{"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":"/code/ddimsampler","entry":"DDIMSampler","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":6,"n_papers_ran":2,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":6,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":4},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2602.16198","paper":"/paper/arxiv-2602-16198","title":"Training-Free Adaptation of Diffusion Models via Doob's h-Transform","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"liamyzq/Doob_training_free_adaptation","path":"search/doob_search.py","file_url":"https://github.com/liamyzq/Doob_training_free_adaptation/blob/HEAD/search/doob_search.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3660048c3ff8d828","mcp_get_code":{"code_sha256":"3660048c3ff8d828"}},{"arxiv_id":"2403.08840","paper":"/paper/noisediffusion-correcting-noise-for-image","title":"NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation","date":"2024-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmlr-group/noisediffusion","path":"controlnet/cldm/ddim_hacked.py","file_url":"https://github.com/tmlr-group/noisediffusion/blob/HEAD/controlnet/cldm/ddim_hacked.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6a0596df2c0e0675","mcp_get_code":{"code_sha256":"6a0596df2c0e0675"}},{"arxiv_id":"2312.03701","paper":"/paper/self-conditioned-image-generation-via","title":"Return of Unconditional Generation: A Self-supervised Representation Generation Method","date":"2023-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LTH14/rcg","path":"pixel_generator/mage/models_mage.py","file_url":"https://github.com/LTH14/rcg/blob/HEAD/pixel_generator/mage/models_mage.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"950818a420758dbe","mcp_get_code":{"code_sha256":"950818a420758dbe"}},{"arxiv_id":"2303.11726","paper":"/paper/3d-human-mesh-estimation-from-virtual-markers-1","title":"3D Human Mesh Estimation from Virtual Markers","date":"2023-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shirleymaxx/vmarker-pro","path":"vmpro/models/diff3dmesh.py","file_url":"https://github.com/shirleymaxx/vmarker-pro/blob/HEAD/vmpro/models/diff3dmesh.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e5c504c0e733b9e0","mcp_get_code":{"code_sha256":"e5c504c0e733b9e0"}},{"arxiv_id":"2301.12503","paper":"/paper/audioldm-text-to-audio-generation-with-latent","title":"AudioLDM: Text-to-Audio Generation with Latent Diffusion Models","date":"2023-01-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoheliu/AudioLDM","path":"audioldm/ldm.py","file_url":"https://github.com/haoheliu/AudioLDM/blob/HEAD/audioldm/ldm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"332deacf272549e4","mcp_get_code":{"code_sha256":"332deacf272549e4"}},{"arxiv_id":"2010.02502","paper":"/paper/denoising-diffusion-implicit-models-1","title":"Denoising Diffusion Implicit Models","date":"2020-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lzhmeng/lca","path":"ldm/models/diffusion/ddim.py","file_url":"https://github.com/lzhmeng/lca/blob/HEAD/ldm/models/diffusion/ddim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2f3f43c470a3f1f5","mcp_get_code":{"code_sha256":"2f3f43c470a3f1f5"}}]}