{"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/18","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":18,"pages_in_order":67,"rows_per_page":100,"rows":[1701,1800],"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/17","next":"/task/image-generation/papers/19","papers":[{"url":"/paper/wavelet-packet-power-spectrum-kullback","slug":"wavelet-packet-power-spectrum-kullback","title":"Fréchet Wavelet Distance: A Domain-Agnostic Metric for Image Generation","date":"2023-12-23","arxiv_id":"2312.15289","repositories_listed":1,"syntology":null},{"url":"/paper/asymmetric-bias-in-text-to-image-generation","slug":"asymmetric-bias-in-text-to-image-generation","title":"Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks","date":"2023-12-22","arxiv_id":"2312.14440","repositories_listed":1,"syntology":null},{"url":"/paper/viescore-towards-explainable-metrics-for","slug":"viescore-towards-explainable-metrics-for","title":"VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation","date":"2023-12-22","arxiv_id":"2312.14867","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/viescore-towards-explainable-metrics-for#ran","syntology_url":"https://syntology.ai/paper/2312.14867","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14867"}},"official":{"repos":["TIGER-AI-Lab/VIEScore"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-diffusion-based-counterfactuals-for","slug":"fast-diffusion-based-counterfactuals-for","title":"Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation","date":"2023-12-21","arxiv_id":"2312.14223","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":10,"phrase":"7 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fast-diffusion-based-counterfactuals-for#ran","syntology_url":"https://syntology.ai/paper/2312.14223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14223"}},"official":{"repos":["nina-weng/fastdime_med"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fine-grained-forecasting-models-via-gaussian","slug":"fine-grained-forecasting-models-via-gaussian","title":"Fine-grained Forecasting Models Via Gaussian Process Blurring Effect","date":"2023-12-21","arxiv_id":"2312.14280","repositories_listed":1,"syntology":null},{"url":"/paper/vcoder-versatile-vision-encoders-for","slug":"vcoder-versatile-vision-encoders-for","title":"VCoder: Versatile Vision Encoders for Multimodal Large Language Models","date":"2023-12-21","arxiv_id":"2312.14233","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":1,"phrase":"6 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/vcoder-versatile-vision-encoders-for#ran","syntology_url":"https://syntology.ai/paper/2312.14233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14233"}},"official":{"repos":["shi-labs/vcoder"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-models-with-learned-adaptive-noise","slug":"diffusion-models-with-learned-adaptive-noise","title":"Diffusion Models With Learned Adaptive Noise","date":"2023-12-20","arxiv_id":"2312.13236","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":9,"n_pointer_only":3,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 1 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/diffusion-models-with-learned-adaptive-noise#ran","syntology_url":"https://syntology.ai/paper/2312.13236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.13236"}},"official":{"repos":["s-sahoo/mulan"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/repaint123-fast-and-high-quality-one-image-to","slug":"repaint123-fast-and-high-quality-one-image-to","title":"Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting","date":"2023-12-20","arxiv_id":"2312.13271","repositories_listed":1,"syntology":{"n":18,"n_ran":16,"n_constructed":0,"n_ran_checked":11,"n_instrument":5,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":4,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/repaint123-fast-and-high-quality-one-image-to#ran","syntology_url":"https://syntology.ai/paper/2312.13271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.13271"}},"official":{"repos":["junwuzhang19/repaint123"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/skyscript-a-large-and-semantically-diverse","slug":"skyscript-a-large-and-semantically-diverse","title":"SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing","date":"2023-12-20","arxiv_id":"2312.12856","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/skyscript-a-large-and-semantically-diverse#ran","syntology_url":"https://syntology.ai/paper/2312.12856","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12856"}},"official":{"repos":["wangzhecheng/skyscript"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/brush-your-text-synthesize-any-scene-text-on","slug":"brush-your-text-synthesize-any-scene-text-on","title":"Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model","date":"2023-12-19","arxiv_id":"2312.12232","repositories_listed":1,"syntology":{"n":19,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":19,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/brush-your-text-synthesize-any-scene-text-on#ran","syntology_url":"https://syntology.ai/paper/2312.12232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12232"}},"official":{"repos":["ecnuljzhang/brush-your-text"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/decoupled-textual-embeddings-for-customized","slug":"decoupled-textual-embeddings-for-customized","title":"Decoupled Textual Embeddings for Customized Image Generation","date":"2023-12-19","arxiv_id":"2312.11826","repositories_listed":1,"syntology":null},{"url":"/paper/streamdiffusion-a-pipeline-level-solution-for","slug":"streamdiffusion-a-pipeline-level-solution-for","title":"StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation","date":"2023-12-19","arxiv_id":"2312.12491","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":9,"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) · 1 unverified","sample_list":"/paper/streamdiffusion-a-pipeline-level-solution-for#ran","syntology_url":"https://syntology.ai/paper/2312.12491","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12491"}},"official":{"repos":["cumulo-autumn/streamdiffusion"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adv-diffusion-imperceptible-adversarial-face","slug":"adv-diffusion-imperceptible-adversarial-face","title":"Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent Diffusion Model","date":"2023-12-18","arxiv_id":"2312.11285","repositories_listed":1,"syntology":null},{"url":"/paper/your-student-is-better-than-expected-adaptive","slug":"your-student-is-better-than-expected-adaptive","title":"Your Student is Better Than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion Models","date":"2023-12-17","arxiv_id":"2312.10835","repositories_listed":1,"syntology":null},{"url":"/paper/deepcallifont-few-shot-chinese-calligraphy","slug":"deepcallifont-few-shot-chinese-calligraphy","title":"DeepCalliFont: Few-shot Chinese Calligraphy Font Synthesis by Integrating Dual-modality Generative Models","date":"2023-12-16","arxiv_id":"2312.10314","repositories_listed":1,"syntology":null},{"url":"/paper/faster-diffusion-rethinking-the-role-of-unet","slug":"faster-diffusion-rethinking-the-role-of-unet","title":"Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference","date":"2023-12-15","arxiv_id":"2312.09608","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/faster-diffusion-rethinking-the-role-of-unet#ran","syntology_url":"https://syntology.ai/paper/2312.09608","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09608"}},"official":{"repos":["hutaihang/faster-diffusion"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/topic-vq-vae-leveraging-latent-codebooks-for","slug":"topic-vq-vae-leveraging-latent-codebooks-for","title":"Topic-VQ-VAE: Leveraging Latent Codebooks for Flexible Topic-Guided Document Generation","date":"2023-12-15","arxiv_id":"2312.11532","repositories_listed":1,"syntology":null},{"url":"/paper/3dgs-avatar-animatable-avatars-via-deformable","slug":"3dgs-avatar-animatable-avatars-via-deformable","title":"3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting","date":"2023-12-14","arxiv_id":"2312.09228","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/3dgs-avatar-animatable-avatars-via-deformable#ran","syntology_url":"https://syntology.ai/paper/2312.09228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09228"}},"official":null}},{"url":"/paper/archiguesser-ai-art-architecture-educational","slug":"archiguesser-ai-art-architecture-educational","title":"ArchiGuesser -- AI Art Architecture Educational Game","date":"2023-12-14","arxiv_id":"2312.09334","repositories_listed":1,"syntology":null},{"url":"/paper/color-agnostic-cross-spectral-disparity","slug":"color-agnostic-cross-spectral-disparity","title":"Color Agnostic Cross-Spectral Disparity Estimation","date":"2023-12-14","arxiv_id":"2312.08946","repositories_listed":1,"syntology":null},{"url":"/paper/fast-sampling-via-de-randomization-for","slug":"fast-sampling-via-de-randomization-for","title":"Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time","date":"2023-12-14","arxiv_id":"2312.09193","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":2,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fast-sampling-via-de-randomization-for#ran","syntology_url":"https://syntology.ai/paper/2312.09193","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09193"}},"official":{"repos":["uclaml/dndm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/vl-gpt-a-generative-pre-trained-transformer","slug":"vl-gpt-a-generative-pre-trained-transformer","title":"VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation","date":"2023-12-14","arxiv_id":"2312.09251","repositories_listed":1,"syntology":null},{"url":"/paper/adapedit-spatio-temporal-guided-adaptive","slug":"adapedit-spatio-temporal-guided-adaptive","title":"AdapEdit: Spatio-Temporal Guided Adaptive Editing Algorithm for Text-Based Continuity-Sensitive Image Editing","date":"2023-12-13","arxiv_id":"2312.08019","repositories_listed":1,"syntology":null},{"url":"/paper/clockwork-diffusion-efficient-generation-with","slug":"clockwork-diffusion-efficient-generation-with","title":"Clockwork Diffusion: Efficient Generation With Model-Step Distillation","date":"2023-12-13","arxiv_id":"2312.08128","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-based-blind-text-image-super","slug":"diffusion-based-blind-text-image-super","title":"Diffusion-based Blind Text Image Super-Resolution","date":"2023-12-13","arxiv_id":"2312.08886","repositories_listed":1,"syntology":null},{"url":"/paper/r-diffusion-a-diffusion-based-density","slug":"r-diffusion-a-diffusion-based-density","title":"$ρ$-Diffusion: A diffusion-based density estimation framework for computational physics","date":"2023-12-13","arxiv_id":"2312.08153","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":0,"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/r-diffusion-a-diffusion-based-density#ran","syntology_url":"https://syntology.ai/paper/2312.08153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08153"}},"official":{"repos":["intel/rho-diffusion"],"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/semantic-aware-data-augmentation-for-text-to","slug":"semantic-aware-data-augmentation-for-text-to","title":"Semantic-aware Data Augmentation for Text-to-image Synthesis","date":"2023-12-13","arxiv_id":"2312.07951","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-driven-initial-image-construction","slug":"semantic-driven-initial-image-construction","title":"The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization","date":"2023-12-13","arxiv_id":"2312.08872","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":7,"n_instrument":6,"n_unverified":1,"n_honours":2,"n_violates":3,"n_no_contract":2,"n_pointer_only":14,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 3 violated, 2 with no contract checked; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/semantic-driven-initial-image-construction#ran","syntology_url":"https://syntology.ai/paper/2312.08872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08872"}},"official":{"repos":["UT-Mao/Initial-Noise-Construction"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/simac-a-simple-anti-customization-method","slug":"simac-a-simple-anti-customization-method","title":"SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion Models","date":"2023-12-13","arxiv_id":"2312.07865","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"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) · 4 unverified","sample_list":"/paper/simac-a-simple-anti-customization-method#ran","syntology_url":"https://syntology.ai/paper/2312.07865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07865"}},"official":{"repos":["somuchtome/simac"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/diffmorpher-unleashing-the-capability-of","slug":"diffmorpher-unleashing-the-capability-of","title":"DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing","date":"2023-12-12","arxiv_id":"2312.07409","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-cocktail-fused-generation-from","slug":"diffusion-cocktail-fused-generation-from","title":"Diffusion Cocktail: Mixing Domain-Specific Diffusion Models for Diversified Image Generations","date":"2023-12-12","arxiv_id":"2312.08873","repositories_listed":1,"syntology":null},{"url":"/paper/divide-and-conquer-attack-harnessing-the","slug":"divide-and-conquer-attack-harnessing-the","title":"Harnessing LLM to Attack LLM-Guarded Text-to-Image Models","date":"2023-12-12","arxiv_id":"2312.07130","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/divide-and-conquer-attack-harnessing-the#ran","syntology_url":"https://syntology.ai/paper/2312.07130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07130"}},"official":{"repos":["researchcode001/divide-and-conquer-attack"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/how-well-does-gpt-4v-ision-adapt-to","slug":"how-well-does-gpt-4v-ision-adapt-to","title":"How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation","date":"2023-12-12","arxiv_id":"2312.07424","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/how-well-does-gpt-4v-ision-adapt-to#ran","syntology_url":"https://syntology.ai/paper/2312.07424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07424"}},"official":{"repos":["jameszhou-gl/gpt-4v-distribution-shift"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/image-content-generation-with-causal","slug":"image-content-generation-with-causal","title":"Image Content Generation with Causal Reasoning","date":"2023-12-12","arxiv_id":"2312.07132","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/image-content-generation-with-causal#ran","syntology_url":"https://syntology.ai/paper/2312.07132","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07132"}},"official":{"repos":["ieit-agi/mix-shannon"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/learned-representation-guided-diffusion","slug":"learned-representation-guided-diffusion","title":"Learned representation-guided diffusion models for large-image generation","date":"2023-12-12","arxiv_id":"2312.07330","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":10,"n_pointer_only":14,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 1 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learned-representation-guided-diffusion#ran","syntology_url":"https://syntology.ai/paper/2312.07330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07330"}},"official":{"repos":["cvlab-stonybrook/large-image-diffusion"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/characteristic-guidance-non-linear-correction","slug":"characteristic-guidance-non-linear-correction","title":"Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scale","date":"2023-12-11","arxiv_id":"2312.07586","repositories_listed":1,"syntology":null},{"url":"/paper/compensation-sampling-for-improved","slug":"compensation-sampling-for-improved","title":"Compensation Sampling for Improved Convergence in Diffusion Models","date":"2023-12-11","arxiv_id":"2312.06285","repositories_listed":1,"syntology":null},{"url":"/paper/controlnet-xs-designing-an-efficient-and","slug":"controlnet-xs-designing-an-efficient-and","title":"ControlNet-XS: Rethinking the Control of Text-to-Image Diffusion Models as Feedback-Control Systems","date":"2023-12-11","arxiv_id":"2312.06573","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":3,"n_no_contract":6,"n_pointer_only":4,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 3 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/controlnet-xs-designing-an-efficient-and#ran","syntology_url":"https://syntology.ai/paper/2312.06573","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06573"}},"official":{"repos":["vislearn/ControlNet-XS"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/uiedp-underwater-image-enhancement-with","slug":"uiedp-underwater-image-enhancement-with","title":"UIEDP:Underwater Image Enhancement with Diffusion Prior","date":"2023-12-11","arxiv_id":"2312.06240","repositories_listed":1,"syntology":null},{"url":"/paper/anomalydiffusion-few-shot-anomaly-image","slug":"anomalydiffusion-few-shot-anomaly-image","title":"AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model","date":"2023-12-10","arxiv_id":"2312.05767","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":6,"n_pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/anomalydiffusion-few-shot-anomaly-image#ran","syntology_url":"https://syntology.ai/paper/2312.05767","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.05767"}},"official":{"repos":["sjtuplayer/anomalydiffusion"],"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/correcting-diffusion-generation-through","slug":"correcting-diffusion-generation-through","title":"Correcting Diffusion Generation through Resampling","date":"2023-12-10","arxiv_id":"2312.06038","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/correcting-diffusion-generation-through#ran","syntology_url":"https://syntology.ai/paper/2312.06038","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06038"}},"official":{"repos":["ucsb-nlp-chang/diffusion_resampling"],"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/synthesizing-traffic-datasets-using-graph","slug":"synthesizing-traffic-datasets-using-graph","title":"Synthesizing Traffic Datasets using Graph Neural Networks","date":"2023-12-08","arxiv_id":"2312.05031","repositories_listed":1,"syntology":null},{"url":"/paper/udifftext-a-unified-framework-for-high","slug":"udifftext-a-unified-framework-for-high","title":"UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models","date":"2023-12-08","arxiv_id":"2312.04884","repositories_listed":1,"syntology":null},{"url":"/paper/dreamvideo-composing-your-dream-videos-with","slug":"dreamvideo-composing-your-dream-videos-with","title":"DreamVideo: Composing Your Dream Videos with Customized Subject and Motion","date":"2023-12-07","arxiv_id":"2312.04433","repositories_listed":1,"syntology":null},{"url":"/paper/forensic-iris-image-synthesis","slug":"forensic-iris-image-synthesis","title":"Forensic Iris Image Synthesis","date":"2023-12-07","arxiv_id":"2312.04125","repositories_listed":1,"syntology":null},{"url":"/paper/generating-illustrated-instructions","slug":"generating-illustrated-instructions","title":"Generating Illustrated Instructions","date":"2023-12-07","arxiv_id":"2312.04552","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"5 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/generating-illustrated-instructions#ran","syntology_url":"https://syntology.ai/paper/2312.04552","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.04552"}},"official":{"repos":["sachit-menon/generating-illustrated-instructions-reproduction"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/investigating-the-design-space-of-diffusion","slug":"investigating-the-design-space-of-diffusion","title":"Investigating the Design Space of Diffusion Models for Speech Enhancement","date":"2023-12-07","arxiv_id":"2312.04370","repositories_listed":1,"syntology":null},{"url":"/paper/photomaker-customizing-realistic-human-photos","slug":"photomaker-customizing-realistic-human-photos","title":"PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding","date":"2023-12-07","arxiv_id":"2312.04461","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/photomaker-customizing-realistic-human-photos#ran","syntology_url":"https://syntology.ai/paper/2312.04461","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.04461"}},"official":{"repos":["TencentARC/PhotoMaker"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/data-driven-crop-growth-simulation-on-time","slug":"data-driven-crop-growth-simulation-on-time","title":"Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks","date":"2023-12-06","arxiv_id":"2312.03443","repositories_listed":1,"syntology":null},{"url":"/paper/diffusionsat-a-generative-foundation-model","slug":"diffusionsat-a-generative-foundation-model","title":"DiffusionSat: A Generative Foundation Model for Satellite Imagery","date":"2023-12-06","arxiv_id":"2312.03606","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"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, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/diffusionsat-a-generative-foundation-model#ran","syntology_url":"https://syntology.ai/paper/2312.03606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.03606"}},"official":null}},{"url":"/paper/kandinsky-3-0-technical-report","slug":"kandinsky-3-0-technical-report","title":"Kandinsky 3.0 Technical Report","date":"2023-12-06","arxiv_id":"2312.03511","repositories_listed":1,"syntology":{"n":14,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 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; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/kandinsky-3-0-technical-report#ran","syntology_url":"https://syntology.ai/paper/2312.03511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.03511"}},"official":{"repos":["ai-forever/kandinsky-3"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/self-conditioned-image-generation-via","slug":"self-conditioned-image-generation-via","title":"Return of Unconditional Generation: A Self-supervised Representation Generation Method","date":"2023-12-06","arxiv_id":"2312.03701","repositories_listed":1,"syntology":{"n":18,"n_ran":12,"n_constructed":7,"n_ran_checked":9,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"12 ran (of which 7 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/self-conditioned-image-generation-via#ran","syntology_url":"https://syntology.ai/paper/2312.03701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.03701"}},"official":{"repos":["LTH14/rcg"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":7,"n_ran_no_instrument_failure":9,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/tokencompose-grounding-diffusion-with-token","slug":"tokencompose-grounding-diffusion-with-token","title":"TokenCompose: Text-to-Image Diffusion with Token-level Supervision","date":"2023-12-06","arxiv_id":"2312.03626","repositories_listed":1,"syntology":null},{"url":"/paper/bivdiff-a-training-free-framework-for-general","slug":"bivdiff-a-training-free-framework-for-general","title":"BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models","date":"2023-12-05","arxiv_id":"2312.02813","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/bivdiff-a-training-free-framework-for-general#ran","syntology_url":"https://syntology.ai/paper/2312.02813","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02813"}},"official":{"repos":["mcg-nju/bivdiff"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/customization-assistant-for-text-to-image","slug":"customization-assistant-for-text-to-image","title":"Customization Assistant for Text-to-image Generation","date":"2023-12-05","arxiv_id":"2312.03045","repositories_listed":1,"syntology":null},{"url":"/paper/diversified-in-domain-synthesis-with","slug":"diversified-in-domain-synthesis-with","title":"Diversified in-domain synthesis with efficient fine-tuning for few-shot classification","date":"2023-12-05","arxiv_id":"2312.03046","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/diversified-in-domain-synthesis-with#ran","syntology_url":"https://syntology.ai/paper/2312.03046","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.03046"}},"official":{"repos":["vturrisi/disef"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fergi-automatic-annotation-of-user","slug":"fergi-automatic-annotation-of-user","title":"FERGI: Automatic Scoring of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reaction","date":"2023-12-05","arxiv_id":"2312.03187","repositories_listed":1,"syntology":null},{"url":"/paper/genie-generative-hard-negative-images-through","slug":"genie-generative-hard-negative-images-through","title":"GeNIe: Generative Hard Negative Images Through Diffusion","date":"2023-12-05","arxiv_id":"2312.02548","repositories_listed":1,"syntology":null},{"url":"/paper/gpt4point-a-unified-framework-for-point","slug":"gpt4point-a-unified-framework-for-point","title":"GPT4Point: A Unified Framework for Point-Language Understanding and Generation","date":"2023-12-05","arxiv_id":"2312.02980","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":1,"n_no_contract":2,"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, 1 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/gpt4point-a-unified-framework-for-point#ran","syntology_url":"https://syntology.ai/paper/2312.02980","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02980"}},"official":null}},{"url":"/paper/visconet-bridging-and-harmonizing-visual-and","slug":"visconet-bridging-and-harmonizing-visual-and","title":"ViscoNet: Bridging and Harmonizing Visual and Textual Conditioning for ControlNet","date":"2023-12-05","arxiv_id":"2312.03154","repositories_listed":1,"syntology":null},{"url":"/paper/a-contrastive-compositional-benchmark-for","slug":"a-contrastive-compositional-benchmark-for","title":"A Contrastive Compositional Benchmark for Text-to-Image Synthesis: A Study with Unified Text-to-Image Fidelity Metrics","date":"2023-12-04","arxiv_id":"2312.02338","repositories_listed":1,"syntology":null},{"url":"/paper/diffit-diffusion-vision-transformers-for","slug":"diffit-diffusion-vision-transformers-for","title":"DiffiT: Diffusion Vision Transformers for Image Generation","date":"2023-12-04","arxiv_id":"2312.02139","repositories_listed":1,"syntology":{"n":25,"n_ran":20,"n_constructed":0,"n_ran_checked":13,"n_instrument":7,"n_unverified":5,"n_honours":3,"n_violates":1,"n_no_contract":9,"n_pointer_only":25,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 3 honoured, 1 violated, 9 with no contract checked; 7 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/diffit-diffusion-vision-transformers-for#ran","syntology_url":"https://syntology.ai/paper/2312.02139","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02139"}},"official":{"repos":["nvlabs/diffit"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/fully-spiking-denoising-diffusion-implicit","slug":"fully-spiking-denoising-diffusion-implicit","title":"Fully Spiking Denoising Diffusion Implicit Models","date":"2023-12-04","arxiv_id":"2312.01742","repositories_listed":1,"syntology":null},{"url":"/paper/unigs-unified-representation-for-image","slug":"unigs-unified-representation-for-image","title":"UniGS: Unified Representation for Image Generation and Segmentation","date":"2023-12-04","arxiv_id":"2312.01985","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/unigs-unified-representation-for-image#ran","syntology_url":"https://syntology.ai/paper/2312.01985","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01985"}},"official":{"repos":["qqlu/entity"],"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/meta-controlnet-enhancing-task-adaptation-via","slug":"meta-controlnet-enhancing-task-adaptation-via","title":"Meta ControlNet: Enhancing Task Adaptation via Meta Learning","date":"2023-12-03","arxiv_id":"2312.01255","repositories_listed":1,"syntology":null},{"url":"/paper/generating-images-of-the-m87-black-hole-using","slug":"generating-images-of-the-m87-black-hole-using","title":"Generating Images of the M87* Black Hole Using GANs","date":"2023-12-02","arxiv_id":"2312.01005","repositories_listed":1,"syntology":null},{"url":"/paper/ultra-resolution-cascaded-diffusion-model-for","slug":"ultra-resolution-cascaded-diffusion-model-for","title":"Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology","date":"2023-12-02","arxiv_id":"2312.01152","repositories_listed":1,"syntology":null},{"url":"/paper/cat-dm-controllable-accelerated-virtual-try","slug":"cat-dm-controllable-accelerated-virtual-try","title":"CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model","date":"2023-11-30","arxiv_id":"2311.18405","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":3,"n_no_contract":2,"n_pointer_only":12,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 3 violated, 2 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/cat-dm-controllable-accelerated-virtual-try#ran","syntology_url":"https://syntology.ai/paper/2311.18405","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.18405"}},"official":{"repos":["zengjianhao/cat-dm"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dfu-scale-robust-diffusion-model-for-zero","slug":"dfu-scale-robust-diffusion-model-for-zero","title":"DFU: scale-robust diffusion model for zero-shot super-resolution image generation","date":"2023-11-30","arxiv_id":"2401.06144","repositories_listed":1,"syntology":null},{"url":"/paper/elasticdiffusion-training-free-arbitrary-size","slug":"elasticdiffusion-training-free-arbitrary-size","title":"ElasticDiffusion: Training-free Arbitrary Size Image Generation through Global-Local Content Separation","date":"2023-11-30","arxiv_id":"2311.18822","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/elasticdiffusion-training-free-arbitrary-size#ran","syntology_url":"https://syntology.ai/paper/2311.18822","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.18822"}},"official":{"repos":["moayedhajiali/elasticdiffusion-official"],"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/layered-rendering-diffusion-model-for-zero","slug":"layered-rendering-diffusion-model-for-zero","title":"Layered Rendering Diffusion Model for Controllable Zero-Shot Image Synthesis","date":"2023-11-30","arxiv_id":"2311.18435","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-and-explaining-image-classifiers","slug":"analyzing-and-explaining-image-classifiers","title":"DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual Explanations","date":"2023-11-29","arxiv_id":"2311.17833","repositories_listed":1,"syntology":null},{"url":"/paper/chatillusion-efficient-aligning-interleaved","slug":"chatillusion-efficient-aligning-interleaved","title":"M$^{2}$Chat: Empowering VLM for Multimodal LLM Interleaved Text-Image Generation","date":"2023-11-29","arxiv_id":"2311.17963","repositories_listed":1,"syntology":null},{"url":"/paper/soda-bottleneck-diffusion-models-for","slug":"soda-bottleneck-diffusion-models-for","title":"SODA: Bottleneck Diffusion Models for Representation Learning","date":"2023-11-29","arxiv_id":"2311.17901","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/soda-bottleneck-diffusion-models-for#ran","syntology_url":"https://syntology.ai/paper/2311.17901","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17901"}},"official":null}},{"url":"/paper/vbench-comprehensive-benchmark-suite-for","slug":"vbench-comprehensive-benchmark-suite-for","title":"VBench: Comprehensive Benchmark Suite for Video Generative Models","date":"2023-11-29","arxiv_id":"2311.17982","repositories_listed":1,"syntology":null},{"url":"/paper/when-stylegan-meets-stable-diffusion-a","slug":"when-stylegan-meets-stable-diffusion-a","title":"When StyleGAN Meets Stable Diffusion: a $\\mathscr{W}_+$ Adapter for Personalized Image Generation","date":"2023-11-29","arxiv_id":"2311.17461","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-scene-text-detectors-with-realistic","slug":"enhancing-scene-text-detectors-with-realistic","title":"Enhancing Scene Text Detectors with Realistic Text Image Synthesis Using Diffusion Models","date":"2023-11-28","arxiv_id":"2311.16555","repositories_listed":1,"syntology":null},{"url":"/paper/federated-learning-with-diffusion-models-for","slug":"federated-learning-with-diffusion-models-for","title":"Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks","date":"2023-11-28","arxiv_id":"2311.16538","repositories_listed":1,"syntology":null},{"url":"/paper/image-inpainting-via-tractable-steering-of","slug":"image-inpainting-via-tractable-steering-of","title":"Image Inpainting via Tractable Steering of Diffusion Models","date":"2023-11-28","arxiv_id":"2401.03349","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/image-inpainting-via-tractable-steering-of#ran","syntology_url":"https://syntology.ai/paper/2401.03349","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.03349"}},"official":{"repos":["ucla-starai/tiramisu"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pea-diffusion-parameter-efficient-adapter","slug":"pea-diffusion-parameter-efficient-adapter","title":"PEA-Diffusion: Parameter-Efficient Adapter with Knowledge Distillation in non-English Text-to-Image Generation","date":"2023-11-28","arxiv_id":"2311.17086","repositories_listed":1,"syntology":null},{"url":"/paper/reason-out-your-layout-evoking-the-layout","slug":"reason-out-your-layout-evoking-the-layout","title":"Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis","date":"2023-11-28","arxiv_id":"2311.17126","repositories_listed":1,"syntology":null},{"url":"/paper/robust-diffusion-gan-using-semi-unbalanced","slug":"robust-diffusion-gan-using-semi-unbalanced","title":"A High-Quality Robust Diffusion Framework for Corrupted Dataset","date":"2023-11-28","arxiv_id":"2311.17101","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":5,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":5,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/robust-diffusion-gan-using-semi-unbalanced#ran","syntology_url":"https://syntology.ai/paper/2311.17101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17101"}},"official":{"repos":["VinAIResearch/RDUOT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/self-discovering-interpretable-diffusion","slug":"self-discovering-interpretable-diffusion","title":"Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation","date":"2023-11-28","arxiv_id":"2311.17216","repositories_listed":1,"syntology":null},{"url":"/paper/text-driven-image-editing-via-learnable","slug":"text-driven-image-editing-via-learnable","title":"Text-Driven Image Editing via Learnable Regions","date":"2023-11-28","arxiv_id":"2311.16432","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/text-driven-image-editing-via-learnable#ran","syntology_url":"https://syntology.ai/paper/2311.16432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.16432"}},"official":{"repos":["yuanze-lin/Learnable_Regions"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/llmga-multimodal-large-language-model-based","slug":"llmga-multimodal-large-language-model-based","title":"LLMGA: Multimodal Large Language Model based Generation Assistant","date":"2023-11-27","arxiv_id":"2311.16500","repositories_listed":1,"syntology":null},{"url":"/paper/removing-nsfw-concepts-from-vision-and","slug":"removing-nsfw-concepts-from-vision-and","title":"Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models","date":"2023-11-27","arxiv_id":"2311.16254","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"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) · 3 unverified","sample_list":"/paper/removing-nsfw-concepts-from-vision-and#ran","syntology_url":"https://syntology.ai/paper/2311.16254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.16254"}},"official":{"repos":["aimagelab/safe-clip"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/self-correcting-llm-controlled-diffusion","slug":"self-correcting-llm-controlled-diffusion","title":"Self-correcting LLM-controlled Diffusion Models","date":"2023-11-27","arxiv_id":"2311.16090","repositories_listed":1,"syntology":null},{"url":"/paper/street-tryon-learning-in-the-wild-virtual-try","slug":"street-tryon-learning-in-the-wild-virtual-try","title":"Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images","date":"2023-11-27","arxiv_id":"2311.16094","repositories_listed":1,"syntology":null},{"url":"/paper/tfmq-dm-temporal-feature-maintenance","slug":"tfmq-dm-temporal-feature-maintenance","title":"TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models","date":"2023-11-27","arxiv_id":"2311.16503","repositories_listed":1,"syntology":null},{"url":"/paper/vit-lens-2-gateway-to-omni-modal-intelligence","slug":"vit-lens-2-gateway-to-omni-modal-intelligence","title":"ViT-Lens: Towards Omni-modal Representations","date":"2023-11-27","arxiv_id":"2311.16081","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vit-lens-2-gateway-to-omni-modal-intelligence#ran","syntology_url":"https://syntology.ai/paper/2311.16081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.16081"}},"official":{"repos":["TencentARC/ViT-Lens"],"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/bs-diff-effective-bone-suppression-using","slug":"bs-diff-effective-bone-suppression-using","title":"BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images","date":"2023-11-26","arxiv_id":"2311.15328","repositories_listed":1,"syntology":null},{"url":"/paper/flow-guided-diffusion-for-video-inpainting","slug":"flow-guided-diffusion-for-video-inpainting","title":"Flow-Guided Diffusion for Video Inpainting","date":"2023-11-26","arxiv_id":"2311.15368","repositories_listed":1,"syntology":null},{"url":"/paper/instastyle-inversion-noise-of-a-stylized","slug":"instastyle-inversion-noise-of-a-stylized","title":"InstaStyle: Inversion Noise of a Stylized Image is Secretly a Style Adviser","date":"2023-11-25","arxiv_id":"2311.15040","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/instastyle-inversion-noise-of-a-stylized#ran","syntology_url":"https://syntology.ai/paper/2311.15040","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.15040"}},"official":{"repos":["cuixing100876/instastyle"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/resfusion-prior-residual-noise-embedded","slug":"resfusion-prior-residual-noise-embedded","title":"Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise","date":"2023-11-25","arxiv_id":"2311.14900","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":1,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/resfusion-prior-residual-noise-embedded#ran","syntology_url":"https://syntology.ai/paper/2311.14900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14900"}},"official":{"repos":["nkicsl/resfusion"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/word-for-person-zero-shot-composed-person","slug":"word-for-person-zero-shot-composed-person","title":"Automatic Synthetic Data and Fine-grained Adaptive Feature Alignment for Composed Person Retrieval","date":"2023-11-25","arxiv_id":"2311.16515","repositories_listed":1,"syntology":null},{"url":"/paper/demofusion-democratising-high-resolution","slug":"demofusion-democratising-high-resolution","title":"DemoFusion: Democratising High-Resolution Image Generation With No $$$","date":"2023-11-24","arxiv_id":"2311.16973","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"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: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/demofusion-democratising-high-resolution#ran","syntology_url":"https://syntology.ai/paper/2311.16973","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.16973"}},"official":{"repos":["PRIS-CV/DemoFusion"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/image-super-resolution-with-text-prompt","slug":"image-super-resolution-with-text-prompt","title":"Image Super-Resolution with Text Prompt Diffusion","date":"2023-11-24","arxiv_id":"2311.14282","repositories_listed":1,"syntology":null},{"url":"/paper/mvcontrol-adding-conditional-control-to-multi","slug":"mvcontrol-adding-conditional-control-to-multi","title":"MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation","date":"2023-11-24","arxiv_id":"2311.14494","repositories_listed":1,"syntology":null},{"url":"/paper/paragraph-to-image-generation-with","slug":"paragraph-to-image-generation-with","title":"Paragraph-to-Image Generation with Information-Enriched Diffusion Model","date":"2023-11-24","arxiv_id":"2311.14284","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/paragraph-to-image-generation-with#ran","syntology_url":"https://syntology.ai/paper/2311.14284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14284"}},"official":{"repos":["weijiawu/paradiffusion"],"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/act-adversarial-consistency-models","slug":"act-adversarial-consistency-models","title":"ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion Models","date":"2023-11-23","arxiv_id":"2311.14097","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/act-adversarial-consistency-models#ran","syntology_url":"https://syntology.ai/paper/2311.14097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14097"}},"official":{"repos":["kong13661/act"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}}],"record_sha256":"d7573b57f290bd748cf8724cc00b4bbd154e5dc5f3d6378f98a37ee939ed2bd9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}