{"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/qkvattentionlegacy","entry":"QKVAttentionLegacy","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":20,"n_papers_ran":17,"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":17,"n_samples_fingerprinted":1,"n_places":20,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":17,"unverified":3},"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":"2601.19498","paper":"/paper/arxiv-2601-19498","title":"Cortex-Grounded Diffusion Models for Brain Image Generation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"ai-med/Cor2Vox","path":"model/BrownianBridge/BrownianBridgeModel_c2v.py","file_url":"https://github.com/ai-med/Cor2Vox/blob/HEAD/model/BrownianBridge/BrownianBridgeModel_c2v.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"b8ea90bec9606340","mcp_get_code":{"code_sha256":"b8ea90bec9606340"}},{"arxiv_id":"2508.03256","paper":"/paper/arxiv-2508-03256","title":"Beyond Isolated Words: Diffusion Brush for Handwritten Text-Line Generation","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"dailenson/DiffBrush","path":"models/unet.py","file_url":"https://github.com/dailenson/DiffBrush/blob/HEAD/models/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f426ee49ae356e0","mcp_get_code":{"code_sha256":"7f426ee49ae356e0"}},{"arxiv_id":"2506.22463","paper":"/paper/modulated-diffusion-accelerating-generative","title":"Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization","date":"2025-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WeizhiGao/MoDiff","path":"qdiff/quant_model.py","file_url":"https://github.com/WeizhiGao/MoDiff/blob/HEAD/qdiff/quant_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"90bafd5717a0d799","mcp_get_code":{"code_sha256":"90bafd5717a0d799"}},{"arxiv_id":"2505.07071","paper":"/paper/semantic-guided-diffusion-model-for-single","title":"Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution","date":"2025-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Liu-Zihang/SAMSR","path":"models/unet.py","file_url":"https://github.com/Liu-Zihang/SAMSR/blob/HEAD/models/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1e895761b1061fb4","mcp_get_code":{"code_sha256":"1e895761b1061fb4"}},{"arxiv_id":"2503.09679","paper":"/paper/dress-disentangled-representation-based-self","title":"DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks","date":"2025-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"layer6ai-labs/DRESS","path":"encoders/diti.py","file_url":"https://github.com/layer6ai-labs/DRESS/blob/HEAD/encoders/diti.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"56122d5495b08e3f","mcp_get_code":{"code_sha256":"56122d5495b08e3f"}},{"arxiv_id":"2410.09400","paper":"/paper/ctrlora-an-extensible-and-efficient-framework","title":"CtrLoRA: An Extensible and Efficient Framework for Controllable Image Generation","date":"2024-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xyfjason/ctrlora","path":"cldm/cldm_ctrlora_inference.py","file_url":"https://github.com/xyfjason/ctrlora/blob/HEAD/cldm/cldm_ctrlora_inference.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":"0e10d815fe77258a","mcp_get_code":{"code_sha256":"0e10d815fe77258a"}},{"arxiv_id":"2403.18035","paper":"/paper/bidirectional-consistency-models","title":"Bidirectional Consistency Models","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mosasaur5526/BCM-iCT-torch","path":"BCM/cm/unet_bcf.py","file_url":"https://github.com/Mosasaur5526/BCM-iCT-torch/blob/HEAD/BCM/cm/unet_bcf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df0352093b65b725","mcp_get_code":{"code_sha256":"df0352093b65b725"}},{"arxiv_id":"2403.12580","paper":"/paper/real-iad-a-real-world-multi-view-dataset-for","title":"Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangzjn/ader","path":"model/realnet.py","file_url":"https://github.com/zhangzjn/ader/blob/HEAD/model/realnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a9911059044897f5","mcp_get_code":{"code_sha256":"a9911059044897f5"}},{"arxiv_id":"2310.12474","paper":"/paper/enhancing-high-resolution-3d-generation","title":"Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping","date":"2023-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fudan-zvg/pgc-3d","path":"ldm/modules/diffusionmodules/openaimodel.py","file_url":"https://github.com/fudan-zvg/pgc-3d/blob/HEAD/ldm/modules/diffusionmodules/openaimodel.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d1535f9d0cc44355","mcp_get_code":{"code_sha256":"d1535f9d0cc44355"}},{"arxiv_id":"2309.03729","paper":"/paper/phasic-content-fusing-diffusion-model-with","title":"Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjtuplayer/few-shot-diffusion","path":"model/big_unet.py","file_url":"https://github.com/sjtuplayer/few-shot-diffusion/blob/HEAD/model/big_unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c21412aa89ed28a7","mcp_get_code":{"code_sha256":"c21412aa89ed28a7"}},{"arxiv_id":"2305.18259","paper":"/paper/glyphcontrol-glyph-conditional-control-for-1","title":"GlyphControl: Glyph Conditional Control for Visual Text Generation","date":"2023-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aigtext/glyphcontrol-release","path":"cldm/cldm.py","file_url":"https://github.com/aigtext/glyphcontrol-release/blob/HEAD/cldm/cldm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ba0567fc5b71dbf6","mcp_get_code":{"code_sha256":"ba0567fc5b71dbf6"}},{"arxiv_id":"2305.16283","paper":"/paper/commonscenes-generating-commonsense-3d-indoor","title":"CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion","date":"2023-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ymxlzgy/commonscenes","path":"model/networks/diffusion_networks/sg_diff.py","file_url":"https://github.com/ymxlzgy/commonscenes/blob/HEAD/model/networks/diffusion_networks/sg_diff.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d6ede9e76b7e5eef","mcp_get_code":{"code_sha256":"d6ede9e76b7e5eef"}},{"arxiv_id":"2303.17189","paper":"/paper/layoutdiffusion-controllable-diffusion-model","title":"LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation","date":"2023-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zgctroy/layoutdiffusion","path":"layout_diffusion/layout_diffusion_unet.py","file_url":"https://github.com/zgctroy/layoutdiffusion/blob/HEAD/layout_diffusion/layout_diffusion_unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b8ac15fc31c4c31d","mcp_get_code":{"code_sha256":"b8ac15fc31c4c31d"}},{"arxiv_id":"2303.05754","paper":"/paper/fast-diffusion-sampler-for-inverse-problems","title":"Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems","date":"2023-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hj-harry/dds","path":"solver_2d.py","file_url":"https://github.com/hj-harry/dds/blob/HEAD/solver_2d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6503094226d94604","mcp_get_code":{"code_sha256":"6503094226d94604"}},{"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":"sainzerjj/sferd","path":"unet/student_unet.py","file_url":"https://github.com/sainzerjj/sferd/blob/HEAD/unet/student_unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f9e4398d47ea4296","mcp_get_code":{"code_sha256":"f9e4398d47ea4296"}},{"arxiv_id":"2302.07685","paper":"/paper/video-probabilistic-diffusion-models-in","title":"Video Probabilistic Diffusion Models in Projected Latent Space","date":"2023-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sihyun-yu/pvdm","path":"models/ddpm/unet.py","file_url":"https://github.com/sihyun-yu/pvdm/blob/HEAD/models/ddpm/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"26f661f1ebafd2e9","mcp_get_code":{"code_sha256":"26f661f1ebafd2e9"}},{"arxiv_id":"2301.05225","paper":"/paper/domain-expansion-of-image-generators","title":"Domain Expansion of Image Generators","date":"2023-01-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lllyasviel/controlnet","path":"cldm/cldm.py","file_url":"https://github.com/lllyasviel/controlnet/blob/HEAD/cldm/cldm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8dd1080ff568f7f4","mcp_get_code":{"code_sha256":"8dd1080ff568f7f4"}},{"arxiv_id":"2212.12990","paper":"/paper/unsupervised-representation-learning-from-pre","title":"Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models","date":"2022-12-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ckczzj/pdae","path":"model/shift_unet.py","file_url":"https://github.com/ckczzj/pdae/blob/HEAD/model/shift_unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec9213c0c8138849","mcp_get_code":{"code_sha256":"ec9213c0c8138849"}},{"arxiv_id":"2105.05233","paper":"/paper/diffusion-models-beat-gans-on-image-synthesis","title":"Diffusion Models Beat GANs on Image Synthesis","date":"2021-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Janspiry/Palette-Image-to-Image-Diffusion-Models","path":"models/guided_diffusion_modules/unet.py","file_url":"https://github.com/Janspiry/Palette-Image-to-Image-Diffusion-Models/blob/HEAD/models/guided_diffusion_modules/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9fd7822b33474816","mcp_get_code":{"code_sha256":"9fd7822b33474816"}},{"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":"Ashwin-Pokharel/base_diffusion","path":"model.py","file_url":"https://github.com/Ashwin-Pokharel/base_diffusion/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1c07b81f544f048b","mcp_get_code":{"code_sha256":"1c07b81f544f048b"}}]}