{"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/basictransformerblock","entry":"BasicTransformerBlock","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":22,"n_papers_ran":6,"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":22,"n_samples_ran":6,"n_samples_fingerprinted":0,"n_places":22,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":6,"unverified":16},"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"624f432e698172de","mcp_get_code":{"code_sha256":"624f432e698172de"}},{"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":"560b9460a4ba3c95","mcp_get_code":{"code_sha256":"560b9460a4ba3c95"}},{"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":"fe821a135405b676","mcp_get_code":{"code_sha256":"fe821a135405b676"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c02f4e63f9a3b275","mcp_get_code":{"code_sha256":"c02f4e63f9a3b275"}},{"arxiv_id":"2407.10179","paper":"/paper/clip-guided-networks-for-transferable","title":"CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks","date":"2024-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ffhibnese/CGNC_Targeted_Adversarial_Attacks","path":"models/generator.py","file_url":"https://github.com/ffhibnese/CGNC_Targeted_Adversarial_Attacks/blob/HEAD/models/generator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"187d70c9c004d1e3","mcp_get_code":{"code_sha256":"187d70c9c004d1e3"}},{"arxiv_id":"2405.11913","paper":"/paper/diff-bgm-a-diffusion-model-for-video","title":"Diff-BGM: A Diffusion Model for Video Background Music Generation","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sizhelee/Diff-BGM","path":"diffbgm/stable_diffusion/model/unet.py","file_url":"https://github.com/sizhelee/Diff-BGM/blob/HEAD/diffbgm/stable_diffusion/model/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e853e757414f297","mcp_get_code":{"code_sha256":"9e853e757414f297"}},{"arxiv_id":"2404.11474","paper":"/paper/towards-highly-realistic-artistic-style","title":"Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt","date":"2024-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jamie-Cheung/LSAST","path":"cldm/cldm.py","file_url":"https://github.com/Jamie-Cheung/LSAST/blob/HEAD/cldm/cldm.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":"cd763b5f891b0938","mcp_get_code":{"code_sha256":"cd763b5f891b0938"}},{"arxiv_id":"2403.06951","paper":"/paper/deadiff-an-efficient-stylization-diffusion","title":"DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Tianhao-Qi/DEADiff_code","path":"ldm/modules/new_attention.py","file_url":"https://github.com/Tianhao-Qi/DEADiff_code/blob/HEAD/ldm/modules/new_attention.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":"5fc1cad8b1b47d66","mcp_get_code":{"code_sha256":"5fc1cad8b1b47d66"}},{"arxiv_id":"2402.19387","paper":"/paper/sed-semantic-aware-discriminator-for-image","title":"SeD: Semantic-Aware Discriminator for Image Super-Resolution","date":"2024-02-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lbc12345/sed","path":"models/sed.py","file_url":"https://github.com/lbc12345/sed/blob/HEAD/models/sed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8648f9eedf9e67ba","mcp_get_code":{"code_sha256":"8648f9eedf9e67ba"}},{"arxiv_id":"2311.04640","paper":"/paper/object-centric-learning-with-slot-mixture","title":"Object-Centric Learning with Slot Mixture Module","date":"2023-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"airi-institute/smm","path":"modules/smm.py","file_url":"https://github.com/airi-institute/smm/blob/HEAD/modules/smm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"62c19c9f7ad564f1","mcp_get_code":{"code_sha256":"62c19c9f7ad564f1"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ddc045dc2824caf3","mcp_get_code":{"code_sha256":"ddc045dc2824caf3"}},{"arxiv_id":"2309.04803","paper":"/paper/towards-real-world-burst-image-super","title":"Towards Real-World Burst Image Super-Resolution: Benchmark and Method","date":"2023-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yjsunnn/FBANet","path":"model.py","file_url":"https://github.com/yjsunnn/FBANet/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6cadca82908e051f","mcp_get_code":{"code_sha256":"6cadca82908e051f"}},{"arxiv_id":"2308.05095","paper":"/paper/layoutllm-t2i-eliciting-layout-guidance-from","title":"LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation","date":"2023-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"layoutllm-t2i/layoutllm-t2i","path":"GLIGEN/ldm/modules/diffusionmodules/gligen_combine_layout.py","file_url":"https://github.com/layoutllm-t2i/layoutllm-t2i/blob/HEAD/GLIGEN/ldm/modules/diffusionmodules/gligen_combine_layout.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"71d199572f378540","mcp_get_code":{"code_sha256":"71d199572f378540"}},{"arxiv_id":"2307.01952","paper":"/paper/sdxl-improving-latent-diffusion-models-for","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","date":"2023-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stability-ai/generative-models","path":"sgm/modules/diffusionmodules/openaimodel.py","file_url":"https://github.com/stability-ai/generative-models/blob/HEAD/sgm/modules/diffusionmodules/openaimodel.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"40d8ba71b73d29d4","mcp_get_code":{"code_sha256":"40d8ba71b73d29d4"}},{"arxiv_id":"2307.01097","paper":"/paper/mvdiffusion-enabling-holistic-multi-view-1","title":"MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion","date":"2023-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Tangshitao/MVDiffusion","path":"src/models/pano/MVGenModel.py","file_url":"https://github.com/Tangshitao/MVDiffusion/blob/HEAD/src/models/pano/MVGenModel.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"17e606b60296442a","mcp_get_code":{"code_sha256":"17e606b60296442a"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c8644b25d74977f2","mcp_get_code":{"code_sha256":"c8644b25d74977f2"}},{"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":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0d3d0643f8f2a574","mcp_get_code":{"code_sha256":"0d3d0643f8f2a574"}},{"arxiv_id":"2304.03869","paper":"/paper/harnessing-the-spatial-temporal-attention-of","title":"Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis","date":"2023-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucsb-nlp-chang/diffusion-spacetime-attn","path":"attention_optimization/stable-diffusion/ldm/modules/attention.py","file_url":"https://github.com/ucsb-nlp-chang/diffusion-spacetime-attn/blob/HEAD/attention_optimization/stable-diffusion/ldm/modules/attention.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"77fb9b023531cd68","mcp_get_code":{"code_sha256":"77fb9b023531cd68"}},{"arxiv_id":"2304.02012","paper":"/paper/egc-image-generation-and-classification-via-a","title":"EGC: Image Generation and Classification via a Diffusion Energy-Based Model","date":"2023-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GuoQiushan/EGC","path":"guided_diffusion/unet.py","file_url":"https://github.com/GuoQiushan/EGC/blob/HEAD/guided_diffusion/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9e0b6eeb542371a5","mcp_get_code":{"code_sha256":"9e0b6eeb542371a5"}},{"arxiv_id":"2303.14412","paper":"/paper/freestyle-layout-to-image-synthesis","title":"Freestyle Layout-to-Image Synthesis","date":"2023-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"essunny310/FreestyleNet","path":"ldm/modules/attention_FLIS.py","file_url":"https://github.com/essunny310/FreestyleNet/blob/HEAD/ldm/modules/attention_FLIS.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"099c3f24dbc4c5dc","mcp_get_code":{"code_sha256":"099c3f24dbc4c5dc"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"76c528e8baaee5c6","mcp_get_code":{"code_sha256":"76c528e8baaee5c6"}},{"arxiv_id":"2211.05556","paper":"/paper/generating-astronomical-spectra-from","title":"Generating astronomical spectra from photometry with conditional diffusion models","date":null,"month_inferred_from_arxiv_id":"2022-11","title_source":"archive","repo":"larsdoorenbos/generate-spectra","path":"generative/unet.py","file_url":"https://github.com/larsdoorenbos/generate-spectra/blob/HEAD/generative/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e3b8a2acc2178d99","mcp_get_code":{"code_sha256":"e3b8a2acc2178d99"}}]}