{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7P5UW5OBPTV4JZ5XWW7I4UWEYU","short_pith_number":"pith:7P5UW5OB","canonical_record":{"source":{"id":"2409.06633","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-10T16:44:47Z","cross_cats_sorted":[],"title_canon_sha256":"af56b0d4624550152682aea82d6916e1a6f4924c855b72e38c8e73b4b9dd86f5","abstract_canon_sha256":"46aefb8a38b0c0bae956470e624ede532f5e9a5f7f6aeb9de6fcb03214203445"},"schema_version":"1.0"},"canonical_sha256":"fbfb4b75c17cebc4e7b7b5be8e52c4c504f66cc10a1abcbb5f355da72c9b544f","source":{"kind":"arxiv","id":"2409.06633","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.06633","created_at":"2026-07-05T10:43:25Z"},{"alias_kind":"arxiv_version","alias_value":"2409.06633v2","created_at":"2026-07-05T10:43:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.06633","created_at":"2026-07-05T10:43:25Z"},{"alias_kind":"pith_short_12","alias_value":"7P5UW5OBPTV4","created_at":"2026-07-05T10:43:25Z"},{"alias_kind":"pith_short_16","alias_value":"7P5UW5OBPTV4JZ5X","created_at":"2026-07-05T10:43:25Z"},{"alias_kind":"pith_short_8","alias_value":"7P5UW5OB","created_at":"2026-07-05T10:43:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7P5UW5OBPTV4JZ5XWW7I4UWEYU","target":"record","payload":{"canonical_record":{"source":{"id":"2409.06633","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-10T16:44:47Z","cross_cats_sorted":[],"title_canon_sha256":"af56b0d4624550152682aea82d6916e1a6f4924c855b72e38c8e73b4b9dd86f5","abstract_canon_sha256":"46aefb8a38b0c0bae956470e624ede532f5e9a5f7f6aeb9de6fcb03214203445"},"schema_version":"1.0"},"canonical_sha256":"fbfb4b75c17cebc4e7b7b5be8e52c4c504f66cc10a1abcbb5f355da72c9b544f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:43:25.375785Z","signature_b64":"t8456HmMr4Nj1jVUNCNQTfmP4eOTn9w0+gWiTHQUPj03x2moomVcFLEe/cTqXnmn5t6/YovoLZczyi/H/0ShBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbfb4b75c17cebc4e7b7b5be8e52c4c504f66cc10a1abcbb5f355da72c9b544f","last_reissued_at":"2026-07-05T10:43:25.375224Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:43:25.375224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.06633","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:43:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+3r9GncTI4ZrRCvvgoJC1yVndREE3d2IXXUwh+3fbfa6WkX6qj8DskbAOx/2zXf1L9UBJoRaxeZynJhNp+YNAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T15:14:53.903210Z"},"content_sha256":"79b450e66e4fb747bee7bc7dcba69d905ca2564d3a8041cb8df61aa02d43a824","schema_version":"1.0","event_id":"sha256:79b450e66e4fb747bee7bc7dcba69d905ca2564d3a8041cb8df61aa02d43a824"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7P5UW5OBPTV4JZ5XWW7I4UWEYU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongrui Huang, Jiangning Zhang, Lizhuang Ma, Ran Yi, Teng Hu, Yabiao Wang","submitted_at":"2024-09-10T16:44:47Z","abstract_excerpt":"In recent years, the development of diffusion models has led to significant progress in image and video generation tasks, with pre-trained models like the Stable Diffusion series playing a crucial role. Inspired by model pruning which lightens large pre-trained models by removing unimportant parameters, we propose a novel model fine-tuning method to make full use of these ineffective parameters and enable the pre-trained model with new task-specified capabilities. In this work, we first investigate the importance of parameters in pre-trained diffusion models, and discover that the smallest 10%"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.06633","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.06633/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:43:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nvc9FW/aYjYITDUsDDc0VTJwV65hrHmAQ7d2QWwTLDvZy2hWZsygGESfgs/ItanRNFKJbcODpyYElG88jWbwAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T15:14:53.903590Z"},"content_sha256":"e90bf04c9b70eb1908d0e4ea6be36ec59614e3b37faa29f5d06eefc57a4b9b56","schema_version":"1.0","event_id":"sha256:e90bf04c9b70eb1908d0e4ea6be36ec59614e3b37faa29f5d06eefc57a4b9b56"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7P5UW5OBPTV4JZ5XWW7I4UWEYU/bundle.json","state_url":"https://pith.science/pith/7P5UW5OBPTV4JZ5XWW7I4UWEYU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7P5UW5OBPTV4JZ5XWW7I4UWEYU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-17T15:14:53Z","links":{"resolver":"https://pith.science/pith/7P5UW5OBPTV4JZ5XWW7I4UWEYU","bundle":"https://pith.science/pith/7P5UW5OBPTV4JZ5XWW7I4UWEYU/bundle.json","state":"https://pith.science/pith/7P5UW5OBPTV4JZ5XWW7I4UWEYU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7P5UW5OBPTV4JZ5XWW7I4UWEYU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7P5UW5OBPTV4JZ5XWW7I4UWEYU","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"46aefb8a38b0c0bae956470e624ede532f5e9a5f7f6aeb9de6fcb03214203445","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-10T16:44:47Z","title_canon_sha256":"af56b0d4624550152682aea82d6916e1a6f4924c855b72e38c8e73b4b9dd86f5"},"schema_version":"1.0","source":{"id":"2409.06633","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.06633","created_at":"2026-07-05T10:43:25Z"},{"alias_kind":"arxiv_version","alias_value":"2409.06633v2","created_at":"2026-07-05T10:43:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.06633","created_at":"2026-07-05T10:43:25Z"},{"alias_kind":"pith_short_12","alias_value":"7P5UW5OBPTV4","created_at":"2026-07-05T10:43:25Z"},{"alias_kind":"pith_short_16","alias_value":"7P5UW5OBPTV4JZ5X","created_at":"2026-07-05T10:43:25Z"},{"alias_kind":"pith_short_8","alias_value":"7P5UW5OB","created_at":"2026-07-05T10:43:25Z"}],"graph_snapshots":[{"event_id":"sha256:e90bf04c9b70eb1908d0e4ea6be36ec59614e3b37faa29f5d06eefc57a4b9b56","target":"graph","created_at":"2026-07-05T10:43:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2409.06633/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, the development of diffusion models has led to significant progress in image and video generation tasks, with pre-trained models like the Stable Diffusion series playing a crucial role. Inspired by model pruning which lightens large pre-trained models by removing unimportant parameters, we propose a novel model fine-tuning method to make full use of these ineffective parameters and enable the pre-trained model with new task-specified capabilities. In this work, we first investigate the importance of parameters in pre-trained diffusion models, and discover that the smallest 10%","authors_text":"Hongrui Huang, Jiangning Zhang, Lizhuang Ma, Ran Yi, Teng Hu, Yabiao Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-10T16:44:47Z","title":"SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.06633","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:79b450e66e4fb747bee7bc7dcba69d905ca2564d3a8041cb8df61aa02d43a824","target":"record","created_at":"2026-07-05T10:43:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"46aefb8a38b0c0bae956470e624ede532f5e9a5f7f6aeb9de6fcb03214203445","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-10T16:44:47Z","title_canon_sha256":"af56b0d4624550152682aea82d6916e1a6f4924c855b72e38c8e73b4b9dd86f5"},"schema_version":"1.0","source":{"id":"2409.06633","kind":"arxiv","version":2}},"canonical_sha256":"fbfb4b75c17cebc4e7b7b5be8e52c4c504f66cc10a1abcbb5f355da72c9b544f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fbfb4b75c17cebc4e7b7b5be8e52c4c504f66cc10a1abcbb5f355da72c9b544f","first_computed_at":"2026-07-05T10:43:25.375224Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:43:25.375224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"t8456HmMr4Nj1jVUNCNQTfmP4eOTn9w0+gWiTHQUPj03x2moomVcFLEe/cTqXnmn5t6/YovoLZczyi/H/0ShBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:43:25.375785Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.06633","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:79b450e66e4fb747bee7bc7dcba69d905ca2564d3a8041cb8df61aa02d43a824","sha256:e90bf04c9b70eb1908d0e4ea6be36ec59614e3b37faa29f5d06eefc57a4b9b56"],"state_sha256":"8105958a96e5e73700315683e821403e6e4e8ab482f1b5e4d220a86656580f73"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZJaED6ExBWS4a9VRmSvM1ET7Ptfe6tiU37PlORD0BaGi0rO32d+/cUXeZh2eYqTnvi6c07lZ7LDsd0vYU7mZCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T15:14:53.907139Z","bundle_sha256":"645218f0a1f3ffb7578a08350eff4cc325525e2139640933c7c7e396ebf7d60a"}}