{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/image-generation/papers/17","list_of":"/task/image-generation","task":"Image Generation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":17,"pages_in_order":67,"rows_per_page":100,"rows":[1601,1700],"of":6689,"counts":{"archive_papers_tagged":6689,"with_a_code_link":3102,"where_syntology_ran_a_sample":1223,"not_listed_spam_title":0,"listed":6689,"listed_where_code_ran":1223,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1063,"every_run_a_failure_of_syntologys_instrument":160,"listed_with_a_run_with_no_instrument_failure":1063,"listed_every_run_a_failure_of_syntologys_instrument":160,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/image-generation","prev":"/task/image-generation/papers/16","next":"/task/image-generation/papers/18","papers":[{"url":"/paper/coarse-to-fine-latent-diffusion-for-pose","slug":"coarse-to-fine-latent-diffusion-for-pose","title":"Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis","date":"2024-02-28","arxiv_id":"2402.18078","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/coarse-to-fine-latent-diffusion-for-pose#ran","syntology_url":"https://syntology.ai/paper/2402.18078","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18078"}},"official":{"repos":["YanzuoLu/CFLD"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/finediffusion-scaling-up-diffusion-models-for","slug":"finediffusion-scaling-up-diffusion-models-for","title":"FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 Classes","date":"2024-02-28","arxiv_id":"2402.18331","repositories_listed":1,"syntology":null},{"url":"/paper/synartifact-classifying-and-alleviating","slug":"synartifact-classifying-and-alleviating","title":"SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Model","date":"2024-02-28","arxiv_id":"2402.18068","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-diffusion-sampling-with-1","slug":"accelerating-diffusion-sampling-with-1","title":"Accelerating Diffusion Sampling with Optimized Time Steps","date":"2024-02-27","arxiv_id":"2402.17376","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/accelerating-diffusion-sampling-with-1#ran","syntology_url":"https://syntology.ai/paper/2402.17376","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17376"}},"official":{"repos":["scxue/DM-NonUniform"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-model-based-image-editing-a-survey","slug":"diffusion-model-based-image-editing-a-survey","title":"Diffusion Model-Based Image Editing: A Survey","date":"2024-02-27","arxiv_id":"2402.17525","repositories_listed":1,"syntology":null},{"url":"/paper/nocplace-nocturnal-visual-place-recognition","slug":"nocplace-nocturnal-visual-place-recognition","title":"NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer","date":"2024-02-27","arxiv_id":"2402.17159","repositories_listed":1,"syntology":null},{"url":"/paper/one-shot-structure-aware-stylized-image","slug":"one-shot-structure-aware-stylized-image","title":"One-Shot Structure-Aware Stylized Image Synthesis","date":"2024-02-27","arxiv_id":"2402.17275","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-contextualized-diffusion-models","slug":"cross-modal-contextualized-diffusion-models","title":"Contextualized Diffusion Models for Text-Guided Image and Video Generation","date":"2024-02-26","arxiv_id":"2402.16627","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cross-modal-contextualized-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2402.16627","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16627"}},"official":{"repos":["yangling0818/contextdiff"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/stochastic-conditional-diffusion-models-for","slug":"stochastic-conditional-diffusion-models-for","title":"Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis","date":"2024-02-26","arxiv_id":"2402.16506","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/stochastic-conditional-diffusion-models-for#ran","syntology_url":"https://syntology.ai/paper/2402.16506","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16506"}},"official":{"repos":["mlvlab/scdm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/gen4gen-generative-data-pipeline-for","slug":"gen4gen-generative-data-pipeline-for","title":"Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition","date":"2024-02-23","arxiv_id":"2402.15504","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/gen4gen-generative-data-pipeline-for#ran","syntology_url":"https://syntology.ai/paper/2402.15504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15504"}},"official":{"repos":["louisYen/Gen4Gen"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/srndiff-short-term-rainfall-nowcasting-with","slug":"srndiff-short-term-rainfall-nowcasting-with","title":"SRNDiff: Short-term Rainfall Nowcasting with Condition Diffusion Model","date":"2024-02-21","arxiv_id":"2402.13737","repositories_listed":1,"syntology":null},{"url":"/paper/t-stitch-accelerating-sampling-in-pre-trained","slug":"t-stitch-accelerating-sampling-in-pre-trained","title":"T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching","date":"2024-02-21","arxiv_id":"2402.14167","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":8,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/t-stitch-accelerating-sampling-in-pre-trained#ran","syntology_url":"https://syntology.ai/paper/2402.14167","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.14167"}},"official":{"repos":["nvlabs/t-stitch"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/a-user-friendly-framework-for-generating","slug":"a-user-friendly-framework-for-generating","title":"A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis","date":"2024-02-20","arxiv_id":"2402.12760","repositories_listed":1,"syntology":null},{"url":"/paper/countercurate-enhancing-physical-and-semantic","slug":"countercurate-enhancing-physical-and-semantic","title":"CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples","date":"2024-02-20","arxiv_id":"2402.13254","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/countercurate-enhancing-physical-and-semantic#ran","syntology_url":"https://syntology.ai/paper/2402.13254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.13254"}},"official":{"repos":["hansolo9682/countercurate"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/visual-style-prompting-with-swapping-self","slug":"visual-style-prompting-with-swapping-self","title":"Visual Style Prompting with Swapping Self-Attention","date":"2024-02-20","arxiv_id":"2402.12974","repositories_listed":1,"syntology":{"n":16,"n_ran":16,"n_constructed":0,"n_ran_checked":15,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/visual-style-prompting-with-swapping-self#ran","syntology_url":"https://syntology.ai/paper/2402.12974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12974"}},"official":{"repos":["naver-ai/Visual-Style-Prompting"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dilightnet-fine-grained-lighting-control-for","slug":"dilightnet-fine-grained-lighting-control-for","title":"DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation","date":"2024-02-19","arxiv_id":"2402.11929","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dilightnet-fine-grained-lighting-control-for#ran","syntology_url":"https://syntology.ai/paper/2402.11929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11929"}},"official":{"repos":["iamNCJ/DiLightNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/the-r-evolution-of-multimodal-large-language","slug":"the-r-evolution-of-multimodal-large-language","title":"The Revolution of Multimodal Large Language Models: A Survey","date":"2024-02-19","arxiv_id":"2402.12451","repositories_listed":1,"syntology":null},{"url":"/paper/unlearncanvas-a-stylized-image-dataset-to","slug":"unlearncanvas-a-stylized-image-dataset-to","title":"UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models","date":"2024-02-19","arxiv_id":"2402.11846","repositories_listed":1,"syntology":{"n":74,"n_ran":55,"n_constructed":0,"n_ran_checked":43,"n_instrument":12,"n_unverified":19,"n_honours":2,"n_violates":1,"n_no_contract":40,"n_pointer_only":40,"phrase":"55 ran (of which 0 constructed an object rather than computing a result; 43 with no instrument failure: 2 honoured, 1 violated, 40 with no contract checked; 12 where Syntology's instrument failed) · 19 unverified","sample_list":"/paper/unlearncanvas-a-stylized-image-dataset-to#ran","syntology_url":"https://syntology.ai/paper/2402.11846","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11846"}},"official":{"repos":["optml-group/unlearncanvas"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/tc-diffrecon-texture-coordination-mri","slug":"tc-diffrecon-texture-coordination-mri","title":"TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method","date":"2024-02-17","arxiv_id":"2402.11274","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-precision-and-recall-to-assess-the","slug":"exploring-precision-and-recall-to-assess-the","title":"Exploring Precision and Recall to assess the quality and diversity of LLMs","date":"2024-02-16","arxiv_id":"2402.10693","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/exploring-precision-and-recall-to-assess-the#ran","syntology_url":"https://syntology.ai/paper/2402.10693","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10693"}},"official":{"repos":["alexverine/pr-4-llm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fundamental-benefit-of-alternating-updates-in","slug":"fundamental-benefit-of-alternating-updates-in","title":"Fundamental Benefit of Alternating Updates in Minimax Optimization","date":"2024-02-16","arxiv_id":"2402.10475","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/fundamental-benefit-of-alternating-updates-in#ran","syntology_url":"https://syntology.ai/paper/2402.10475","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10475"}},"official":{"repos":["hanseuljo/alex-gda"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/training-class-imbalanced-diffusion-model-via","slug":"training-class-imbalanced-diffusion-model-via","title":"Training Class-Imbalanced Diffusion Model Via Overlap Optimization","date":"2024-02-16","arxiv_id":"2402.10821","repositories_listed":1,"syntology":null},{"url":"/paper/umair-fps-user-aware-multi-modal-animation","slug":"umair-fps-user-aware-multi-modal-animation","title":"UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style","date":"2024-02-16","arxiv_id":"2402.10381","repositories_listed":1,"syntology":null},{"url":"/paper/universal-prompt-optimizer-for-safe-text-to","slug":"universal-prompt-optimizer-for-safe-text-to","title":"Universal Prompt Optimizer for Safe Text-to-Image Generation","date":"2024-02-16","arxiv_id":"2402.10882","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-parallel-sampling-of-diffusion","slug":"accelerating-parallel-sampling-of-diffusion","title":"Accelerating Parallel Sampling of Diffusion Models","date":"2024-02-15","arxiv_id":"2402.09970","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/accelerating-parallel-sampling-of-diffusion#ran","syntology_url":"https://syntology.ai/paper/2402.09970","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09970"}},"official":{"repos":["tzw1998/parataa-diffusion"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/social-reward-evaluating-and-enhancing","slug":"social-reward-evaluating-and-enhancing","title":"Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community","date":"2024-02-15","arxiv_id":"2402.09872","repositories_listed":1,"syntology":null},{"url":"/paper/textual-localization-decomposing-multi","slug":"textual-localization-decomposing-multi","title":"Textual Localization: Decomposing Multi-concept Images for Subject-Driven Text-to-Image Generation","date":"2024-02-15","arxiv_id":"2402.09966","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-nibbler-an-open-red-teaming","slug":"adversarial-nibbler-an-open-red-teaming","title":"Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation","date":"2024-02-14","arxiv_id":"2403.12075","repositories_listed":1,"syntology":null},{"url":"/paper/magic-me-identity-specific-video-customized","slug":"magic-me-identity-specific-video-customized","title":"Magic-Me: Identity-Specific Video Customized Diffusion","date":"2024-02-14","arxiv_id":"2402.09368","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/magic-me-identity-specific-video-customized#ran","syntology_url":"https://syntology.ai/paper/2402.09368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09368"}},"official":{"repos":["zhen-dong/magic-me"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-continuous-3d-words-for-text-to","slug":"learning-continuous-3d-words-for-text-to","title":"Learning Continuous 3D Words for Text-to-Image Generation","date":"2024-02-13","arxiv_id":"2402.08654","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/learning-continuous-3d-words-for-text-to#ran","syntology_url":"https://syntology.ai/paper/2402.08654","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.08654"}},"official":{"repos":["ttchengab/continuous_3d_words_code"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/migc-multi-instance-generation-controller-for","slug":"migc-multi-instance-generation-controller-for","title":"MIGC: Multi-Instance Generation Controller for Text-to-Image Synthesis","date":"2024-02-08","arxiv_id":"2402.05408","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/migc-multi-instance-generation-controller-for#ran","syntology_url":"https://syntology.ai/paper/2402.05408","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.05408"}},"official":{"repos":["limuloo/migc"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/anatomically-controllable-medical-image","slug":"anatomically-controllable-medical-image","title":"Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models","date":"2024-02-07","arxiv_id":"2402.05210","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/anatomically-controllable-medical-image#ran","syntology_url":"https://syntology.ai/paper/2402.05210","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.05210"}},"official":{"repos":["mazurowski-lab/segmentation-guided-diffusion"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/colorswap-a-color-and-word-order-dataset-for","slug":"colorswap-a-color-and-word-order-dataset-for","title":"ColorSwap: A Color and Word Order Dataset for Multimodal Evaluation","date":"2024-02-07","arxiv_id":"2402.04492","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/colorswap-a-color-and-word-order-dataset-for#ran","syntology_url":"https://syntology.ai/paper/2402.04492","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.04492"}},"official":{"repos":["top34051/colorswap"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/evoseed-unveiling-the-threat-on-deep-neural","slug":"evoseed-unveiling-the-threat-on-deep-neural","title":"Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!","date":"2024-02-07","arxiv_id":"2402.04699","repositories_listed":1,"syntology":null},{"url":"/paper/noise-map-guidance-inversion-with-spatial","slug":"noise-map-guidance-inversion-with-spatial","title":"Noise Map Guidance: Inversion with Spatial Context for Real Image Editing","date":"2024-02-07","arxiv_id":"2402.04625","repositories_listed":1,"syntology":null},{"url":"/paper/quest-low-bit-diffusion-model-quantization","slug":"quest-low-bit-diffusion-model-quantization","title":"QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning","date":"2024-02-06","arxiv_id":"2402.03666","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":3,"n_instrument":8,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":11,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 8 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/quest-low-bit-diffusion-model-quantization#ran","syntology_url":"https://syntology.ai/paper/2402.03666","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03666"}},"official":{"repos":["hatchetProject/QuEST"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/iguane-a-3d-generalizable-cyclegan-for","slug":"iguane-a-3d-generalizable-cyclegan-for","title":"IGUANe: a 3D generalizable CycleGAN for multicenter harmonization of brain MR images","date":"2024-02-05","arxiv_id":"2402.03227","repositories_listed":1,"syntology":null},{"url":"/paper/instancediffusion-instance-level-control-for","slug":"instancediffusion-instance-level-control-for","title":"InstanceDiffusion: Instance-level Control for Image Generation","date":"2024-02-05","arxiv_id":"2402.03290","repositories_listed":1,"syntology":{"n":18,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":0,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/instancediffusion-instance-level-control-for#ran","syntology_url":"https://syntology.ai/paper/2402.03290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03290"}},"official":{"repos":["frank-xwang/InstanceDiffusion"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/training-free-consistent-text-to-image","slug":"training-free-consistent-text-to-image","title":"Training-Free Consistent Text-to-Image Generation","date":"2024-02-05","arxiv_id":"2402.03286","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/training-free-consistent-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2402.03286","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03286"}},"official":null}},{"url":"/paper/diffusion-cross-domain-recommendation","slug":"diffusion-cross-domain-recommendation","title":"Diffusion Cross-domain Recommendation","date":"2024-02-03","arxiv_id":"2402.02182","repositories_listed":1,"syntology":null},{"url":"/paper/can-mllms-perform-text-to-image-in-context","slug":"can-mllms-perform-text-to-image-in-context","title":"Can MLLMs Perform Text-to-Image In-Context Learning?","date":"2024-02-02","arxiv_id":"2402.01293","repositories_listed":1,"syntology":null},{"url":"/paper/cheating-suffix-targeted-attack-to-text-to","slug":"cheating-suffix-targeted-attack-to-text-to","title":"On the Multi-modal Vulnerability of Diffusion Models","date":"2024-02-02","arxiv_id":"2402.01369","repositories_listed":1,"syntology":null},{"url":"/paper/cross-view-masked-diffusion-transformers-for","slug":"cross-view-masked-diffusion-transformers-for","title":"Cross-view Masked Diffusion Transformers for Person Image Synthesis","date":"2024-02-02","arxiv_id":"2402.01516","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/cross-view-masked-diffusion-transformers-for#ran","syntology_url":"https://syntology.ai/paper/2402.01516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01516"}},"official":{"repos":["trungpx/xmdpt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/animatelcm-accelerating-the-animation-of","slug":"animatelcm-accelerating-the-animation-of","title":"AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data","date":"2024-02-01","arxiv_id":"2402.00769","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/animatelcm-accelerating-the-animation-of#ran","syntology_url":"https://syntology.ai/paper/2402.00769","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.00769"}},"official":{"repos":["g-u-n/animatelcm"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/caphuman-capture-your-moments-in-parallel","slug":"caphuman-capture-your-moments-in-parallel","title":"CapHuman: Capture Your Moments in Parallel Universes","date":"2024-02-01","arxiv_id":"2402.00627","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":2,"n_no_contract":3,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 2 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/caphuman-capture-your-moments-in-parallel#ran","syntology_url":"https://syntology.ai/paper/2402.00627","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.00627"}},"official":{"repos":["vamosc/caphuman"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unconditional-latent-diffusion-models","slug":"unconditional-latent-diffusion-models","title":"Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data","date":"2024-02-01","arxiv_id":"2402.01054","repositories_listed":1,"syntology":null},{"url":"/paper/repositioning-the-subject-within-image","slug":"repositioning-the-subject-within-image","title":"Repositioning the Subject within Image","date":"2024-01-30","arxiv_id":"2401.16861","repositories_listed":1,"syntology":null},{"url":"/paper/through-wall-imaging-based-on-wifi-channel","slug":"through-wall-imaging-based-on-wifi-channel","title":"Through-Wall Imaging based on WiFi Channel State Information","date":"2024-01-30","arxiv_id":"2401.17417","repositories_listed":1,"syntology":null},{"url":"/paper/x-ray-image-generation-as-a-method-of","slug":"x-ray-image-generation-as-a-method-of","title":"X-ray Image Generation as a Method of Performance Prediction for Real-Time Inspection: a Case Study","date":"2024-01-30","arxiv_id":"2401.16847","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-facial-forgery-detection","slug":"diffusion-facial-forgery-detection","title":"Diffusion Facial Forgery Detection","date":"2024-01-29","arxiv_id":"2401.15859","repositories_listed":1,"syntology":null},{"url":"/paper/multilingual-text-to-image-generation","slug":"multilingual-text-to-image-generation","title":"Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You","date":"2024-01-29","arxiv_id":"2401.16092","repositories_listed":1,"syntology":null},{"url":"/paper/gem-boost-simple-network-for-glass-surface","slug":"gem-boost-simple-network-for-glass-surface","title":"GEM: Boost Simple Network for Glass Surface Segmentation via Segment Anything Model and Data Synthesis","date":"2024-01-27","arxiv_id":"2401.15282","repositories_listed":1,"syntology":null},{"url":"/paper/annotated-hands-for-generative-models","slug":"annotated-hands-for-generative-models","title":"Annotated Hands for Generative Models","date":"2024-01-26","arxiv_id":"2401.15075","repositories_listed":1,"syntology":null},{"url":"/paper/taiyi-diffusion-xl-advancing-bilingual-text","slug":"taiyi-diffusion-xl-advancing-bilingual-text","title":"Taiyi-Diffusion-XL: Advancing Bilingual Text-to-Image Generation with Large Vision-Language Model Support","date":"2024-01-26","arxiv_id":"2401.14688","repositories_listed":1,"syntology":null},{"url":"/paper/bootpig-bootstrapping-zero-shot-personalized","slug":"bootpig-bootstrapping-zero-shot-personalized","title":"BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models","date":"2024-01-25","arxiv_id":"2401.13974","repositories_listed":1,"syntology":null},{"url":"/paper/creativesynth-creative-blending-and-synthesis","slug":"creativesynth-creative-blending-and-synthesis","title":"CreativeSynth: Cross-Art-Attention for Artistic Image Synthesis with Multimodal Diffusion","date":"2024-01-25","arxiv_id":"2401.14066","repositories_listed":1,"syntology":null},{"url":"/paper/deconstructing-denoising-diffusion-models-for","slug":"deconstructing-denoising-diffusion-models-for","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","date":"2024-01-25","arxiv_id":"2401.14404","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":2,"n_honours":4,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 4 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deconstructing-denoising-diffusion-models-for#ran","syntology_url":"https://syntology.ai/paper/2401.14404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.14404"}},"official":null}},{"url":"/paper/diffusion-enhancement-for-cloud-removal-in","slug":"diffusion-enhancement-for-cloud-removal-in","title":"Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery","date":"2024-01-25","arxiv_id":"2401.15105","repositories_listed":1,"syntology":null},{"url":"/paper/explicitly-representing-syntax-improves","slug":"explicitly-representing-syntax-improves","title":"Explicitly Representing Syntax Improves Sentence-to-layout Prediction of Unexpected Situations","date":"2024-01-25","arxiv_id":"2401.14212","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-manipulate-artistic-images","slug":"learning-to-manipulate-artistic-images","title":"Learning to Manipulate Artistic Images","date":"2024-01-25","arxiv_id":"2401.13976","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-to-manipulate-artistic-images#ran","syntology_url":"https://syntology.ai/paper/2401.13976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.13976"}},"official":{"repos":["snailforce/sim-net"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ddmi-domain-agnostic-latent-diffusion-models","slug":"ddmi-domain-agnostic-latent-diffusion-models","title":"DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations","date":"2024-01-23","arxiv_id":"2401.12517","repositories_listed":1,"syntology":{"n":21,"n_ran":18,"n_constructed":0,"n_ran_checked":12,"n_instrument":6,"n_unverified":3,"n_honours":0,"n_violates":2,"n_no_contract":10,"n_pointer_only":6,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 2 violated, 10 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ddmi-domain-agnostic-latent-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2401.12517","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.12517"}},"official":{"repos":["mlvlab/DDMI"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/mastering-text-to-image-diffusion","slug":"mastering-text-to-image-diffusion","title":"Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs","date":"2024-01-22","arxiv_id":"2401.11708","repositories_listed":1,"syntology":{"n":24,"n_ran":21,"n_constructed":0,"n_ran_checked":12,"n_instrument":9,"n_unverified":3,"n_honours":2,"n_violates":3,"n_no_contract":7,"n_pointer_only":16,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 3 violated, 7 with no contract checked; 9 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mastering-text-to-image-diffusion#ran","syntology_url":"https://syntology.ai/paper/2401.11708","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.11708"}},"official":{"repos":["yangling0818/rpg-diffusionmaster"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/scalable-high-resolution-pixel-space-image","slug":"scalable-high-resolution-pixel-space-image","title":"Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers","date":"2024-01-21","arxiv_id":"2401.11605","repositories_listed":1,"syntology":null},{"url":"/paper/compose-and-conquer-diffusion-based-3d-depth","slug":"compose-and-conquer-diffusion-based-3d-depth","title":"Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis","date":"2024-01-17","arxiv_id":"2401.09048","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":1,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 2 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/compose-and-conquer-diffusion-based-3d-depth#ran","syntology_url":"https://syntology.ai/paper/2401.09048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.09048"}},"official":{"repos":["tomtom1103/compose-and-conquer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mits-gan-safeguarding-medical-imaging-from","slug":"mits-gan-safeguarding-medical-imaging-from","title":"MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks","date":"2024-01-17","arxiv_id":"2401.09624","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-supervision-makes-layout-to-image","slug":"adversarial-supervision-makes-layout-to-image","title":"Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive","date":"2024-01-16","arxiv_id":"2401.08815","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/adversarial-supervision-makes-layout-to-image#ran","syntology_url":"https://syntology.ai/paper/2401.08815","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.08815"}},"official":{"repos":["boschresearch/aldm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/connect-collapse-corrupt-learning-cross-modal","slug":"connect-collapse-corrupt-learning-cross-modal","title":"Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data","date":"2024-01-16","arxiv_id":"2401.08567","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":5,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","sample_list":"/paper/connect-collapse-corrupt-learning-cross-modal#ran","syntology_url":"https://syntology.ai/paper/2401.08567","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.08567"}},"official":{"repos":["yuhui-zh15/c3"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-linear-array-pushbroom-image-restoration","slug":"deep-linear-array-pushbroom-image-restoration","title":"Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration Network","date":"2024-01-16","arxiv_id":"2401.08171","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/deep-linear-array-pushbroom-image-restoration#ran","syntology_url":"https://syntology.ai/paper/2401.08171","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.08171"}},"official":{"repos":["jhw2000/jarnet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-dynamic-3d-object-generation-from-a","slug":"fast-dynamic-3d-object-generation-from-a","title":"Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video","date":"2024-01-16","arxiv_id":"2401.08742","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fast-dynamic-3d-object-generation-from-a#ran","syntology_url":"https://syntology.ai/paper/2401.08742","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.08742"}},"official":{"repos":["fudan-zvg/efficient4d"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fixed-point-diffusion-models","slug":"fixed-point-diffusion-models","title":"Fixed Point Diffusion Models","date":"2024-01-16","arxiv_id":"2401.08741","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/fixed-point-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2401.08741","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.08741"}},"official":{"repos":["lukemelas/fixed-point-diffusion-models"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/revealing-vulnerabilities-in-stable-diffusion","slug":"revealing-vulnerabilities-in-stable-diffusion","title":"Revealing Vulnerabilities in Stable Diffusion via Targeted Attacks","date":"2024-01-16","arxiv_id":"2401.08725","repositories_listed":1,"syntology":null},{"url":"/paper/sit-exploring-flow-and-diffusion-based","slug":"sit-exploring-flow-and-diffusion-based","title":"SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers","date":"2024-01-16","arxiv_id":"2401.08740","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/sit-exploring-flow-and-diffusion-based#ran","syntology_url":"https://syntology.ai/paper/2401.08740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.08740"}},"official":{"repos":["willisma/sit"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/hierarchical-fashion-design-with-multi-stage","slug":"hierarchical-fashion-design-with-multi-stage","title":"HieraFashDiff: Hierarchical Fashion Design with Multi-stage Diffusion Models","date":"2024-01-15","arxiv_id":"2401.07450","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-denoising-diffusion-models","slug":"quantum-denoising-diffusion-models","title":"Quantum Denoising Diffusion Models","date":"2024-01-13","arxiv_id":"2401.07049","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-the-surface-a-global-scale-analysis-of","slug":"beyond-the-surface-a-global-scale-analysis-of","title":"ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation","date":"2024-01-12","arxiv_id":"2401.06310","repositories_listed":1,"syntology":null},{"url":"/paper/erasediff-erasing-data-influence-in-diffusion","slug":"erasediff-erasing-data-influence-in-diffusion","title":"Erasing Undesirable Influence in Diffusion Models","date":"2024-01-11","arxiv_id":"2401.05779","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":12,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/erasediff-erasing-data-influence-in-diffusion#ran","syntology_url":"https://syntology.ai/paper/2401.05779","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.05779"}},"official":{"repos":["jingwu321/erasediff"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/scissorhands-scrub-data-influence-via","slug":"scissorhands-scrub-data-influence-via","title":"Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks","date":"2024-01-11","arxiv_id":"2401.06187","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/scissorhands-scrub-data-influence-via#ran","syntology_url":"https://syntology.ai/paper/2401.06187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.06187"}},"official":{"repos":["jingwu321/scissorhands"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/pixart-d-fast-and-controllable-image","slug":"pixart-d-fast-and-controllable-image","title":"PIXART-δ: Fast and Controllable Image Generation with Latent Consistency Models","date":"2024-01-10","arxiv_id":"2401.05252","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pixart-d-fast-and-controllable-image#ran","syntology_url":"https://syntology.ai/paper/2401.05252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.05252"}},"official":{"repos":["PixArt-alpha/PixArt-alpha"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhanced-distribution-alignment-for-post","slug":"enhanced-distribution-alignment-for-post","title":"EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models","date":"2024-01-09","arxiv_id":"2401.04585","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/enhanced-distribution-alignment-for-post#ran","syntology_url":"https://syntology.ai/paper/2401.04585","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.04585"}},"official":{"repos":["BienLuky/EDA-DM"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/plug-in-diffusion-model-for-sequential","slug":"plug-in-diffusion-model-for-sequential","title":"Plug-in Diffusion Model for Sequential Recommendation","date":"2024-01-05","arxiv_id":"2401.02913","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":1,"n_ran_checked":10,"n_instrument":1,"n_unverified":1,"n_honours":3,"n_violates":1,"n_no_contract":6,"n_pointer_only":12,"phrase":"11 ran (of which 1 constructed an object rather than computing a result; 10 with no instrument failure: 3 honoured, 1 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/plug-in-diffusion-model-for-sequential#ran","syntology_url":"https://syntology.ai/paper/2401.02913","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.02913"}},"official":{"repos":["hulkima/pdrec"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":1,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-dataset-and-benchmark-for-copyright","slug":"a-dataset-and-benchmark-for-copyright","title":"A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models","date":"2024-01-04","arxiv_id":"2403.12052","repositories_listed":1,"syntology":null},{"url":"/paper/amused-an-open-muse-reproduction","slug":"amused-an-open-muse-reproduction","title":"aMUSEd: An Open MUSE Reproduction","date":"2024-01-03","arxiv_id":"2401.01808","repositories_listed":1,"syntology":null},{"url":"/paper/dig-in-diffusion-guidance-for-investigating","slug":"dig-in-diffusion-guidance-for-investigating","title":"DiG-IN: Diffusion Guidance for Investigating Networks - Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual Explanations","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/exact-fusion-via-feature-distribution","slug":"exact-fusion-via-feature-distribution","title":"Exact Fusion via Feature Distribution Matching for Few-shot Image Generation","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generating-handwritten-mathematical","slug":"generating-handwritten-mathematical","title":"Generating Handwritten Mathematical Expressions From Symbol Graphs: An End-to-End Pipeline","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/intelligent-grimm-open-ended-visual-1","slug":"intelligent-grimm-open-ended-visual-1","title":"Intelligent Grimm - Open-ended Visual Storytelling via Latent Diffusion Models","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/new-job-new-gender-measuring-the-social-bias","slug":"new-job-new-gender-measuring-the-social-bias","title":"New Job, New Gender? Measuring the Social Bias in Image Generation Models","date":"2024-01-01","arxiv_id":"2401.00763","repositories_listed":1,"syntology":null},{"url":"/paper/seed-bench-benchmarking-multimodal-large","slug":"seed-bench-benchmarking-multimodal-large","title":"SEED-Bench: Benchmarking Multimodal Large Language Models","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/textnerf-a-novel-scene-text-image-synthesis","slug":"textnerf-a-novel-scene-text-image-synthesis","title":"TextNeRF: A Novel Scene-Text Image Synthesis Method based on Neural Radiance Fields","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/vkd-improving-knowledge-distillation-using","slug":"vkd-improving-knowledge-distillation-using","title":"VkD: Improving Knowledge Distillation using Orthogonal Projections","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/when-stylegan-meets-stable-diffusion-a-w","slug":"when-stylegan-meets-stable-diffusion-a-w","title":"When StyleGAN Meets Stable Diffusion: a W+ Adapter for Personalized Image Generation","date":"2024-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generative-model-driven-synthetic-training","slug":"generative-model-driven-synthetic-training","title":"Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Rail Defect Detection","date":"2023-12-31","arxiv_id":"2401.00393","repositories_listed":1,"syntology":null},{"url":"/paper/unified-io-2-scaling-autoregressive","slug":"unified-io-2-scaling-autoregressive","title":"Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action","date":"2023-12-28","arxiv_id":"2312.17172","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unified-io-2-scaling-autoregressive#ran","syntology_url":"https://syntology.ai/paper/2312.17172","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.17172"}},"official":{"repos":["allenai/unified-io-2"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/zone-zero-shot-instruction-guided-local","slug":"zone-zero-shot-instruction-guided-local","title":"ZONE: Zero-Shot Instruction-Guided Local Editing","date":"2023-12-28","arxiv_id":"2312.16794","repositories_listed":1,"syntology":null},{"url":"/paper/bellman-optimal-step-size-straightening-of","slug":"bellman-optimal-step-size-straightening-of","title":"Bellman Optimal Stepsize Straightening of Flow-Matching Models","date":"2023-12-27","arxiv_id":"2312.16414","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":8,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":14,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/bellman-optimal-step-size-straightening-of#ran","syntology_url":"https://syntology.ai/paper/2312.16414","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.16414"}},"official":{"repos":["nguyenngocbaocmt02/boss"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-initialization-for-personalized-text-to","slug":"cross-initialization-for-personalized-text-to","title":"Cross Initialization for Personalized Text-to-Image Generation","date":"2023-12-26","arxiv_id":"2312.15905","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cross-initialization-for-personalized-text-to#ran","syntology_url":"https://syntology.ai/paper/2312.15905","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.15905"}},"official":{"repos":["lyupang/crossinitialization"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/one-dimensional-adapter-to-rule-them-all","slug":"one-dimensional-adapter-to-rule-them-all","title":"One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications","date":"2023-12-26","arxiv_id":"2312.16145","repositories_listed":1,"syntology":null},{"url":"/paper/ssr-encoder-encoding-selective-subject","slug":"ssr-encoder-encoding-selective-subject","title":"SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation","date":"2023-12-26","arxiv_id":"2312.16272","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ssr-encoder-encoding-selective-subject#ran","syntology_url":"https://syntology.ai/paper/2312.16272","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.16272"}},"official":{"repos":["Xiaojiu-z/SSR_Encoder"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-recipe-for-scaling-up-text-to-video","slug":"a-recipe-for-scaling-up-text-to-video","title":"A Recipe for Scaling up Text-to-Video Generation with Text-free Videos","date":"2023-12-25","arxiv_id":"2312.15770","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-prompt-relabeling-for-diffusion","slug":"iterative-prompt-relabeling-for-diffusion","title":"Learning from Mistakes: Iterative Prompt Relabeling for Text-to-Image Diffusion Model Training","date":"2023-12-23","arxiv_id":"2312.16204","repositories_listed":1,"syntology":null}],"record_sha256":"be766a8d117aa813662cf1c5745c73068059c92a9e3efb87da1db78be84aefff","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}