653900e49a53d19563963c7280d3823bd8827a1b2adb65","observation_id":"19996bf5-bf93-4563-87e8-1135c645280a","resolution":{"observed_at":"2026-08-12T12:34:43.450215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.436265Z","title":"Dual aggregation transformer for image super-resolution","venue":null,"work_id":"2eafdd10-9417-4341-be74-e88f5d9804e7","year":2023},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.725511Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:781a1892581144b663ab2d3e5566c2d3d3a18128baeaceb339f786451df1ce9e","observation_id":"85ef0c2b-1b15-4e92-8aea-02120bf0bd86","resolution":{"observed_at":"2026-08-12T12:34:43.439991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.426207Z","title":"Image quality assessment: Unifying struc- ture and texture similarity","venue":null,"work_id":"35aeb6a4-d6f8-4b1a-9f34-e83519d6c0a0","year":2020},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.729350Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:5140419ba23b28e849eb7c69fb48cde03e2bd03e6cab76f92d8dee300e95b2b2","observation_id":"1c905ec1-c54d-4143-bae0-df71c09d3c49","resolution":{"observed_at":"2026-08-12T12:34:43.429750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.416075Z","title":"Towards accurate post-training quantization for vision trans- former","venue":null,"work_id":"c4d4b068-cb4c-48fd-a293-c8ae04653e58","year":2022},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.732852Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:b7000ba2be603d64ee66b0e2f7bbfe451dc4fd458f1ba32d567b9bad5166cbc0","observation_id":"0ef45c12-2def-479d-9b44-cc02bf7ef5b2","resolution":{"observed_at":"2026-08-12T12:34:43.419717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.406025Z","title":"Image super-resolution using deep convo- lutional networks","venue":null,"work_id":"ab0b42d1-a8a1-4c87-8b7f-c62b82907afe","year":2015},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.736402Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:2c982aa919fa5f7077261e72b1831bda77dfbef0f36029e7b12c32f720cf8f12","observation_id":"1a6cd110-2ebd-477f-a03a-28a335cb248c","resolution":{"observed_at":"2026-08-12T12:34:43.409369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.396306Z","title":"Learned step size quantization","venue":null,"work_id":"56af4828-3ae4-4e18-8b0d-320cb35fa1c8","year":null},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.739941Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:21ea0f8fc423c72bc910806f99e6c86cd335edadc4fd356dbcab5531b2523e7c","observation_id":"6001ce9d-148a-4d3b-9a63-4f5dea20ff7c","resolution":{"observed_at":"2026-08-12T12:34:43.399820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03270","last_updated":"2024-04-13T07:33:57Z","snapshot_observed_at":"2026-08-13T05:57:07.688871Z","submitted_at":"2023-10-05T02:51:53Z","title":"EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03270","snapshot_observed_at":"2026-08-12T12:34:42.743625Z","title":"Efficientdm: Efficient quantization-aware fine-tuning of low-bit diffusion models.arXiv preprint arXiv:2310.03270, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.743625Z"},"links":{"cited_paper":"/paper/2310.03270","citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:43e586736967da33ad3969b21f5a685482d49b197e94f908e7c50cc53a4ab85f","observation_id":"b1fb498a-8e3d-4562-89f5-9c96b39e877e","resolution":{"observed_at":"2026-08-12T12:34:42.743625Z","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":"2026-08-12T12:34:43.386562Z","title":"Ptqd: Accurate post- training quantization for diffusion models","venue":null,"work_id":"bd9204bb-14cd-44c9-ad9c-aff9fa7d16e7","year":null},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.747459Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:d275c8500aca91343487840c2f19da88577b1bd40ab7cbce3f369de213b83018","observation_id":"43261d5f-5bb1-47fc-9742-afb6cfe1e496","resolution":{"observed_at":"2026-08-12T12:34:43.390113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.376731Z","title":"Accurate post training quantiza- tion with small calibration sets","venue":null,"work_id":"25658304-8840-465c-b8d4-99d9222e6671","year":2021},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.750960Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:43b52bc36b7779a9c65574e3a050395f6906f63d8be3a3f383b16181519231ed","observation_id":"cb4ca918-4f33-4288-ae9f-264197433563","resolution":{"observed_at":"2026-08-12T12:34:43.380490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.366466Z","title":"Quantization and train- ing of neural networks for efficient integer-arithmetic- only inference","venue":null,"work_id":"de659f54-4f85-4b12-a5ea-08da0d1849a7","year":2018},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.754344Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:9e57157dd8d8b767ff147022ce877ae2a901fed584bae80049fc0e50a6bf7a83","observation_id":"0387e517-e230-439d-9420-55261c49e98b","resolution":{"observed_at":"2026-08-12T12:34:43.370611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.357194Z","title":"Real-world super- resolution via kernel estimation and noise injection","venue":null,"work_id":"cfb070f1-5453-4ebf-bda2-35fc6c0993ed","year":2020},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.757754Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:fd9cc33ce3ee94f21e176cc56de27478ef4fda02e26beabd7b4fab4b51b0f940","observation_id":"0809a4bc-486c-4015-8d9d-721827011230","resolution":{"observed_at":"2026-08-12T12:34:43.360590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.346653Z","title":"Musiq: Multi-scale image quality transformer","venue":null,"work_id":"0ea7eb7c-27a3-464d-a13d-c5412fab33a6","year":2021},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.760966Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:3d3ff07e670e662f5b8337501325b959f12cac200ebf70de7f4a7cf323162362","observation_id":"f730f3a0-7975-461f-92fb-5d9d4c4b1223","resolution":{"observed_at":"2026-08-12T12:34:43.350389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.336723Z","title":"Ac- curate image super-resolution using very deep convo- lutional networks","venue":null,"work_id":"fab964e6-3b2f-4f15-a0df-1909bb65818a","year":2016},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.764313Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:fa882207a0c990a548e57e7930b14beb96f3272fe9ab5edbb269959219eb5393","observation_id":"91d4eea9-fe02-43b5-b1a3-70988bc19110","resolution":{"observed_at":"2026-08-12T12:34:43.340249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.326389Z","title":"Low-bit quantization of neural networks for efficient inference","venue":null,"work_id":"33aa58f1-18e3-480b-97b3-43eb3dee5ff8","year":2019},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.767591Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:f5bac75d2510b1d24341c5587f679ebbf6692c00df6740d18e8eb5a27a94c73b","observation_id":"67e8cccd-93c0-41e8-a835-bda845562f58","resolution":{"observed_at":"2026-08-12T12:34:43.329924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04224","last_updated":"2025-03-09T16:37:34Z","snapshot_observed_at":"2026-08-12T22:30:03.965972Z","submitted_at":"2024-10-05T16:41:36Z","title":"Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminator","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04224","snapshot_observed_at":"2026-08-12T12:34:42.771018Z","title":"Distillation-free one-step diffusion for real-world image super-resolution","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.771018Z"},"links":{"cited_paper":"/paper/2410.04224","citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:20b6ba5ffafae0d6b766db06ff0c17054c8b2f44eb148694ca28139875e2b8b6","observation_id":"51fc6812-c983-4e22-965d-23b0960b2885","resolution":{"observed_at":"2026-08-12T12:34:42.771018Z","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":"2026-08-12T12:34:43.316318Z","title":"Q-diffusion: Quantizing diffusion models","venue":null,"work_id":"238f8a40-63d6-4f71-8688-6cf8da2465b1","year":2023},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.774745Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:82b44252f632dfbd361aa134ccb6b080633f23a7af16ac75afb883fcd8147e9a","observation_id":"44dde6e3-7c00-4142-804d-2ea442b2cc2f","resolution":{"observed_at":"2026-08-12T12:34:43.319690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.05426","last_updated":"2021-07-25T09:34:39Z","snapshot_observed_at":"2026-08-10T19:57:21.389202Z","submitted_at":"2021-02-10T13:46:16Z","title":"BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.05426","snapshot_observed_at":"2026-08-12T12:34:42.778111Z","title":"Brecq: Pushing the limit of post-training quan- tization by block reconstruction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.778111Z"},"links":{"cited_paper":"/paper/2102.05426","citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:37c1658ce8261bfaa85c183662bdda81adcba66b4a9bdc6fdbfd2146c1ce64c5","observation_id":"b659237e-d9db-443a-a87f-7ce19a20df2e","resolution":{"observed_at":"2026-08-12T12:34:42.778111Z","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":"2026-08-12T12:34:43.306233Z","title":"Q-dm: An efficient low-bit quan- tized diffusion model","venue":null,"work_id":"0518a587-fde6-45d2-9498-cf5119b8dd88","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.782044Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:bbeab3db5a8b56f05bbfbd8e678512802680f9016003c7ef7989e12f44d703f7","observation_id":"b042eb6a-43c3-4428-9e53-6f5ba517fb55","resolution":{"observed_at":"2026-08-12T12:34:43.309746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.296407Z","title":"Swinir: Image restoration using swin transformer","venue":null,"work_id":"624315ab-3647-4918-af99-8fcb88300262","year":2021},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.785343Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:46f102e01525b07bd90a34895f90553d93b32560c64cb1abbd8413abca4c75cd","observation_id":"79e52d67-6ece-4c38-a160-8ac93b1e2967","resolution":{"observed_at":"2026-08-12T12:34:43.300003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.286549Z","title":"Awq: Activation-aware weight quantization for on-device llm compression and acceleration.Proceedings of Ma- chine Learning and Systems, 2024","venue":null,"work_id":"52020e9f-f145-4297-a20f-2a8bf161f9c9","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.788702Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:38f1f349284d9dc3a9a0fb5dd1880fc9fa12f9752590cf9565a73cecf4619b57","observation_id":"1e2bdc18-a19c-4b2e-95ac-ddc72e837e13","resolution":{"observed_at":"2026-08-12T12:34:43.290237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.276649Z","title":"Diffbir: Towards blind image restoration with generative diffusion prior","venue":null,"work_id":"d9eea072-dfec-4e4a-807e-afbb54b56db5","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.792011Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:0f1ebd58244e4984b7aae96b655b69a2cc9d6243b8e86bb9cec9f144b3340ada","observation_id":"d1dadc90-59c3-4214-8d0b-8e74f730f736","resolution":{"observed_at":"2026-08-12T12:34:43.280211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.265330Z","title":"Nonuniform-to-uniform quantization: Towards accurate quantization via gen- eralized straight-through estimation","venue":null,"work_id":"76428f51-29a8-4a93-8677-8b39c70499f8","year":2022},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.795197Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:818868b0b0133848040ed5b1ea6bf90cb0f4110576c5a68ca063d801f090e8b5","observation_id":"a1ee1b6a-13ae-4920-b8c3-684c5e24ea76","resolution":{"observed_at":"2026-08-12T12:34:43.269365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.03788","last_updated":"2023-12-06T11:10:55Z","snapshot_observed_at":"2026-08-13T05:08:49.990146Z","submitted_at":"2023-12-06T11:10:55Z","title":"SmoothQuant+: Accurate and Efficient 4-bit Post-Training WeightQuantization for LLM","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.03788","snapshot_observed_at":"2026-08-12T12:34:42.798520Z","title":"Smoothquant+: Ac- curate and efficient 4-bit post-training weightquantiza- tion for llm","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.798520Z"},"links":{"cited_paper":"/paper/2312.03788","citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:daa4027c476d59340e2c44f9dc6c89da2c6675747e8527f1e0cf7a078eca39af","observation_id":"9481735a-40fc-4f12-803b-097a98035315","resolution":{"observed_at":"2026-08-12T12:34:42.798520Z","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":"2026-08-12T12:34:43.255107Z","title":"Quantsr: ac- curate low-bit quantization for efficient image super- resolution","venue":null,"work_id":"a687aa4b-3ffa-4425-be1d-29bc84092403","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.802136Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:0bcc3b394b5c8c7fcf6b440facb28e4d3fcb3f82590116b1aef1845d64735822","observation_id":"3533fd46-e859-473f-8eb0-435b3996dc6e","resolution":{"observed_at":"2026-08-12T12:34:43.258769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.244336Z","title":"High- 9 resolution image synthesis with latent diffusion mod- els","venue":null,"work_id":"6cf58c19-e7d1-4fc5-8ea8-5f8c70d383f6","year":2022},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.805134Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:c925934aefb973264c5985deec601680d37a27d98df7ffcdec33ffb898ee0a2e","observation_id":"7197b5f5-79a8-4a38-a7bc-0fb29ead49e4","resolution":{"observed_at":"2026-08-12T12:34:43.248353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.233473Z","title":"Post-training quantization on diffusion models","venue":null,"work_id":"31242bfc-31e5-49a7-964f-7edfc87fdb91","year":2023},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.808506Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:36dd29be5b4d487f753403a6098441b2579e1cdf9dd5ba02d8ac1476b05aa90e","observation_id":"327e536d-48ee-4265-9c91-3e489948a2dd","resolution":{"observed_at":"2026-08-12T12:34:43.237020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.222648Z","title":"Omniquant: Omnidi- rectionally calibrated quantization for large language models","venue":null,"work_id":"0653570e-01a3-4d00-9a81-90bad51682c7","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.811806Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:fa23a6444185de2f5c1cb97b441bbb1340576431c4a8f3332eb6b39909482a90","observation_id":"6f0ee9bd-13e0-4ef8-80d2-7e208180b8a3","resolution":{"observed_at":"2026-08-12T12:34:43.226298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.211613Z","title":"Temporal dynamic quanti- zation for diffusion models","venue":null,"work_id":"27179c6f-51b9-4c3f-b514-6cda2a4d9441","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.815037Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:3bfe6327cbe35c00ebe4e28a2fafc50af19d4ea1e4c66051d360d3dd00583681","observation_id":"e04ef787-c852-4f4c-b2f3-9daa4fdbaa9f","resolution":{"observed_at":"2026-08-12T12:34:43.215782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.201162Z","title":"Denoising diffusion implicit models","venue":null,"work_id":"f732e9bc-0f0a-4236-94af-13692ce8a92c","year":2021},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.818562Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:8d382a592ee314bdd7a04a454e45a865560c9a7a338f942188a9e5a397ab53a6","observation_id":"fb0daccc-8a99-4d7e-970c-0eb9d264034a","resolution":{"observed_at":"2026-08-12T12:34:43.204659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.190636Z","title":"Consistency models","venue":null,"work_id":"11651c52-d72b-46b2-ac5c-32189299e66f","year":2023},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.822024Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:856d84b93942cd27e65a79307ec37b6df2ee1a0b6915d5067463a02c8af35d2b","observation_id":"74c742d1-eed6-49eb-9616-b7af13b0d22d","resolution":{"observed_at":"2026-08-12T12:34:43.194343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.179805Z","title":"Towards accurate post- training quantization for diffusion models","venue":null,"work_id":"6a0c3b9e-ab5f-4e91-a188-958f84d5b8ea","year":null},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.825429Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:20deb032bd5ac879591429c54a3ac998276b1682497e6275860ef5a60be32d5f","observation_id":"ad3fae1d-f687-406a-bcb9-48f7151f3a49","resolution":{"observed_at":"2026-08-12T12:34:43.183394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03666","last_updated":"2025-07-15T15:33:13Z","snapshot_observed_at":"2026-08-13T04:25:32.070460Z","submitted_at":"2024-02-06T03:39:44Z","title":"QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03666","snapshot_observed_at":"2026-08-12T12:34:42.829238Z","title":"Quest: Low-bit diffu- sion model quantization via efficient selective finetun- ing","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.829238Z"},"links":{"cited_paper":"/paper/2402.03666","citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:206f1555b4d94c639cab3723b472e1ae23b2cf2b60ee77238f17505037379c6e","observation_id":"fed9aa07-2c05-4200-b85f-97b2b78f8592","resolution":{"observed_at":"2026-08-12T12:34:42.829238Z","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":"2026-08-12T12:34:42.833060Z","title":"Exploring clip for assessing the look and feel of im- ages","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.833060Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:090a97503a34aad50b54489298cbac1d2d177de2678df3e3e1719981b60ab0ec","observation_id":"d721407f-733b-4c20-8e3a-8d4a9087c368","resolution":{"observed_at":"2026-08-12T12:34:42.833060Z","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":"2026-08-12T12:34:43.162413Z","title":"Exploiting diffusion prior for real-world image super-resolution","venue":null,"work_id":"7c17e6d2-ce71-4c66-8241-5ab8807251e8","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.836398Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:56462a734b24a27072ce6ac6d8349e7ee89216941f6f477599bd1751c07112a9","observation_id":"88b53d34-79f6-490d-abb2-c96b3d9fd3ca","resolution":{"observed_at":"2026-08-12T12:34:43.166461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.151941Z","title":"Real-esrgan: Training real-world blind super- resolution with pure synthetic data","venue":null,"work_id":"d1fdc871-81d0-4857-a7e5-3ad26e1bc2ad","year":2021},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.839928Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:e7c572b3d0845c8b03b474fa874878602d02632287c5d1cebb925fe068d791d0","observation_id":"102b345d-31c7-4b5d-98cc-744ea5630faf","resolution":{"observed_at":"2026-08-12T12:34:43.155616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.141556Z","title":"Sinsr: diffusion- based image super-resolution in a single step","venue":null,"work_id":"4cb261fe-04b5-4342-a5a4-8dc3f8b16acf","year":null},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.843308Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:58b37e9910983a6453b9db8218c34afc99e1b5c2b89e3dddce40d0f00e5aa552","observation_id":"e8921aeb-cdf2-4bdd-9919-c92eb2caf3a1","resolution":{"observed_at":"2026-08-12T12:34:43.145210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:42.847029Z","title":"Image quality assessment: from error visibility to structural similarity","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.847029Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:65c8deaef66768aba57a42989f7b826524a4eb7b49f8d2f3262c7d8f694309b4","observation_id":"08ad086e-65c8-45db-9429-9a54d4701865","resolution":{"observed_at":"2026-08-12T12:34:42.847029Z","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":"2026-08-12T12:34:43.124655Z","title":"Prolific- dreamer: High-fidelity and diverse text-to-3d genera- tion with variational score distillation","venue":null,"work_id":"08cf9c13-f433-415f-9c4b-0e0ba6671d2c","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.850442Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:555a611f7597279be7524102fc03c3ae907b341f0ec1d2b3fd72e1d33a1715f3","observation_id":"5b17ac20-3f90-44ca-8b13-f4a4d73c8dcc","resolution":{"observed_at":"2026-08-12T12:34:43.128394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.114579Z","title":"Compo- nent divide-and-conquer for real-world image super- resolution","venue":null,"work_id":"ae173d7d-d7ef-4a8d-b9f4-79a3dde3bff4","year":2020},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.853851Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:8f55cfd7a1fa5d991d7b021b189c8f2ed2bcc73b342dfdc0115a6ed8bd2ba046","observation_id":"5b07e530-d412-41fe-bcd4-057bfcb9f156","resolution":{"observed_at":"2026-08-12T12:34:43.118116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.104089Z","title":"One-step effective diffusion network for real- world image super-resolution","venue":null,"work_id":"03f8817d-a3ec-4cad-9271-c3559baaa7ac","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.857205Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:3bf8eb33ec1ab983e359c7c9c2cf56809c5c8f01bdfca20ce8c126cbeee62ab5","observation_id":"f142724e-a91c-4d8b-ae41-1d133d5f0ed7","resolution":{"observed_at":"2026-08-12T12:34:43.107702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.094293Z","title":"Seesr: Towards semantics-aware real-world image super-resolution","venue":null,"work_id":"e6f7d634-bfba-4b68-8e5c-9c3159e1a9b5","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.860499Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:d5e5ed171e699e3023eb4b4bbd4d548d888409cdd23d29c202d6b8cf941fd8fe","observation_id":"34cd5c0d-a4af-4104-84e5-c925e092cb89","resolution":{"observed_at":"2026-08-12T12:34:43.097806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.084435Z","title":"Qa-lora: Quantization-aware low-rank adaptation of large language models","venue":null,"work_id":"cf8fd2f9-d6ac-4193-8629-ac75705596e9","year":null},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.863936Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:29fcaf93c9f7b2177728090dcc1129a3c4afa41a53e41be19cea47b8a6ff9fc7","observation_id":"1252301c-6003-4679-98d0-a1ab71768aec","resolution":{"observed_at":"2026-08-12T12:34:43.088082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.072844Z","title":"Maniqa: Multi-dimension attention network for no-reference image quality assessment","venue":null,"work_id":"1a973964-ad25-4540-9d43-c6937d722592","year":null},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.867185Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:5c30268ba97c81f5b8dccbc8e445fb110fa134fd263030dd3770eec091260be1","observation_id":"66c0e17b-8583-43bd-a9b3-96dcedb0cbe9","resolution":{"observed_at":"2026-08-12T12:34:43.077204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.062489Z","title":"Zero- quant: Efficient and affordable post-training quantiza- tion for large-scale transformers","venue":null,"work_id":"252185ae-cd12-4cfc-bccd-9d2b7b878dba","year":2022},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.870690Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:96fe094f461549f54bcfc23a366d37c7fc904de3c8e1568063faf82c90e4fa60","observation_id":"1f5d59b3-2b80-46bd-b118-8a4e291a02a8","resolution":{"observed_at":"2026-08-12T12:34:43.066179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.051902Z","title":"One-step diffusion with distribution matching distillation","venue":null,"work_id":"978d6295-7c6b-4e35-97c2-c99a0b306ac4","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.873989Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:e85d3ffd51680c1209f42d61cc2929046258f5fa9474c00b6449f1075d8bf123","observation_id":"98c5ecf6-8703-4913-ac37-f9618eb7618d","resolution":{"observed_at":"2026-08-12T12:34:43.055759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.041317Z","title":"Ptq4vit: Post-training quantization framework for vision transformers with twin uniform quantization","venue":null,"work_id":"29e71554-85f3-4102-8acc-b4cb06900d67","year":2022},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.877204Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:5b038d4af999676339977143458c86cefe32a2a65c9fc89cf01bff25370a935b","observation_id":"e84d3d37-7657-4f94-92e8-15deeef999f1","resolution":{"observed_at":"2026-08-12T12:34:43.045137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.029977Z","title":"Designing a practical degradation model for deep blind image super-resolution","venue":null,"work_id":"b232364e-c315-45cc-b184-53d9b241c5c3","year":2021},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.880378Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:a0643c9699d35c9cb20c9541ae85f8e9185cd5d5e4abff3f6792c88c9e2a4a60","observation_id":"5bfe1afe-ac9d-474a-af65-16f4629a98d1","resolution":{"observed_at":"2026-08-12T12:34:43.034044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.019032Z","title":null,"venue":null,"work_id":"7d4da491-6e12-4bf2-a133-df74bec77186","year":2015},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.883584Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:d4144f396fa85ef3f54e5d17aa16cb2bbbb0af3116ffb033492559e7374cff48","observation_id":"95d00a6a-dd97-4c49-a48d-bd714388ae39","resolution":{"observed_at":"2026-08-12T12:34:43.022548Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:43.008441Z","title":"The unreasonable ef- fectiveness of deep features as a perceptual metric","venue":null,"work_id":"8508f4f9-edba-429c-964a-5fbde9ba849a","year":2018},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.886715Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:cc0b3291e34ff4fc156563a0b40034bec9c3652e305d250a5fd444ab1b32aac3","observation_id":"347282b9-0c7b-4c23-95b2-d4ccf143c64a","resolution":{"observed_at":"2026-08-12T12:34:43.012167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:42.997155Z","title":"Image super-resolution using very deep residual channel attention networks","venue":null,"work_id":"454fb6f5-683d-4886-9a80-74d7fb43503b","year":2018},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.890211Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:d08338eabe91b2c4e8b48e81c37f3732c4397aeeb65c9de1179c5909021b9234","observation_id":"827aab33-cf35-4c78-8f14-19edcf01882f","resolution":{"observed_at":"2026-08-12T12:34:43.001097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:42.985624Z","title":"Residual dense network for image super- resolution","venue":null,"work_id":"49a58cc7-faf8-43ab-997f-bd0a76a922c1","year":2018},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.893273Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:27f1a43d38fe15252820b0ba12c5785526944c7c18322979eb5bfa0c56fd4d20","observation_id":"35c05c0b-1253-4322-a37e-13d5ac414f95","resolution":{"observed_at":"2026-08-12T12:34:42.989520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:34:42.971745Z","title":"Unipc: A unified predictor-corrector framework for fast sampling of diffusion models","venue":null,"work_id":"7a5c2b1b-b75a-4b73-8dd9-49a7743923bd","year":2024},"citing_paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T12:34:42.896602Z"},"links":{"citing_paper":"/paper/2411.17106"},"observation_digest":"sha256:631f133515e45cf0def31be0ff5961bc2aae0a00a4c22be8b5392923609068ae","observation_id":"34bed5a2-9571-48ac-9550-a15896c7dd1e","resolution":{"observed_at":"2026-08-12T12:34:42.977629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.17106","last_updated":"2024-12-03T04:14:09Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T12:28:11.393555Z","submitted_at":"2024-11-26T04:49:42Z","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":46},"total_outbound_references":54},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2411.17106."}