{"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/topodiffusionnet-a-topology-aware-diffusion","title":"TopoDiffusionNet: A Topology-aware Diffusion Model","arxiv_id":"2410.16646","date":"2024-10-22","proceeding":null,"authors":["Saumya Gupta","Dimitris Samaras","Chao Chen"],"abstract":"Diffusion models excel at creating visually impressive images but often struggle to generate images with a specified topology. The Betti number, which represents the number of structures in an image, is a fundamental measure in topology. Yet, diffusion models fail to satisfy even this basic constraint. This limitation restricts their utility in applications requiring exact control, like robotics and environmental modeling. To address this, we propose TopoDiffusionNet (TDN), a novel approach that enforces diffusion models to maintain the desired topology. We leverage tools from topological data analysis, particularly persistent homology, to extract the topological structures within an image. We then design a topology-based objective function to guide the denoising process, preserving intended structures while suppressing noisy ones. Our experiments across four datasets demonstrate significant improvements in topological accuracy. TDN is the first to integrate topology with diffusion models, opening new avenues of research in this area.","url_abs":"https://arxiv.org/abs/2410.16646v1","url_pdf":"https://arxiv.org/pdf/2410.16646v1.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":"topodiffusionnet-a-topology-aware-diffusion","repo_url":"https://github.com/Saumya-Gupta-26/TopoDiffusionNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"topological-data-analysis","task_name":"Topological Data Analysis"},{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"tdn","method_name":"TDN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2410.16646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.16646"}},"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/Saumya-Gupta-26/TopoDiffusionNet","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_draft_wrong":2,"ran_honours":1,"unverified":4},"by_repo_kind":{"official":{"samples":7,"ran":3,"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":"cfd76fd0d89574a4","entry":"approx_standard_normal_cdf","repo":"Saumya-Gupta-26/TopoDiffusionNet","repo_kind":"official","path":"improved_diffusion/losses.py","file_url":"https://github.com/Saumya-Gupta-26/TopoDiffusionNet/blob/HEAD/improved_diffusion/losses.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cfd76fd0d89574a4"}},{"code_sha256_prefix":"cd33283d615fb3d7","entry":"discretized_gaussian_log_likelihood","repo":"Saumya-Gupta-26/TopoDiffusionNet","repo_kind":"official","path":"improved_diffusion/losses.py","file_url":"https://github.com/Saumya-Gupta-26/TopoDiffusionNet/blob/HEAD/improved_diffusion/losses.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cd33283d615fb3d7"}},{"code_sha256_prefix":"cf2798b666b231ca","entry":"normal_kl","repo":"Saumya-Gupta-26/TopoDiffusionNet","repo_kind":"official","path":"improved_diffusion/losses.py","file_url":"https://github.com/Saumya-Gupta-26/TopoDiffusionNet/blob/HEAD/improved_diffusion/losses.py","link_basis":"harvester_set","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":"cf2798b666b231ca"}},{"code_sha256_prefix":"899dd35be8a7d9c8","entry":"compute_average","repo":"Saumya-Gupta-26/TopoDiffusionNet","repo_kind":"official","path":"analysis-scripts/eval-metrics.py","file_url":"https://github.com/Saumya-Gupta-26/TopoDiffusionNet/blob/HEAD/analysis-scripts/eval-metrics.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"899dd35be8a7d9c8"}},{"code_sha256_prefix":"442558c88bf5c121","entry":"compute_metrics","repo":"Saumya-Gupta-26/TopoDiffusionNet","repo_kind":"official","path":"analysis-scripts/eval-metrics.py","file_url":"https://github.com/Saumya-Gupta-26/TopoDiffusionNet/blob/HEAD/analysis-scripts/eval-metrics.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"442558c88bf5c121"}},{"code_sha256_prefix":"a863803cdd5f3ce6","entry":"make_master_params","repo":"Saumya-Gupta-26/TopoDiffusionNet","repo_kind":"official","path":"improved_diffusion/fp16_util.py","file_url":"https://github.com/Saumya-Gupta-26/TopoDiffusionNet/blob/HEAD/improved_diffusion/fp16_util.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a863803cdd5f3ce6"}},{"code_sha256_prefix":"30e43bcf12d042b0","entry":"unflatten_master_params","repo":"Saumya-Gupta-26/TopoDiffusionNet","repo_kind":"official","path":"improved_diffusion/fp16_util.py","file_url":"https://github.com/Saumya-Gupta-26/TopoDiffusionNet/blob/HEAD/improved_diffusion/fp16_util.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"30e43bcf12d042b0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}