{"work":{"id":"2be93143-ab6f-48d6-96d5-3e85d7246f07","openalex_id":"https://openalex.org/W4405917844","doi":"10.48550/arxiv.2412.06264","arxiv_id":"2412.06264","raw_key":null,"title":"Flow Matching Guide and Code","authors":null,"authors_text":"Yaron Lipman, Marton Havasi, Peter Holderrieth, Neta Shaul, Matt Le, Brian Karrer","year":2024,"venue":"cs.LG","abstract":"Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including image, video, audio, speech, and biological structures. This guide offers a comprehensive and self-contained review of FM, covering its mathematical foundations, design choices, and extensions. By also providing a PyTorch package featuring relevant examples (e.g., image and text generation), this work aims to serve as a resource for both novice and experienced researchers interested in understanding, applying and further developing FM.","external_url":"https://arxiv.org/abs/2412.06264","cited_by_count":10,"metadata_source":"pith","metadata_fetched_at":"2026-08-05T02:28:24.338817+00:00","pith_arxiv_id":"2412.06264","created_at":"2026-05-09T06:10:41.349900+00:00","updated_at":"2026-08-05T02:28:24.338817+00:00","title_quality_ok":false,"display_title":"Flow Matching Guide and Code","render_title":"Flow Matching Guide and Code"},"hub":{"state":{"work_id":"2be93143-ab6f-48d6-96d5-3e85d7246f07","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":98,"external_cited_by_count":10,"distinct_field_count":13,"first_pith_cited_at":"2025-02-02T14:52:50+00:00","last_pith_cited_at":"2026-07-06T16:04:23+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-08-22T17:59:25.613142+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":12},{"context_role":"method","n":7},{"context_role":"baseline","n":1}],"polarity_counts":[{"context_polarity":"background","n":12},{"context_polarity":"use_method","n":7},{"context_polarity":"baseline","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}