{"as_of":"2026-08-11T13:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:08f7a2203af477fffffe0c0cc1bd292bb45961d4c92f2802f673b179087eebf4","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T14:48:16.035237Z","state":"measured"},{"denominator":58,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":58,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T10:47:12.543726Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T15:18:32.876727Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01639","snapshot_observed_at":"2026-08-04T10:47:12.543726Z","title":"Sliderspace: Decomposing the visual capabilities of diffusion models.arXiv preprint arXiv:2502.01639, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.08532","last_updated":"2026-07-21T10:18:45Z","snapshot_observed_at":"2026-08-08T06:26:02.256146Z","submitted_at":"2025-10-09T17:51:03Z","title":"Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T10:47:12.543726Z"},"links":{"cited_paper":"/paper/2502.01639","citing_paper":"/paper/2510.08532"},"observation_digest":"sha256:89d6206ac9d8ebfa9712ca79fd8a94183ebc5e3b024207818d9e586b75e92c36","observation_id":"fac0574d-fe0b-49c5-9c08-8fb6de32b46f","resolution":{"observed_at":"2026-08-04T10:47:12.543726Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"cited_work":{"arxiv_id":"2502.01639","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.01639","snapshot_observed_at":"2026-07-03T15:18:32.876727Z","title":"Sliderspace: Decomposing the visual capabilities of diffusion models","venue":null,"work_id":"ed26e2f2-facb-4a6c-8a7c-64bc049dbf9e","year":2025},"citing_paper":{"arxiv_id":"2604.19953","last_updated":"2026-04-21T20:00:04Z","snapshot_observed_at":"2026-07-06T23:06:31.110859Z","submitted_at":"2026-04-21T20:00:04Z","title":"LatentGandr: Visual Exploration of Generative AI Latent Space via Local Embeddings","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T01:13:28.548337Z"},"links":{"cited_paper":"/paper/2502.01639","citing_paper":"/paper/2604.19953"},"observation_digest":"sha256:fa6b4e9f886576a9cf7092d33dbea6b2ce0cba7ac0429fc67c244705564c42d1","observation_id":"295c4180-7685-4c88-97c8-0479dadc5a20","resolution":{"observed_at":"2026-05-11T13:41:08.615438Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"cited_work":{"arxiv_id":"2502.01639","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.01639","snapshot_observed_at":"2026-07-03T15:18:32.876727Z","title":"Sliderspace: Decomposing the visual capabilities of diffusion models","venue":null,"work_id":"ed26e2f2-facb-4a6c-8a7c-64bc049dbf9e","year":2025},"citing_paper":{"arxiv_id":"2605.11494","last_updated":"2026-05-12T04:10:42Z","snapshot_observed_at":"2026-07-06T23:23:21.034839Z","submitted_at":"2026-05-12T04:10:42Z","title":"STRIDE: Training-Free Diversity Guidance via PCA-Directed Feature Perturbation in Single-Step Diffusion Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-13T02:53:35.519419Z"},"links":{"cited_paper":"/paper/2502.01639","citing_paper":"/paper/2605.11494"},"observation_digest":"sha256:3776ef227229655071c3f6a2f9ccb9f8f46a2250c47724b67ee6753a0bbd1b3a","observation_id":"7c4e459c-cf48-44a7-b40d-32c98eb67b21","resolution":{"observed_at":"2026-05-13T02:57:09.278330Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"cited_work":{"arxiv_id":"2502.01639","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.01639","snapshot_observed_at":"2026-07-03T15:18:32.876727Z","title":"Sliderspace: Decomposing the visual capabilities of diffusion models","venue":null,"work_id":"ed26e2f2-facb-4a6c-8a7c-64bc049dbf9e","year":2025},"citing_paper":{"arxiv_id":"2607.02402","last_updated":"2026-07-22T17:01:15Z","snapshot_observed_at":"2026-08-02T09:02:22.036739Z","submitted_at":"2026-07-02T16:35:52Z","title":"Show Me Examples: Inferring Visual Concepts from Image Sets","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-03T15:12:00.041335Z"},"links":{"cited_paper":"/paper/2502.01639","citing_paper":"/paper/2607.02402"},"observation_digest":"sha256:a2569057e682fbd7b7d9d57ee49e6a504468e85a14de8aa248553d4b2851e77c","observation_id":"80d65b35-2dea-4183-ae99-083bd2bd3b99","resolution":{"observed_at":"2026-07-03T15:18:32.878403Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01639","snapshot_observed_at":"2026-07-12T08:05:49.527175Z","title":"arXiv preprint arXiv:2502.01639 (2025) 5","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.02402","last_updated":"2026-07-22T17:01:15Z","snapshot_observed_at":"2026-08-02T09:02:22.036739Z","submitted_at":"2026-07-02T16:35:52Z","title":"Show Me Examples: Inferring Visual Concepts from Image Sets","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-12T08:05:49.527175Z"},"links":{"cited_paper":"/paper/2502.01639","citing_paper":"/paper/2607.02402"},"observation_digest":"sha256:b94c0616bbbbdb98025e48827b5ba6b0bfe9f8402608e2cdb7bac71c518e055a","observation_id":"6242054c-ae2e-4dc6-a363-fc89c5f671c4","resolution":{"observed_at":"2026-07-12T08:05:49.527175Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01639","snapshot_observed_at":"2026-08-02T09:02:24.462657Z","title":"arXiv preprint arXiv:2502.01639 (2025) 5","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.02402","last_updated":"2026-07-22T17:01:15Z","snapshot_observed_at":"2026-08-02T09:02:22.036739Z","submitted_at":"2026-07-02T16:35:52Z","title":"Show Me Examples: Inferring Visual Concepts from Image Sets","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T09:02:24.462657Z"},"links":{"cited_paper":"/paper/2502.01639","citing_paper":"/paper/2607.02402"},"observation_digest":"sha256:912bbcce37876b3d8975b3d7e470fbeaa010ab9692b2c2a823eef3dc2fcad3ba","observation_id":"9330b7be-03d1-4733-a074-996158755f62","resolution":{"observed_at":"2026-08-02T09:02:24.462657Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.01639/citation-record","integrity":"/paper/2502.01639/integrity","json":"/paper/2502.01639/citation-record.json","paper":"/paper/2502.01639"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:48:16.914620Z","title":"Im- age2stylegan: How to embed images into the stylegan latent space? In Proceedings of the IEEE/CVF inter- national conference on computer vision, pages 4432– 4441, 2019","venue":null,"work_id":"2fa64de0-3dbc-49e1-9259-071493521de6","year":2019},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.789776Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:acfd5f308ae4d0aa918a75e80cbe2c3b7ce4ff399d7b2b443a809eee0f29d052","observation_id":"ad44ff71-fb66-43b4-bab7-5a4e822199e4","resolution":{"observed_at":"2026-08-09T14:48:16.920198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.898709Z","title":"Styleflow: Attribute-conditioned explo- ration of stylegan-generated images using conditional continuous normalizing flows","venue":null,"work_id":"4195a91e-269b-4ef0-9dfc-25796e522922","year":2021},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.794530Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:4e56454e8e6b3b86309e5c7395c2ab4d0366a184889c6536d758703715140c44","observation_id":"ce0d3944-28f7-429a-870c-d44cf044ca7f","resolution":{"observed_at":"2026-08-09T14:48:16.903736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:15.798975Z","title":"Introducing claude 3.5 sonnet, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.798975Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:fb0a2963056e0156f997e1570ba88f203eee973dbe2547d7a34f9a4ae3f9a1a5","observation_id":"faddb737-686e-4dbb-881c-f0f30de49277","resolution":{"observed_at":"2026-08-09T14:48:15.798975Z","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":"2026-08-09T14:48:16.869384Z","title":"Announcing state-of-the-art flux.1 dev and schnell models, 2024","venue":null,"work_id":"17aac1df-7356-4945-af56-24a59ca3102f","year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.803776Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:69bf46d2b5538c4dbc8742745623f92f78d325565fe1b5b478c1a41d3de06fcf","observation_id":"40ee7794-abba-4a37-8f3e-e92b3b5d606e","resolution":{"observed_at":"2026-08-09T14:48:16.875029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.852661Z","title":"Ledits++: Limitless im- age editing using text-to-image models","venue":null,"work_id":"e7335c03-43ea-428d-b943-2ecbf8d235b6","year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.808153Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:1916c2a586aac5d133c0c24becce82143d3ff9eede79bbc643f07a008ca9e3f5","observation_id":"b5f413a7-415e-4990-b629-daab3d2d53dd","resolution":{"observed_at":"2026-08-09T14:48:16.858109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.836331Z","title":"Large scale gan training for high fidelity natural image synthesis","venue":null,"work_id":"0393ee1b-bd9c-4730-a932-6eeaafd739f9","year":2018},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.812501Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:a2bac0486c9fdcb03ac41499ffd5698edbcefd2761fd7ac601fa96b1643beec5","observation_id":"e42d9cf0-1fd9-4de4-9398-ed52f94a2d08","resolution":{"observed_at":"2026-08-09T14:48:16.841317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.820515Z","title":"Instructpix2pix: Learning to follow image edit- ing instructions","venue":null,"work_id":"38b5e024-d5b6-4853-8187-c634cd8d1bde","year":2023},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.817504Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:14526b06781e6f3d8e27c37f44d1e5675148f43fb331c9986b34d53405698b0d","observation_id":"e308a0d0-ad3f-4524-ac27-14ea7835c338","resolution":{"observed_at":"2026-08-09T14:48:16.825772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.02395","last_updated":"2024-11-04T18:59:05Z","snapshot_observed_at":"2026-07-06T19:45:00.034364Z","submitted_at":"2024-11-04T18:59:05Z","title":"Training-free Regional Prompting for Diffusion Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.02395","snapshot_observed_at":"2026-08-09T14:48:15.822318Z","title":"Training-free regional prompt- ing for diffusion transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.822318Z"},"links":{"cited_paper":"/paper/2411.02395","citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:98a9b321300b9a654e2eb7a70c2975774ea18176c83235bda25bdbe0722d0ccc","observation_id":"4d2e7c94-3433-4a22-8d8e-d1816c15aaaf","resolution":{"observed_at":"2026-08-09T14:48:15.822318Z","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":"2026-08-09T14:48:16.804139Z","title":"Noiseclr: A con- trastive learning approach for unsupervised discovery of interpretable directions in diffusion models","venue":null,"work_id":"b19fc4fe-64ac-4cef-8317-8eadf2103489","year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.827525Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:3b4ef17fcbaa2ee75049396a2a441c9c1a99757e5b592b221465204d4bfcdb65","observation_id":"fa9a6217-feca-4b13-bfc7-05e140de3b3e","resolution":{"observed_at":"2026-08-09T14:48:16.810117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:15.832113Z","title":"Turboedit: Text-based image editing using few-step diffusion models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.832113Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:d4282ab5a0a964d8849f9cee32ba273267b38995c5c83b0e150b419a9028c224","observation_id":"84f8f5de-2749-405b-8cb7-5a9c55ef692d","resolution":{"observed_at":"2026-08-09T14:48:15.832113Z","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":"2026-08-09T14:48:16.778342Z","title":"Diffusion models beat gans on image synthesis","venue":null,"work_id":"fe8d2023-5dde-45d3-ab83-3dd16336fb01","year":2021},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.836693Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:6dbe6dacc3c95ca7e284b2a35318903f83f3fa7d1021fe95bd6a6b4eb3908921","observation_id":"519271ea-0d51-419e-ac59-df46444d78ee","resolution":{"observed_at":"2026-08-09T14:48:16.783346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.09413","last_updated":"2024-11-22T05:12:30Z","snapshot_observed_at":"2026-08-10T23:02:25.296575Z","submitted_at":"2024-06-13T17:59:56Z","title":"Interpreting the Weight Space of Customized Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.09413","snapshot_observed_at":"2026-08-09T14:48:15.841402Z","title":"Interpreting the weight space of customized diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.841402Z"},"links":{"cited_paper":"/paper/2406.09413","citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:f42d2281cf0c21b7c1b62c7d3390f533739f08b98dbda61033cc7246d3439be1","observation_id":"9941adf8-5f26-4116-8425-87f359dd85d1","resolution":{"observed_at":"2026-08-09T14:48:15.841402Z","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":"2026-08-09T14:48:16.762743Z","title":"Scaling rectified flow transformers for high-resolution image synthesis","venue":null,"work_id":"f17466ec-d5bc-484b-9048-bf299b2e051d","year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.846473Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:0a2ad042cde90838b64e5b1d080be4bd438426e357d6d1a5286bea9146a9bbdd","observation_id":"21c09cfa-581a-40ba-8b4d-5fe02a910e02","resolution":{"observed_at":"2026-08-09T14:48:16.767447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.747258Z","title":"Dreamsim: Learning new dimensions of hu- man visual similarity using synthetic data","venue":null,"work_id":"2e8b1e8f-a8ed-4474-84ca-f47e9ea36d9a","year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.851113Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:5358980f67575e5c278ca9176b15a08cfb6601d15ec02cf43b7a1484c4bd46a4","observation_id":"4d14e808-d7e2-4243-99b3-1bed27729e57","resolution":{"observed_at":"2026-08-09T14:48:16.752425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.731509Z","title":"Stylegan-nada: Clip-guided domain adaptation of im- age generators","venue":null,"work_id":"16dd4db7-fd85-48fa-b27c-7d4403ff42a8","year":2022},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.855433Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:894b4f7b65c2b5074fd792cb83e8be8f0b6e5bedd1ec522eb966fa262991edf2","observation_id":"0ef45419-85ae-497b-bd79-a56ee675a532","resolution":{"observed_at":"2026-08-09T14:48:16.736597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.715120Z","title":"Concept sliders: Lora adaptors for precise control in diffusion models","venue":null,"work_id":"ec6cfe17-b6ea-4238-ab20-13c7563edc0d","year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.860038Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:e5f2bbe7acfcd69c032a3ee042d17f13d8a0fb4ee4a1de198fb81e517f4caae0","observation_id":"154b2911-b21f-44db-9e0c-9f9dedad6105","resolution":{"observed_at":"2026-08-09T14:48:16.720448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.698917Z","title":"Generative adversar- ial nets","venue":null,"work_id":"cc43b834-9e09-412d-a906-112b97d80f73","year":2014},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.864956Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:deba3d1e44307b94a0aef85f068ee01951a847d5cb48187a0558a62a36a5541c","observation_id":"b96fa78a-d3d6-4344-a493-619dd3645d43","resolution":{"observed_at":"2026-08-09T14:48:16.704023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.683290Z","title":"Towards a framework for human-ai interaction patterns in co-creative gan appli- cations","venue":null,"work_id":"3f14e722-39f6-4575-b36f-4377d7405103","year":2022},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.869561Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:765106b1b364fefd36200f16801ee21c36208fcb66ff0f7117b48043b9a8db23","observation_id":"ac88cbfe-92c3-44e6-bbb7-2bee67972e7d","resolution":{"observed_at":"2026-08-09T14:48:16.688348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.667077Z","title":"Ganspace: Discovering inter- pretable gan controls","venue":null,"work_id":"e0d1d7e0-c4d0-44e4-9111-c6af38507eaa","year":2020},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.874068Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:ced2979b2e1b4a3d176eebfee9d3e7a09f68e1d49c9cf30b04624313b592a59c","observation_id":"55dd9fcf-0376-4d3e-8f40-9c88ae945dbc","resolution":{"observed_at":"2026-08-09T14:48:16.673116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2208.01626","last_updated":"2022-08-02T17:55:41Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-08-02T17:55:41Z","title":"Prompt-to-Prompt Image Editing with Cross Attention Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.01626","snapshot_observed_at":"2026-08-09T14:48:15.878559Z","title":"Prompt- to-prompt image editing with cross attention control","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.878559Z"},"links":{"cited_paper":"/paper/2208.01626","citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:fc9a2cb5109434e13a693d8dfa7077332b38401bba18e8fe95bfa3d308b1a3da","observation_id":"1ff04984-05e8-4eee-b9e1-845b2aacddff","resolution":{"observed_at":"2026-08-09T14:48:15.878559Z","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":"2026-08-09T14:48:16.649110Z","title":"Style aligned image generation via 9 shared attention","venue":null,"work_id":"70cf5aa8-7c0d-4a7f-a544-7ae74393a6d2","year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.883326Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:60ccad4ae6cac42a0a412a9f8cb70f81811f26edbc6feed8b88ea8bd474b6c93","observation_id":"b5c77873-12cb-4be8-bb60-24c8c6914b0c","resolution":{"observed_at":"2026-08-09T14:48:16.654295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08718","last_updated":"2022-03-23T19:47:21Z","snapshot_observed_at":"2026-07-06T11:01:02.207193Z","submitted_at":"2021-04-18T05:00:29Z","title":"CLIPScore: A Reference-free Evaluation Metric for Image Captioning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08718","snapshot_observed_at":"2026-08-09T14:48:15.887654Z","title":"Clipscore: A reference- free evaluation metric for image captioning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.887654Z"},"links":{"cited_paper":"/paper/2104.08718","citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:68d10bd28458f541dcc1a80e718916928642a96d70ac07299767098e29117545","observation_id":"2b59a164-cdd1-4f7a-b359-2d427d5a683a","resolution":{"observed_at":"2026-08-09T14:48:15.887654Z","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":"2026-08-09T14:48:16.632967Z","title":"On modeling human-computer co- creativity","venue":null,"work_id":"e327ac5e-a2fe-40a6-b7ca-5565998d653a","year":2014},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.892519Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:cae5132f8415e42151b8fcb834075694c40bfeabc5166a640f3e024c84d6303e","observation_id":"f4e3085f-88fa-40d1-9bfb-416ccefa0767","resolution":{"observed_at":"2026-08-09T14:48:16.638273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-11T08:20:29.798517Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-09T14:48:15.897154Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.897154Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:b438cda5f6c715ff6a0dd5614652b5f4495612f31af40e659a8a72396b097ea4","observation_id":"a798ba2c-23e8-4302-8ae8-c0250b8aad31","resolution":{"observed_at":"2026-08-09T14:48:15.897154Z","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":"2026-08-09T14:48:16.616416Z","title":"Image synthesis style studies, 2022","venue":null,"work_id":"727465b5-320d-42aa-a063-ca9bdad20b08","year":2022},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.902691Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:08ec8dde43e07ed37deeb973b065470e0c929a358e9c9aae1fbede92e07a9d38","observation_id":"e52cf6bc-fee3-4cd2-bb7c-f588c499998f","resolution":{"observed_at":"2026-08-09T14:48:16.621975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.599868Z","title":"Improving image generation with better captions","venue":null,"work_id":"75787a1e-5770-4416-9dc5-5ef1e7c147a5","year":2023},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.907650Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:58a609d16eafd8b3da959919f3f41fe6ab67b8ade515a4acaaa71350ff09d872","observation_id":"0a971e42-e83c-40a7-b17f-a2dc92d48675","resolution":{"observed_at":"2026-08-09T14:48:16.604877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.582377Z","title":"Scaling up gans for text-to-image synthesis","venue":null,"work_id":"e36cd06a-ecc1-48e1-b500-881460080c7b","year":2023},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.912263Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:713d1142ad9042bf33ae6f76a073f8b052f8b733d2122dba836dcd708965005d","observation_id":"d5091c7f-0bf2-4aa4-b48e-0cdbc8c1236d","resolution":{"observed_at":"2026-08-09T14:48:16.588006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.563061Z","title":"A style- based generator architecture for generative adversar- ial networks","venue":null,"work_id":"1a77768e-8d66-4d5b-a7c6-813f3cdcaf9b","year":2019},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.917945Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:bfa14c699615633bac93c37abb404d1e54c6bb38c1e3284c5768f4458a9b99c9","observation_id":"626bfd10-cf2c-4efe-96d3-f16c888345bd","resolution":{"observed_at":"2026-08-09T14:48:16.568052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.546398Z","title":"Multi-concept cus- tomization of text-to-image diffusion","venue":null,"work_id":"15ed9b06-798f-40ae-9809-06c355be7113","year":1931},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.922497Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:08fcd5ecdadbfa03261e69ec63adbc171f14580ab06251efca1f513e83245453","observation_id":"c2c81de7-a78a-413d-9f6d-00bc5ddb81a8","resolution":{"observed_at":"2026-08-09T14:48:16.552138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.531070Z","title":"Photomaker: Cus- tomizing realistic human photos via stacked id embed- ding","venue":null,"work_id":"537b72a5-7d9c-43b7-b760-a35c5d69d388","year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.927105Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:84b0ce003471cc063508f85ac6c5ae5914e0939cca7f85ab6cb9a4a09af708a7","observation_id":"2228cad8-e253-4eb3-82c7-b68a7dfa809e","resolution":{"observed_at":"2026-08-09T14:48:16.536136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.513890Z","title":"Unsupervised compo- sitional concepts discovery with text-to-image genera- tive models","venue":null,"work_id":"ba9f0753-2b75-47fb-92ca-4a75cd449eeb","year":2023},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.931531Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:df85750bd311ec5fa98f1fab55c3c52f3378f331a3599a11ee6aed3e222b3ca0","observation_id":"3264bb9b-bc96-425b-b18b-8e1d7ce13f78","resolution":{"observed_at":"2026-08-09T14:48:16.519569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.488703Z","title":"Zero- shot image-to-image translation","venue":null,"work_id":"b3575308-7352-4eb3-b395-c13ad6a4d91e","year":2023},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.936119Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:966a25167314b03bcb5077891b2d6bd8675a4c744642ec48deac7b1df77ef351","observation_id":"53b82a3b-55fb-409b-b37f-003c02cb988b","resolution":{"observed_at":"2026-08-09T14:48:16.494145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:15.940906Z","title":"Styleclip: Text-driven manipulation of stylegan imagery","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.940906Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:d29c38a978bfcc3a8545fd4224635a418fb2a2da66b559d6ee4e022ac955df87","observation_id":"e77d3eb6-a41a-4ba4-87d2-57a65a9862a0","resolution":{"observed_at":"2026-08-09T14:48:15.940906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01952","last_updated":"2023-07-04T23:04:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-04T23:04:57Z","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01952","snapshot_observed_at":"2026-08-09T14:48:15.945388Z","title":"Sdxl: Improving latent diffusion models for high-resolution image synthesis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.945388Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:aa97aa7cc980a95589b932691bbf86c7f2788b8c5515ee4880267ac352660dab","observation_id":"746712f6-857d-4b3d-a8bb-8f218a301290","resolution":{"observed_at":"2026-08-09T14:48:15.945388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06434","last_updated":"2016-01-07T23:09:39Z","snapshot_observed_at":"2026-08-04T04:34:27.861448Z","submitted_at":"2015-11-19T22:50:32Z","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06434","snapshot_observed_at":"2026-08-09T14:48:15.949618Z","title":"Unsupervised representation learning with deep convolutional generative adversarial net- works","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.949618Z"},"links":{"cited_paper":"/paper/1511.06434","citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:30dfedf1fd3ca98fd661a9af7bc69f6bd798f62359b466520d8f3753a8356f63","observation_id":"9871517b-9483-4acb-a460-26f41a5a865c","resolution":{"observed_at":"2026-08-09T14:48:15.949618Z","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":"2026-08-09T14:48:16.447735Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":"caddfb17-0bc8-4551-b544-b7ede0bf3d91","year":2021},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.954464Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:0e7218cc2ff44f800e1bb54d520ba35e0f7b4f58c969ff29fe5281d2c3f5c6a6","observation_id":"5cd292a6-18e9-44dc-b5b0-f91d645f9413","resolution":{"observed_at":"2026-08-09T14:48:16.453519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.430733Z","title":"Stable diffusion 2.0 release, 2022","venue":null,"work_id":"03c3c192-1f4c-43a7-9c5d-58bea8755d27","year":2022},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.958533Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:187e77c6cb77f326b1a7a9270f484795e8846d1bb2100d236f2fd1ed393d67a7","observation_id":"d08f788c-62f8-4bdb-9dc3-6b9c69162fe9","resolution":{"observed_at":"2026-08-09T14:48:16.436105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.414249Z","title":"Stable diffusion v1-4 model card, 2022","venue":null,"work_id":"e4657820-176f-4b59-bf5f-ed1db5d5f910","year":2022},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.962595Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:a4ba91e458a373280a038679b871c57d2c6c7fd4449e4d26baf5361307a7cc44","observation_id":"07351f98-5961-4881-9c79-7928a2e84e5c","resolution":{"observed_at":"2026-08-09T14:48:16.418920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.398218Z","title":"Stable diffusion v2 model card, 2022","venue":null,"work_id":"c48168d7-e106-4928-82d6-f53eee3dee4d","year":2022},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.966653Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:57c2bfccfb66809d68a4b5cf18859c54efc01468c54d7d571e8e8afb8e7ba845","observation_id":"e739000f-0168-4108-9014-5f670fd5e598","resolution":{"observed_at":"2026-08-09T14:48:16.403426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.381566Z","title":"High- resolution image synthesis with latent diffusion mod- els","venue":null,"work_id":"5ff34831-a97a-425e-bede-6e4ec353bbc9","year":2022},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.970810Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:a5ea1cabf1ae7609c36f6ca3db36ae207d9fc7b14f19156443a564782a1bf17f","observation_id":"8e63244c-929a-496c-9fd4-62b0ee7df00e","resolution":{"observed_at":"2026-08-09T14:48:16.387167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:15.974897Z","title":"Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.974897Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:cf705e0ec2cb20517a00c495d51ed3d05ca597b2b7153c47828fae091858abbb","observation_id":"3b34bd37-1cc8-49ca-868a-5cb96d977ffa","resolution":{"observed_at":"2026-08-09T14:48:15.974897Z","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":"2026-08-09T14:48:16.353270Z","title":"Cloneofsimo/lora: Using low-rank adapta- tion to quickly fine-tune diffusion models.s","venue":null,"work_id":"f60ad439-4f14-4860-88e5-f4f071bd324e","year":2023},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.979474Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:c71267186de4f1ec106053d36548b9badf78e4bc84f38ab2b11b5dd5468f18e2","observation_id":"540f27ec-8df2-4740-a13b-543e733e1a62","resolution":{"observed_at":"2026-08-09T14:48:16.358516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.338610Z","title":"Adversarial diffusion distillation","venue":null,"work_id":"bd3de0bd-8e70-4ba9-9c36-9ca9f3bdb59a","year":null},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.984171Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:eae69e2e0b06c1c23c0d81e7fdb3f130d6e4bd6e5949b71edae810197c5b858b","observation_id":"43b0e881-10d0-4c23-b225-229b6baf9655","resolution":{"observed_at":"2026-08-09T14:48:16.343225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.322657Z","title":"Facenet: A unified embedding for face recog- nition and clustering","venue":null,"work_id":"0d169d9a-1dfb-4da0-8151-66c4e0d3a6ec","year":2015},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.989165Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:8834cfc340d8fcd9b7cfba80fc3a441d23ba08da412329bcce6dcd370a4ecc92","observation_id":"4398faa2-49f4-40ff-933e-0841f8e1bf06","resolution":{"observed_at":"2026-08-09T14:48:16.327188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.306612Z","title":"Interfacegan: Interpreting the disentangled face representation learned by gans","venue":null,"work_id":"f9600f98-3ace-4686-af50-ac242ebe73c4","year":2004},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.993998Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:721c71b7c21b8127cc7638bdcf309c81f101bb5e97c3bacaf33f0433f6a9040d","observation_id":"6e4ec73e-fd28-49e5-a13d-ab614cd1ab32","resolution":{"observed_at":"2026-08-09T14:48:16.312176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.288290Z","title":"Instantbooth: Personalized text-to-image generation without test-time finetuning","venue":null,"work_id":"88c27989-6187-4704-9462-e3bac44d5a62","year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:15.998525Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:8f93112c0feba3f53aa0ec8dbd11b5c234f090a23bfab0ad0e0f04bd7389e71a","observation_id":"46c05361-41f2-4657-94de-df49d76cb495","resolution":{"observed_at":"2026-08-09T14:48:16.293593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.271480Z","title":"Stylespace analysis: Disentangled controls for style- gan image generation","venue":null,"work_id":"627513fe-7f98-4cb8-8230-1e363f55bdad","year":2021},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:16.003205Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:8cbc5e52161fea9d0f8aa91ea3844e55520d69337d4bf7a45a20700e2e8ef5f9","observation_id":"ccef0954-00db-4c42-aff7-7ffc31fd38b3","resolution":{"observed_at":"2026-08-09T14:48:16.277108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.254412Z","title":"Turboedit: Instant text-based image editing","venue":null,"work_id":"bfaf9279-8423-426d-bbd4-78be333e5c76","year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:16.013668Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:4319fa60d12295d5c8cfd665017f991d96cef3bfef5fa3e7f2497983b3c91438","observation_id":"5a4280b8-100d-4c58-a87c-f7e33d75105c","resolution":{"observed_at":"2026-08-09T14:48:16.260177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-09T14:48:16.236203Z","title":"Inversion-free image editing with natu- ral language","venue":null,"work_id":"4f89bdfb-7207-4893-b1ff-4beba2382870","year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:16.018550Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:7a85887e606215ce8f13e8e1ecc97babeb303ac089cd46e7c847ad84b1ac9671","observation_id":"9bef35cc-240a-40b9-a0b1-9d9fc5784848","resolution":{"observed_at":"2026-08-09T14:48:16.241656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.06721","last_updated":"2023-08-13T08:34:51Z","snapshot_observed_at":"2026-07-06T16:05:39.158819Z","submitted_at":"2023-08-13T08:34:51Z","title":"IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.06721","snapshot_observed_at":"2026-08-09T14:48:16.023237Z","title":"Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:16.023237Z"},"links":{"cited_paper":"/paper/2308.06721","citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:6bb5b4a25cc54f76ce9b2d1997350f5b8aae7f2e519b6ea4b3e07bd32aa7a384","observation_id":"580fe796-5fd3-4700-8ffb-00eed1295f1d","resolution":{"observed_at":"2026-08-09T14:48:16.023237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14867","last_updated":"2024-05-24T17:08:32Z","snapshot_observed_at":"2026-08-05T03:51:41.162350Z","submitted_at":"2024-05-23T17:59:49Z","title":"Improved Distribution Matching Distillation for Fast Image Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14867","snapshot_observed_at":"2026-08-09T14:48:16.028108Z","title":"Improved distribution matching distil- lation for fast image synthesis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:16.028108Z"},"links":{"cited_paper":"/paper/2405.14867","citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:81be29edb4201f478b43609b4c63c56c48856874832c43d2819de1dabe479673","observation_id":"7cfafa4e-f20b-4273-ae69-8b5e7559c176","resolution":{"observed_at":"2026-08-09T14:48:16.028108Z","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":"2026-08-09T14:48:16.216933Z","title":"person” show higher varia- tion in CLIP space compared to rarer concepts like “waterfalls","venue":null,"work_id":"37e406b1-b6e7-4e31-839c-9fc78c1cf07f","year":2023},"citing_paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-09T14:48:16.035237Z"},"links":{"citing_paper":"/paper/2502.01639"},"observation_digest":"sha256:0ce933673790d720a3fe1cfe9a47bac9931eb5486a16c73faa85f6d49588511b","observation_id":"e9552ccb-f88f-45bc-8eea-6bf7c5b0b787","resolution":{"observed_at":"2026-08-09T14:48:16.223970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.01639","last_updated":"2025-02-03T18:59:55Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T14:40:13.022503Z","submitted_at":"2025-02-03T18:59:55Z","title":"SliderSpace: Decomposing the Visual Capabilities of Diffusion Models"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":39},"total_outbound_references":52},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 6 inbound Pith citation observations for arXiv:2502.01639."}