{"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/aggregate-attention","entry":"aggregate_attention","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":17,"n_papers_ran":10,"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":16,"n_samples_ran":9,"n_samples_fingerprinted":0,"n_places":18,"n_places_pointer_only":11,"by_status":{"ran_honours":5,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":4,"unverified":7},"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":"2412.02237","paper":"/paper/cross-attention-head-position-patterns-can","title":"Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models","date":"2024-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snu-drl/hrv","path":"applications/attend_and_excite_hrv.py","file_url":"https://github.com/snu-drl/hrv/blob/HEAD/applications/attend_and_excite_hrv.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ca57b6c2d536bc73","mcp_get_code":{"code_sha256":"ca57b6c2d536bc73"}},{"arxiv_id":"2409.19967","paper":"/paper/magnet-we-never-know-how-text-to-image","title":"Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function","date":"2024-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"I2-Multimedia-Lab/Magnet","path":"utils/ptp_utils.py","file_url":"https://github.com/I2-Multimedia-Lab/Magnet/blob/HEAD/utils/ptp_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a020c1a6b1012624","mcp_get_code":{"code_sha256":"a020c1a6b1012624"}},{"arxiv_id":"2409.12466","paper":"/paper/audioeditor-a-training-free-diffusion-based","title":"AudioEditor: A Training-Free Diffusion-Based Audio Editing Framework","date":null,"month_inferred_from_arxiv_id":"2024-09","title_source":"archive","repo":"nku-hlt/audioeditor","path":"prompt2prompt/attn_control.py","file_url":"https://github.com/nku-hlt/audioeditor/blob/HEAD/prompt2prompt/attn_control.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"a0df83fb9cd93265","mcp_get_code":{"code_sha256":"a0df83fb9cd93265"}},{"arxiv_id":"2404.04960","paper":"/paper/pairaug-what-can-augmented-image-text-pairs","title":"PairAug: What Can Augmented Image-Text Pairs Do for Radiology?","date":"2024-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YtongXie/PairAug","path":"IntraAug_Step2_T2I.py","file_url":"https://github.com/YtongXie/PairAug/blob/HEAD/IntraAug_Step2_T2I.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"23ca7e055397296f","mcp_get_code":{"code_sha256":"23ca7e055397296f"}},{"arxiv_id":"2403.11627","paper":"/paper/lora-composer-leveraging-low-rank-adaptation","title":"LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models","date":"2024-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"young98cn/lora_composer","path":"region_lora/utils/attn_util.py","file_url":"https://github.com/young98cn/lora_composer/blob/HEAD/region_lora/utils/attn_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4ea31d866b78ba77","mcp_get_code":{"code_sha256":"4ea31d866b78ba77"}},{"arxiv_id":"2402.11846","paper":"/paper/unlearncanvas-a-stylized-image-dataset-to","title":"UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sen-mao/SuppressEOT","path":"suppress_eot_w_nulltext.py","file_url":"https://github.com/sen-mao/SuppressEOT/blob/HEAD/suppress_eot_w_nulltext.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f3e6737ebc70cbbf","mcp_get_code":{"code_sha256":"f3e6737ebc70cbbf"}},{"arxiv_id":"2402.05375","paper":"/paper/get-what-you-want-not-what-you-don-t-image","title":"Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models","date":"2024-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sen-mao/suppresseot","path":"suppress_eot_w_nulltext.py","file_url":"https://github.com/sen-mao/suppresseot/blob/HEAD/suppress_eot_w_nulltext.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f3e6737ebc70cbbf","mcp_get_code":{"code_sha256":"f3e6737ebc70cbbf"}},{"arxiv_id":"2401.07709","paper":"/paper/towards-efficient-diffusion-based-image","title":"Towards Efficient Diffusion-Based Image Editing with Instant Attention Masks","date":"2024-01-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaotianqing/InstDiffEdit","path":"model/attention_store.py","file_url":"https://github.com/xiaotianqing/InstDiffEdit/blob/HEAD/model/attention_store.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"44d6e9270531af10","mcp_get_code":{"code_sha256":"44d6e9270531af10"}},{"arxiv_id":"2309.14303","paper":"/paper/dataset-diffusion-diffusion-based-synthetic","title":"Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic Segmentation","date":"2023-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VinAIResearch/Dataset-Diffusion","path":"src/attn_processor.py","file_url":"https://github.com/VinAIResearch/Dataset-Diffusion/blob/HEAD/src/attn_processor.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"bdc53ede381a35c8","mcp_get_code":{"code_sha256":"bdc53ede381a35c8"}},{"arxiv_id":"2309.02773","paper":"/paper/diffusion-model-is-secretly-a-training-free","title":"Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter","date":"2023-09-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VCG-team/DiffSegmenter","path":"open_vocabulary/coco/ptp_stable_coco.py","file_url":"https://github.com/VCG-team/DiffSegmenter/blob/HEAD/open_vocabulary/coco/ptp_stable_coco.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"35c56b6e1ae0a476","mcp_get_code":{"code_sha256":"35c56b6e1ae0a476"}},{"arxiv_id":"2307.14352","paper":"/paper/general-image-to-image-translation-with-one","title":"General Image-to-Image Translation with One-Shot Image Guidance","date":"2023-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"crystalneuro/visual-concept-translator","path":"ptp_inversion.py","file_url":"https://github.com/crystalneuro/visual-concept-translator/blob/HEAD/ptp_inversion.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":"23ca7e055397296f","mcp_get_code":{"code_sha256":"23ca7e055397296f"}},{"arxiv_id":"2307.10864","paper":"/paper/divide-bind-your-attention-for-improved","title":"Divide & Bind Your Attention for Improved Generative Semantic Nursing","date":"2023-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"boschresearch/Divide-and-Bind","path":"utils/annotation_sd.py","file_url":"https://github.com/boschresearch/Divide-and-Bind/blob/HEAD/utils/annotation_sd.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"c2a62ea502f8be8b","mcp_get_code":{"code_sha256":"c2a62ea502f8be8b"}},{"arxiv_id":"2307.10816","paper":"/paper/boxdiff-text-to-image-synthesis-with-training","title":"BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion","date":"2023-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"showlab/boxdiff","path":"utils/ptp_utils.py","file_url":"https://github.com/showlab/boxdiff/blob/HEAD/utils/ptp_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3cfdfcac97b762af","mcp_get_code":{"code_sha256":"3cfdfcac97b762af"}},{"arxiv_id":"2304.06140","paper":"/paper/an-edit-friendly-ddpm-noise-space-inversion","title":"An Edit Friendly DDPM Noise Space: Inversion and Manipulations","date":"2023-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"inbarhub/ddpm_inversion","path":"prompt_to_prompt/ptp_classes.py","file_url":"https://github.com/inbarhub/ddpm_inversion/blob/HEAD/prompt_to_prompt/ptp_classes.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e151ef7384cb586b","mcp_get_code":{"code_sha256":"e151ef7384cb586b"}},{"arxiv_id":"2301.13826","paper":"/paper/attend-and-excite-attention-based-semantic","title":"Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models","date":"2023-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AttendAndExcite/Attend-and-Excite","path":"utils/ptp_utils.py","file_url":"https://github.com/AttendAndExcite/Attend-and-Excite/blob/HEAD/utils/ptp_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e101acf585a7866f","mcp_get_code":{"code_sha256":"e101acf585a7866f"}},{"arxiv_id":"2211.09794","paper":"/paper/null-text-inversion-for-editing-real-images","title":"Null-text Inversion for Editing Real Images using Guided Diffusion Models","date":"2022-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"phymhan/prompt-to-prompt","path":"null_text_inversion.py","file_url":"https://github.com/phymhan/prompt-to-prompt/blob/HEAD/null_text_inversion.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6db9848012fc30bf","mcp_get_code":{"code_sha256":"6db9848012fc30bf"}},{"arxiv_id":"aaai_28210","paper":null,"title":"arXiv:aaai_28210","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"AnonymousPony/adap-edit","path":"prompt-to-prompt_ldm.py","file_url":"https://github.com/AnonymousPony/adap-edit/blob/HEAD/prompt-to-prompt_ldm.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":"1acfda981f18f522","mcp_get_code":{"code_sha256":"1acfda981f18f522"}},{"arxiv_id":"aaai_28210","paper":null,"title":"arXiv:aaai_28210","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"AnonymousPony/adap-edit","path":"prompt-to-prompt_stable.py","file_url":"https://github.com/AnonymousPony/adap-edit/blob/HEAD/prompt-to-prompt_stable.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":"5589c49ba7cefbe3","mcp_get_code":{"code_sha256":"5589c49ba7cefbe3"}}]}