{"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/create-model-and-diffusion","entry":"create_model_and_diffusion","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":18,"n_papers_ran":0,"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":13,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":18,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":13},"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":"2512.03430","paper":"/paper/arxiv-2512-03430","title":"Label-Efficient Hyperspectral Image Classification via Spectral FiLM Modulation of Low-Level Pretrained Diffusion Features","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"openai/guided-diffusion","path":"guided_diffusion/script_util.py","file_url":"https://github.com/openai/guided-diffusion/blob/HEAD/guided_diffusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a3cfc58dafbe392","mcp_get_code":{"code_sha256":"6a3cfc58dafbe392"}},{"arxiv_id":"2511.05844","paper":"/paper/arxiv-2511-05844","title":"Enhancing Diffusion Model Guidance through Calibration and Regularization","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"ajavid34/guided-info-diffusion","path":"guided_diffusion/script_util.py","file_url":"https://github.com/ajavid34/guided-info-diffusion/blob/HEAD/guided_diffusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6a3cfc58dafbe392","mcp_get_code":{"code_sha256":"6a3cfc58dafbe392"}},{"arxiv_id":"2411.08378","paper":"/paper/physics-informed-distillation-for-diffusion","title":"Physics Informed Distillation for Diffusion Models","date":"2024-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pantheon5100/pid_diffusion","path":"cm/script_util.py","file_url":"https://github.com/pantheon5100/pid_diffusion/blob/HEAD/cm/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a320073abc54d8b8","mcp_get_code":{"code_sha256":"a320073abc54d8b8"}},{"arxiv_id":"2410.23905","paper":"/paper/text-difuse-an-interactive-multi-modal-image","title":"Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Leiii-Cao/Text-DiFuse","path":"diffusion_fusion/script_util.py","file_url":"https://github.com/Leiii-Cao/Text-DiFuse/blob/HEAD/diffusion_fusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d9013b673760ac7c","mcp_get_code":{"code_sha256":"d9013b673760ac7c"}},{"arxiv_id":"2402.16991","paper":"/paper/a-phase-transition-in-diffusion-models","title":"A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data","date":"2024-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pcsl-epfl/forward-backward-diffusion","path":"guided_diffusion/script_util.py","file_url":"https://github.com/pcsl-epfl/forward-backward-diffusion/blob/HEAD/guided_diffusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a3cfc58dafbe392","mcp_get_code":{"code_sha256":"6a3cfc58dafbe392"}},{"arxiv_id":"2312.11121","paper":"/paper/multi-scale-reconstruction-of-turbulent","title":"Multi-scale Reconstruction of Turbulent Rotating Flows with Generative Diffusion Models","date":null,"month_inferred_from_arxiv_id":"2023-12","title_source":"archive","repo":"smartturb/repaint-turb","path":"guided_diffusion/script_util.py","file_url":"https://github.com/smartturb/repaint-turb/blob/HEAD/guided_diffusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f8939a171c3aacc9","mcp_get_code":{"code_sha256":"f8939a171c3aacc9"}},{"arxiv_id":"2307.12868","paper":"/paper/understanding-the-latent-space-of-diffusion-1","title":"Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry","date":"2023-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"enkeejunior1/Diffusion-Pullback","path":"src/models/guided_diffusion/script_util.py","file_url":"https://github.com/enkeejunior1/Diffusion-Pullback/blob/HEAD/src/models/guided_diffusion/script_util.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":"6e12917950c39f61","mcp_get_code":{"code_sha256":"6e12917950c39f61"}},{"arxiv_id":"2307.08529","paper":"/paper/synthetic-lagrangian-turbulence-by-generative","title":"Synthetic Lagrangian Turbulence by Generative Diffusion Models","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smartturb/diffusion-lagr","path":"guided_diffusion/script_util.py","file_url":"https://github.com/smartturb/diffusion-lagr/blob/HEAD/guided_diffusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"763edfd42d31215a","mcp_get_code":{"code_sha256":"763edfd42d31215a"}},{"arxiv_id":"2306.03436","paper":"/paper/protecting-the-intellectual-property-of","title":"Intellectual Property Protection of Diffusion Models via the Watermark Diffusion Process","date":"2023-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"senp98/wdm","path":"wdm/script_util.py","file_url":"https://github.com/senp98/wdm/blob/HEAD/wdm/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b89e3afbf5271f3","mcp_get_code":{"code_sha256":"2b89e3afbf5271f3"}},{"arxiv_id":"2306.00501","paper":"/paper/image-generation-with-shortest-path-diffusion","title":"Image generation with shortest path diffusion","date":"2023-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mtkresearch/shortest-path-diffusion","path":"guided_diffusion/script_util.py","file_url":"https://github.com/mtkresearch/shortest-path-diffusion/blob/HEAD/guided_diffusion/script_util.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":"f6a91281eec7c875","mcp_get_code":{"code_sha256":"f6a91281eec7c875"}},{"arxiv_id":"2304.12824","paper":"/paper/contrastive-energy-prediction-for-exact","title":"Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning","date":"2023-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thu-ml/cep-energy-guided-diffusion","path":"images/guided_diffusion/script_util.py","file_url":"https://github.com/thu-ml/cep-energy-guided-diffusion/blob/HEAD/images/guided_diffusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a3cfc58dafbe392","mcp_get_code":{"code_sha256":"6a3cfc58dafbe392"}},{"arxiv_id":"2303.01469","paper":"/paper/consistency-models","title":"Consistency Models","date":"2023-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openai/consistency_models","path":"cm/script_util.py","file_url":"https://github.com/openai/consistency_models/blob/HEAD/cm/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7d8e408ce7ec5b07","mcp_get_code":{"code_sha256":"7d8e408ce7ec5b07"}},{"arxiv_id":"2212.00490","paper":"/paper/zero-shot-image-restoration-using-denoising","title":"Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model","date":"2022-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ipc-lab/deepjscc-diffusion","path":"guided_diffusion/script_util.py","file_url":"https://github.com/ipc-lab/deepjscc-diffusion/blob/HEAD/guided_diffusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd3e49ff96ee5e82","mcp_get_code":{"code_sha256":"dd3e49ff96ee5e82"}},{"arxiv_id":"2206.10550","paper":"/paper/certified-adversarial-robustness-for-free","title":"(Certified!!) Adversarial Robustness for Free!","date":"2022-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ethz-privsec/diffusion_denoised_smoothing","path":"imagenet/guided_diffusion/script_util.py","file_url":"https://github.com/ethz-privsec/diffusion_denoised_smoothing/blob/HEAD/imagenet/guided_diffusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a3cfc58dafbe392","mcp_get_code":{"code_sha256":"6a3cfc58dafbe392"}},{"arxiv_id":"2206.07696","paper":"/paper/diffusion-models-for-video-prediction-and","title":"Diffusion Models for Video Prediction and Infilling","date":"2022-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Tobi-r9/RaMViD","path":"diffusion_openai/script_util.py","file_url":"https://github.com/Tobi-r9/RaMViD/blob/HEAD/diffusion_openai/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"62c3113f19ddedd4","mcp_get_code":{"code_sha256":"62c3113f19ddedd4"}},{"arxiv_id":"2206.00941","paper":"/paper/improving-diffusion-models-for-inverse","title":"Improving Diffusion Models for Inverse Problems using Manifold Constraints","date":"2022-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gzhu06/MCG-ipynb","path":"guided_diffusion/script_util.py","file_url":"https://github.com/gzhu06/MCG-ipynb/blob/HEAD/guided_diffusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"13bf955284791eec","mcp_get_code":{"code_sha256":"13bf955284791eec"}},{"arxiv_id":"2111.14818","paper":"/paper/blended-diffusion-for-text-driven-editing-of","title":"Blended Diffusion for Text-driven Editing of Natural Images","date":"2021-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"omriav/blended-diffusion","path":"guided_diffusion/guided_diffusion/script_util.py","file_url":"https://github.com/omriav/blended-diffusion/blob/HEAD/guided_diffusion/guided_diffusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a3cfc58dafbe392","mcp_get_code":{"code_sha256":"6a3cfc58dafbe392"}},{"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":"dan8991/Image-coding-perceptual-enhancement-with-diffusion-models","path":"improved_diffusion/script_util.py","file_url":"https://github.com/dan8991/Image-coding-perceptual-enhancement-with-diffusion-models/blob/HEAD/improved_diffusion/script_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f4409032385d5a9f","mcp_get_code":{"code_sha256":"f4409032385d5a9f"}}]}