{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:DR2XCJIZ22G45GAGBRGIXS7CFR","short_pith_number":"pith:DR2XCJIZ","canonical_record":{"source":{"id":"2408.08751","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-16T13:50:50Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"35e988e84137e39b2ec3f8c07bafd20756d92ddf999e7765587a942f9fda63ec","abstract_canon_sha256":"5ff054842ccb9bc979ecfadb657cc9b39e698749daa14b0b58ab83acd1e020ea"},"schema_version":"1.0"},"canonical_sha256":"1c75712519d68dce98060c4c8bcbe22c774904e50dc81e0199609604258b1bc2","source":{"kind":"arxiv","id":"2408.08751","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.08751","created_at":"2026-07-05T08:56:08Z"},{"alias_kind":"arxiv_version","alias_value":"2408.08751v1","created_at":"2026-07-05T08:56:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08751","created_at":"2026-07-05T08:56:08Z"},{"alias_kind":"pith_short_12","alias_value":"DR2XCJIZ22G4","created_at":"2026-07-05T08:56:08Z"},{"alias_kind":"pith_short_16","alias_value":"DR2XCJIZ22G45GAG","created_at":"2026-07-05T08:56:08Z"},{"alias_kind":"pith_short_8","alias_value":"DR2XCJIZ","created_at":"2026-07-05T08:56:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:DR2XCJIZ22G45GAGBRGIXS7CFR","target":"record","payload":{"canonical_record":{"source":{"id":"2408.08751","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-16T13:50:50Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"35e988e84137e39b2ec3f8c07bafd20756d92ddf999e7765587a942f9fda63ec","abstract_canon_sha256":"5ff054842ccb9bc979ecfadb657cc9b39e698749daa14b0b58ab83acd1e020ea"},"schema_version":"1.0"},"canonical_sha256":"1c75712519d68dce98060c4c8bcbe22c774904e50dc81e0199609604258b1bc2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:56:08.602638Z","signature_b64":"tZ8t24+xqUc8eC95/j3AyFxZ+Q0enDWAmeb5FukCrzMXpbSK0hpzSopfRLJ67yDyBkMS2RBbmusdUbUjnYb5Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c75712519d68dce98060c4c8bcbe22c774904e50dc81e0199609604258b1bc2","last_reissued_at":"2026-07-05T08:56:08.602225Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:56:08.602225Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.08751","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:56:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cw7m7uq7w5d8r8keNIypdnuCs45nVd+G49tMO0jdKiImbIjUebr4msnHyDBQPRvE7wg14TaI7B6FZS/LQ14CBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T22:54:58.345625Z"},"content_sha256":"a2b704724b3d7bf3f42aabd50cf01e8b59e6050fced3312d6b08be059a64fb91","schema_version":"1.0","event_id":"sha256:a2b704724b3d7bf3f42aabd50cf01e8b59e6050fced3312d6b08be059a64fb91"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:DR2XCJIZ22G45GAGBRGIXS7CFR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Comparative Analysis of Generative Models: Enhancing Image Synthesis with VAEs, GANs, and Stable Diffusion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Sanchayan Vivekananthan","submitted_at":"2024-08-16T13:50:50Z","abstract_excerpt":"This paper examines three major generative modelling frameworks: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Stable Diffusion models. VAEs are effective at learning latent representations but frequently yield blurry results. GANs can generate realistic images but face issues such as mode collapse. Stable Diffusion models, while producing high-quality images with strong semantic coherence, are demanding in terms of computational resources. Additionally, the paper explores how incorporating Grounding DINO and Grounded SAM with Stable Diffusion improves image accu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.08751","kind":"arxiv","version":1},"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/2408.08751/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":"2026-07-05T08:56:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nEaW2IRIePnrmyz9CnFUDNmx12HairZYS3777z2yY6oGmM1iQgpX/aHNskIIjTOj+GyDqio1sTYBLdd3fdkIDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T22:54:58.346141Z"},"content_sha256":"464b838d0877fd17dd0683f8dbedd600e8c80abfe75f9a61febd5c55f61aa6c7","schema_version":"1.0","event_id":"sha256:464b838d0877fd17dd0683f8dbedd600e8c80abfe75f9a61febd5c55f61aa6c7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DR2XCJIZ22G45GAGBRGIXS7CFR/bundle.json","state_url":"https://pith.science/pith/DR2XCJIZ22G45GAGBRGIXS7CFR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DR2XCJIZ22G45GAGBRGIXS7CFR/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":"2026-08-05T22:54:58Z","links":{"resolver":"https://pith.science/pith/DR2XCJIZ22G45GAGBRGIXS7CFR","bundle":"https://pith.science/pith/DR2XCJIZ22G45GAGBRGIXS7CFR/bundle.json","state":"https://pith.science/pith/DR2XCJIZ22G45GAGBRGIXS7CFR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DR2XCJIZ22G45GAGBRGIXS7CFR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DR2XCJIZ22G45GAGBRGIXS7CFR","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":"5ff054842ccb9bc979ecfadb657cc9b39e698749daa14b0b58ab83acd1e020ea","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-16T13:50:50Z","title_canon_sha256":"35e988e84137e39b2ec3f8c07bafd20756d92ddf999e7765587a942f9fda63ec"},"schema_version":"1.0","source":{"id":"2408.08751","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.08751","created_at":"2026-07-05T08:56:08Z"},{"alias_kind":"arxiv_version","alias_value":"2408.08751v1","created_at":"2026-07-05T08:56:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08751","created_at":"2026-07-05T08:56:08Z"},{"alias_kind":"pith_short_12","alias_value":"DR2XCJIZ22G4","created_at":"2026-07-05T08:56:08Z"},{"alias_kind":"pith_short_16","alias_value":"DR2XCJIZ22G45GAG","created_at":"2026-07-05T08:56:08Z"},{"alias_kind":"pith_short_8","alias_value":"DR2XCJIZ","created_at":"2026-07-05T08:56:08Z"}],"graph_snapshots":[{"event_id":"sha256:464b838d0877fd17dd0683f8dbedd600e8c80abfe75f9a61febd5c55f61aa6c7","target":"graph","created_at":"2026-07-05T08:56:08Z","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/2408.08751/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper examines three major generative modelling frameworks: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Stable Diffusion models. VAEs are effective at learning latent representations but frequently yield blurry results. GANs can generate realistic images but face issues such as mode collapse. Stable Diffusion models, while producing high-quality images with strong semantic coherence, are demanding in terms of computational resources. Additionally, the paper explores how incorporating Grounding DINO and Grounded SAM with Stable Diffusion improves image accu","authors_text":"Sanchayan Vivekananthan","cross_cats":["eess.IV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-16T13:50:50Z","title":"Comparative Analysis of Generative Models: Enhancing Image Synthesis with VAEs, GANs, and Stable Diffusion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.08751","kind":"arxiv","version":1},"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:a2b704724b3d7bf3f42aabd50cf01e8b59e6050fced3312d6b08be059a64fb91","target":"record","created_at":"2026-07-05T08:56:08Z","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":"5ff054842ccb9bc979ecfadb657cc9b39e698749daa14b0b58ab83acd1e020ea","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-16T13:50:50Z","title_canon_sha256":"35e988e84137e39b2ec3f8c07bafd20756d92ddf999e7765587a942f9fda63ec"},"schema_version":"1.0","source":{"id":"2408.08751","kind":"arxiv","version":1}},"canonical_sha256":"1c75712519d68dce98060c4c8bcbe22c774904e50dc81e0199609604258b1bc2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c75712519d68dce98060c4c8bcbe22c774904e50dc81e0199609604258b1bc2","first_computed_at":"2026-07-05T08:56:08.602225Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:56:08.602225Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tZ8t24+xqUc8eC95/j3AyFxZ+Q0enDWAmeb5FukCrzMXpbSK0hpzSopfRLJ67yDyBkMS2RBbmusdUbUjnYb5Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:56:08.602638Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.08751","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a2b704724b3d7bf3f42aabd50cf01e8b59e6050fced3312d6b08be059a64fb91","sha256:464b838d0877fd17dd0683f8dbedd600e8c80abfe75f9a61febd5c55f61aa6c7"],"state_sha256":"d89f12268c7fcb83faea7e2c02297fc77f781cc0a9d888477788011101a2f404"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2GamYxkffaHm0/+cgEi38EPsFI6KoKN81JgxnQJRyreGyjBR+EztNgZYSKu38eikakDErqkhyFDIDOiTtdFsCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T22:54:58.350549Z","bundle_sha256":"d9005f8957b3aaf01fc6f2d21e68157ee38f3b1d798b61f8d0f86dc40019f39a"}}