{"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/diffusionwrapper","entry":"DiffusionWrapper","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":5,"n_papers_ran":1,"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":5,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"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":"2401.09047","paper":"/paper/videocrafter2-overcoming-data-limitations-for","title":"VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models","date":"2024-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"videocrafter/videocrafter","path":"lvdm/models/ddpm3d.py","file_url":"https://github.com/videocrafter/videocrafter/blob/HEAD/lvdm/models/ddpm3d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5c9c5717118146d8","mcp_get_code":{"code_sha256":"5c9c5717118146d8"}},{"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":"bb9a12b66a9694f1","mcp_get_code":{"code_sha256":"bb9a12b66a9694f1"}},{"arxiv_id":"2206.02262","paper":"/paper/diffusion-gan-training-gans-with-diffusion","title":"Diffusion-GAN: Training GANs with Diffusion","date":"2022-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhendong-wang/prompt-diffusion","path":"ldm/models/diffusion/ddpm.py","file_url":"https://github.com/zhendong-wang/prompt-diffusion/blob/HEAD/ldm/models/diffusion/ddpm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"46449d701844def0","mcp_get_code":{"code_sha256":"46449d701844def0"}},{"arxiv_id":"2102.05379","paper":"/paper/argmax-flows-and-multinomial-diffusion","title":"Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions","date":"2021-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YuzheZhang-1999/DiffTSR","path":"model/TDM/models/diffusion_multinomial.py","file_url":"https://github.com/YuzheZhang-1999/DiffTSR/blob/HEAD/model/TDM/models/diffusion_multinomial.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":"a15ee5030a6486ce","mcp_get_code":{"code_sha256":"a15ee5030a6486ce"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vainf/diff-pruning","path":"ldm_exp/ldm/models/diffusion/ddpm.py","file_url":"https://github.com/vainf/diff-pruning/blob/HEAD/ldm_exp/ldm/models/diffusion/ddpm.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":"fa3d1058fa2100f3","mcp_get_code":{"code_sha256":"fa3d1058fa2100f3"}}]}