{"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/edm-sampler","entry":"edm_sampler","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":34,"n_papers_ran":7,"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":20,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":35,"n_places_pointer_only":17,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":1,"ran":0,"unverified":17},"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":"2608.05471","paper":"/paper/arxiv-2608-05471","title":"A Foundational EDM2-Based Generative Model for High-Resolution Synthetic Fetal Ultrasound Imaging from Open Datasets","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"xfetus/fetal-ultrasound-edm2","path":"generate_images.py","file_url":"https://github.com/xfetus/fetal-ultrasound-edm2/blob/HEAD/generate_images.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a14db9805bf41197","mcp_get_code":{"code_sha256":"a14db9805bf41197"}},{"arxiv_id":"2605.03802","paper":"/paper/arxiv-2605-03802","title":"Towards accurate extreme event likelihoods from diffusion model climate emulators","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"NVlabs/cBottle","path":"src/cbottle/diffusion_samplers.py","file_url":"https://github.com/NVlabs/cBottle/blob/HEAD/src/cbottle/diffusion_samplers.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":"b8f910a033c16972","mcp_get_code":{"code_sha256":"b8f910a033c16972"}},{"arxiv_id":"2512.20233","paper":"/paper/arxiv-2512-20233","title":"How I Met Your Bias: Investigating Bias Amplification in Diffusion Models","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"NVlabs/edm","path":"generate.py","file_url":"https://github.com/NVlabs/edm/blob/HEAD/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8b16b0674af254db","mcp_get_code":{"code_sha256":"8b16b0674af254db"}},{"arxiv_id":"2510.27497","paper":"/paper/arxiv-2510-27497","title":"InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"HaoruiLi46/InertialAR","path":"InertialAR/diffusion_loss.py","file_url":"https://github.com/HaoruiLi46/InertialAR/blob/HEAD/InertialAR/diffusion_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5410f612607c88fc","mcp_get_code":{"code_sha256":"5410f612607c88fc"}},{"arxiv_id":"2509.16447","paper":"/paper/arxiv-2509-16447","title":"Local Mechanisms of Compositional Generalization in Conditional Diffusion","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"NVlabs/edm2","path":"generate_images.py","file_url":"https://github.com/NVlabs/edm2/blob/HEAD/generate_images.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a14db9805bf41197","mcp_get_code":{"code_sha256":"a14db9805bf41197"}},{"arxiv_id":"2506.09376","paper":"/paper/revisiting-diffusion-models-from-generative","title":"Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation","date":"2025-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Zyriix/GDD","path":"generate.py","file_url":"https://github.com/Zyriix/GDD/blob/HEAD/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"77de35b318b043bc","mcp_get_code":{"code_sha256":"77de35b318b043bc"}},{"arxiv_id":"2502.14583","paper":"/paper/a-theory-for-conditional-generative-modeling","title":"A Theory for Conditional Generative Modeling on Multiple Data Sources","date":"2025-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-gsai/multi-source-gm","path":"real_world_experiments/generate_images.py","file_url":"https://github.com/ml-gsai/multi-source-gm/blob/HEAD/real_world_experiments/generate_images.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a14db9805bf41197","mcp_get_code":{"code_sha256":"a14db9805bf41197"}},{"arxiv_id":"2501.18865","paper":"/paper/reg-rectified-gradient-guidance-for","title":"REG: Rectified Gradient Guidance for Conditional Diffusion Models","date":"2025-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengqigao/REG","path":"EDMv2/generate_images.py","file_url":"https://github.com/zhengqigao/REG/blob/HEAD/EDMv2/generate_images.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"27ca4582511b7d38","mcp_get_code":{"code_sha256":"27ca4582511b7d38"}},{"arxiv_id":"2501.15785","paper":"/paper/memorization-and-regularization-in-generative","title":"Memorization and Regularization in Generative Diffusion Models","date":"2025-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baptistar/DiffusionModelDynamics","path":"RectangleImages/generate.py","file_url":"https://github.com/baptistar/DiffusionModelDynamics/blob/HEAD/RectangleImages/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8b16b0674af254db","mcp_get_code":{"code_sha256":"8b16b0674af254db"}},{"arxiv_id":"2412.07808","paper":"/paper/boosting-alignment-for-post-unlearning-text","title":"Boosting Alignment for Post-Unlearning Text-to-Image Generative Models","date":"2024-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"reds-lab/restricted_gradient_diversity_unlearning","path":"CIFAR/generate.py","file_url":"https://github.com/reds-lab/restricted_gradient_diversity_unlearning/blob/HEAD/CIFAR/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8b16b0674af254db","mcp_get_code":{"code_sha256":"8b16b0674af254db"}},{"arxiv_id":"2410.24060","paper":"/paper/understanding-generalizability-of-diffusion","title":"Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Morefre/Understanding-Generalizability-of-Diffusion-Models-Requires-Rethinking-the-Hidden-Gaussian-Structure","path":"generate.py","file_url":"https://github.com/Morefre/Understanding-Generalizability-of-Diffusion-Models-Requires-Rethinking-the-Hidden-Gaussian-Structure/blob/HEAD/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8b16b0674af254db","mcp_get_code":{"code_sha256":"8b16b0674af254db"}},{"arxiv_id":"2409.02426","paper":"/paper/diffusion-models-learn-low-dimensional","title":"Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering","date":"2024-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"00dcc1be26a2af0f","mcp_get_code":{"code_sha256":"00dcc1be26a2af0f"}},{"arxiv_id":"2406.02507","paper":"/paper/guiding-a-diffusion-model-with-a-bad-version","title":"Guiding a Diffusion Model with a Bad Version of Itself","date":"2024-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nvlabs/edm2","path":"generate_images.py","file_url":"https://github.com/nvlabs/edm2/blob/HEAD/generate_images.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"a14db9805bf41197","mcp_get_code":{"code_sha256":"a14db9805bf41197"}},{"arxiv_id":"2406.02507","paper":"/paper/guiding-a-diffusion-model-with-a-bad-version","title":"Guiding a Diffusion Model with a Bad Version of Itself","date":"2024-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dopplerchase/cira-diff","path":"cira_diff/edm.py","file_url":"https://github.com/dopplerchase/cira-diff/blob/HEAD/cira_diff/edm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"6afde01dd97af6ea","mcp_get_code":{"code_sha256":"6afde01dd97af6ea"}},{"arxiv_id":"2405.18503","paper":"/paper/soundctm-uniting-score-based-and-consistency","title":"SoundCTM: Unifying Score-based and Consistency Models for Full-band Text-to-Sound Generation","date":"2024-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sony/soundctm","path":"tango_edm/models_edm.py","file_url":"https://github.com/sony/soundctm/blob/HEAD/tango_edm/models_edm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8b16b0674af254db","mcp_get_code":{"code_sha256":"8b16b0674af254db"}},{"arxiv_id":"2404.07724","paper":"/paper/applying-guidance-in-a-limited-interval","title":"Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models","date":"2024-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kynkaat/guidance-interval","path":"sampling/edm_sampler.py","file_url":"https://github.com/kynkaat/guidance-interval/blob/HEAD/sampling/edm_sampler.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":"a1b475ef1c3f3263","mcp_get_code":{"code_sha256":"a1b475ef1c3f3263"}},{"arxiv_id":"2403.01189","paper":"/paper/training-unbiased-diffusion-models-from","title":"Training Unbiased Diffusion Models From Biased Dataset","date":"2024-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alsdudrla10/TIW-DSM","path":"generate.py","file_url":"https://github.com/alsdudrla10/TIW-DSM/blob/HEAD/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8b16b0674af254db","mcp_get_code":{"code_sha256":"8b16b0674af254db"}},{"arxiv_id":"2402.17517","paper":"/paper/label-noise-robust-diffusion-models","title":"Label-Noise Robust Diffusion Models","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"byeonghu-na/tdsm","path":"generate.py","file_url":"https://github.com/byeonghu-na/tdsm/blob/HEAD/generate.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":"3b64dcecdee0a7f5","mcp_get_code":{"code_sha256":"3b64dcecdee0a7f5"}},{"arxiv_id":"2401.04856","paper":"/paper/a-good-score-does-not-lead-to-a-good","title":"A Good Score Does not Lead to A Good Generative Model","date":"2024-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sixuli/ddpm_and_kde","path":"src/DiffMemorize/generate_optim.py","file_url":"https://github.com/sixuli/ddpm_and_kde/blob/HEAD/src/DiffMemorize/generate_optim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8add76eac47fd8ce","mcp_get_code":{"code_sha256":"8add76eac47fd8ce"}},{"arxiv_id":"2312.02696","paper":"/paper/analyzing-and-improving-the-training-dynamics","title":"Analyzing and Improving the Training Dynamics of Diffusion Models","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"a14db9805bf41197","mcp_get_code":{"code_sha256":"a14db9805bf41197"}},{"arxiv_id":"2310.05264","paper":"/paper/the-emergence-of-reproducibility-and","title":"The Emergence of Reproducibility and Generalizability in Diffusion Models","date":"2023-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huijieZH/Diffusion-Model-Generalizability","path":"edm/generate.py","file_url":"https://github.com/huijieZH/Diffusion-Model-Generalizability/blob/HEAD/edm/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00dcc1be26a2af0f","mcp_get_code":{"code_sha256":"00dcc1be26a2af0f"}},{"arxiv_id":"2310.04041","paper":"/paper/observation-guided-diffusion-probabilistic","title":"Observation-Guided Diffusion Probabilistic Models","date":"2023-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junoh-kang/ogdm_edm","path":"generate.py","file_url":"https://github.com/junoh-kang/ogdm_edm/blob/HEAD/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"abcc7581cc4b80ec","mcp_get_code":{"code_sha256":"abcc7581cc4b80ec"}},{"arxiv_id":"2310.02664","paper":"/paper/on-memorization-in-diffusion-models","title":"On Memorization in Diffusion Models","date":"2023-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/DiffMemorize","path":"generate_edm.py","file_url":"https://github.com/sail-sg/DiffMemorize/blob/HEAD/generate_edm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8b16b0674af254db","mcp_get_code":{"code_sha256":"8b16b0674af254db"}},{"arxiv_id":"2310.01236","paper":"/paper/mirror-diffusion-models-for-constrained-and","title":"Mirror Diffusion Models for Constrained and Watermarked Generation","date":"2023-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ghliu/mdm","path":"generate_watermark.py","file_url":"https://github.com/ghliu/mdm/blob/HEAD/generate_watermark.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":"8b16b0674af254db","mcp_get_code":{"code_sha256":"8b16b0674af254db"}},{"arxiv_id":"2309.03350","paper":"/paper/relay-diffusion-unifying-diffusion-process-1","title":"Relay Diffusion: Unifying diffusion process across resolutions for image synthesis","date":"2023-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"THUDM/RelayDiffusion","path":"generate_imagenet.py","file_url":"https://github.com/THUDM/RelayDiffusion/blob/HEAD/generate_imagenet.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":"00ad7cd5515a96f8","mcp_get_code":{"code_sha256":"00ad7cd5515a96f8"}},{"arxiv_id":"2306.09305","paper":"/paper/fast-training-of-diffusion-models-with-masked","title":"Fast Training of Diffusion Models with Masked Transformers","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anima-lab/maskdit","path":"sample.py","file_url":"https://github.com/anima-lab/maskdit/blob/HEAD/sample.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d84d2d3b03445f34","mcp_get_code":{"code_sha256":"d84d2d3b03445f34"}},{"arxiv_id":"2306.06991","paper":"/paper/fast-diffusion-model","title":"Fast Diffusion Model","date":"2023-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/fdm","path":"generate.py","file_url":"https://github.com/sail-sg/fdm/blob/HEAD/generate.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":"8b16b0674af254db","mcp_get_code":{"code_sha256":"8b16b0674af254db"}},{"arxiv_id":"2304.12526","paper":"/paper/patch-diffusion-faster-and-more-data-1","title":"Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models","date":"2023-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Zhendong-Wang/Patch-Diffusion","path":"generate.py","file_url":"https://github.com/Zhendong-Wang/Patch-Diffusion/blob/HEAD/generate.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":"9f88bec60fdc8cf7","mcp_get_code":{"code_sha256":"9f88bec60fdc8cf7"}},{"arxiv_id":"2303.10137","paper":"/paper/a-recipe-for-watermarking-diffusion-models","title":"A Recipe for Watermarking Diffusion Models","date":"2023-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunqing-me/watermarkdm","path":"edm/generate.py","file_url":"https://github.com/yunqing-me/watermarkdm/blob/HEAD/edm/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8b16b0674af254db","mcp_get_code":{"code_sha256":"8b16b0674af254db"}},{"arxiv_id":"2302.04265","paper":"/paper/pfgm-unlocking-the-potential-of-physics","title":"PFGM++: Unlocking the Potential of Physics-Inspired Generative Models","date":"2023-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"newbeeer/pfgmpp","path":"generate.py","file_url":"https://github.com/newbeeer/pfgmpp/blob/HEAD/generate.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"1a497dc67152411a","mcp_get_code":{"code_sha256":"1a497dc67152411a"}},{"arxiv_id":"2302.00670","paper":"/paper/stable-target-field-for-reduced-variance","title":"Stable Target Field for Reduced Variance Score Estimation in Diffusion Models","date":"2023-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"newbeeer/stf","path":"generate.py","file_url":"https://github.com/newbeeer/stf/blob/HEAD/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4fa854e489f921db","mcp_get_code":{"code_sha256":"4fa854e489f921db"}},{"arxiv_id":"2301.11445","paper":"/paper/3dshape2vecset-a-3d-shape-representation-for","title":"3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models","date":"2023-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"1zb/3dshape2vecset","path":"models_class_cond.py","file_url":"https://github.com/1zb/3dshape2vecset/blob/HEAD/models_class_cond.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b657cfb7a6465cbc","mcp_get_code":{"code_sha256":"b657cfb7a6465cbc"}},{"arxiv_id":"2206.00364","paper":"/paper/elucidating-the-design-space-of-diffusion","title":"Elucidating the Design Space of Diffusion-Based Generative Models","date":"2022-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8b16b0674af254db","mcp_get_code":{"code_sha256":"8b16b0674af254db"}},{"arxiv_id":"2203.17003","paper":"/paper/equivariant-diffusion-for-molecule-generation","title":"Equivariant Diffusion for Molecule Generation in 3D","date":"2022-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanzhi-zhu/mini_edm","path":"train_edm.py","file_url":"https://github.com/yuanzhi-zhu/mini_edm/blob/HEAD/train_edm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e4b3a16b7841ced","mcp_get_code":{"code_sha256":"3e4b3a16b7841ced"}},{"arxiv_id":"Xia_Rectified_Diffusion_Guidance_for_Conditional_Generation_CVPR_2025_paper","paper":null,"title":"arXiv:Xia_Rectified_Diffusion_Guidance_for_Conditional_Generation_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"thuxmf/recfg","path":"generate_images.py","file_url":"https://github.com/thuxmf/recfg/blob/HEAD/generate_images.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"53d8885663cb864c","mcp_get_code":{"code_sha256":"53d8885663cb864c"}}]}