{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/flow-matching-guide-and-code","title":"Flow Matching Guide and Code","arxiv_id":"2412.06264","date":"2024-12-09","proceeding":null,"authors":["Yaron Lipman","Marton Havasi","Peter Holderrieth","Neta Shaul","Matt Le","Brian Karrer","Ricky T. Q. Chen","David Lopez-Paz","Heli Ben-Hamu","Itai Gat"],"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.","url_abs":"https://arxiv.org/abs/2412.06264v1","url_pdf":"https://arxiv.org/pdf/2412.06264v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"flow-matching-guide-and-code","repo_url":"https://github.com/facebookresearch/flow_matching","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"flow-matching-guide-and-code","repo_url":"https://github.com/finnsherry/FlowMatching","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"flow-matching-guide-and-code","repo_url":"https://github.com/g4vrel/CFM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2412.06264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.06264"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/g4vrel/CFM","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/finnsherry/FlowMatching","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/facebookresearch/flow_matching","reach":{"status":"ok","spdx":"NOASSERTION"}}],"summary":{"ran_fixture":1,"ran_honours":1},"by_repo_kind":{"listed":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"f82ca305cc3dc040","entry":"cross_product","repo":"finnsherry/FlowMatching","repo_kind":"listed","path":"lieflow/groups.py","file_url":"https://github.com/finnsherry/FlowMatching/blob/HEAD/lieflow/groups.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f82ca305cc3dc040"}},{"code_sha256_prefix":"32184fba24a9d92a","entry":"ρ_c_normalised","repo":"finnsherry/FlowMatching","repo_kind":"listed","path":"lieflow/models.py","file_url":"https://github.com/finnsherry/FlowMatching/blob/HEAD/lieflow/models.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"32184fba24a9d92a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}