{"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/10","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":10,"pages_in_order":67,"rows_per_page":100,"rows":[901,1000],"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/9","next":"/task/image-generation/papers/11","papers":[{"url":"/paper/t2isafety-benchmark-for-assessing-fairness","slug":"t2isafety-benchmark-for-assessing-fairness","title":"T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation","date":"2025-01-22","arxiv_id":"2501.12612","repositories_listed":1,"syntology":null},{"url":"/paper/composeanyone-controllable-layout-to-human","slug":"composeanyone-controllable-layout-to-human","title":"ComposeAnyone: Controllable Layout-to-Human Generation with Decoupled Multimodal Conditions","date":"2025-01-21","arxiv_id":"2501.12173","repositories_listed":1,"syntology":null},{"url":"/paper/expertise-elevates-ai-usage-experimental","slug":"expertise-elevates-ai-usage-experimental","title":"Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists","date":"2025-01-21","arxiv_id":"2501.12374","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-formulation-of-lipschitz-constrained","slug":"a-new-formulation-of-lipschitz-constrained","title":"A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs","date":"2025-01-20","arxiv_id":"2501.11236","repositories_listed":1,"syntology":null},{"url":"/paper/block-flow-learning-straight-flow-on-data","slug":"block-flow-learning-straight-flow-on-data","title":"Block Flow: Learning Straight Flow on Data Blocks","date":"2025-01-20","arxiv_id":"2501.11361","repositories_listed":1,"syntology":null},{"url":"/paper/physics-informed-deepct-sinogram-wavelet","slug":"physics-informed-deepct-sinogram-wavelet","title":"Physics-informed DeepCT: Sinogram Wavelet Decomposition Meets Masked Diffusion","date":"2025-01-17","arxiv_id":"2501.09935","repositories_listed":1,"syntology":null},{"url":"/paper/anystory-towards-unified-single-and-multiple","slug":"anystory-towards-unified-single-and-multiple","title":"AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation","date":"2025-01-16","arxiv_id":"2501.09503","repositories_listed":1,"syntology":null},{"url":"/paper/pixels-progressive-image-xemplar-based","slug":"pixels-progressive-image-xemplar-based","title":"PIXELS: Progressive Image Xemplar-based Editing with Latent Surgery","date":"2025-01-16","arxiv_id":"2501.09826","repositories_listed":1,"syntology":null},{"url":"/paper/svia-a-street-view-image-anonymization","slug":"svia-a-street-view-image-anonymization","title":"SVIA: A Street View Image Anonymization Framework for Self-Driving Applications","date":"2025-01-16","arxiv_id":"2501.09393","repositories_listed":1,"syntology":null},{"url":"/paper/complexity-control-facilitates-reasoning","slug":"complexity-control-facilitates-reasoning","title":"Complexity Control Facilitates Reasoning-Based Compositional Generalization in Transformers","date":"2025-01-15","arxiv_id":"2501.08537","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":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/complexity-control-facilitates-reasoning#ran","syntology_url":"https://syntology.ai/paper/2501.08537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.08537"}},"official":{"repos":["sjtuzzw/complexity_control"],"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/generative-diffusion-model-with-inverse","slug":"generative-diffusion-model-with-inverse","title":"Generative diffusion model with inverse renormalization group flows","date":"2025-01-15","arxiv_id":"2501.09064","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-llms-can-reason-about-aesthetics","slug":"multimodal-llms-can-reason-about-aesthetics","title":"Multimodal LLMs Can Reason about Aesthetics in Zero-Shot","date":"2025-01-15","arxiv_id":"2501.09012","repositories_listed":1,"syntology":null},{"url":"/paper/yuan-yielding-unblemished-aesthetics-through","slug":"yuan-yielding-unblemished-aesthetics-through","title":"Yuan: Yielding Unblemished Aesthetics Through A Unified Network for Visual Imperfections Removal in Generated Images","date":"2025-01-15","arxiv_id":"2501.08505","repositories_listed":1,"syntology":null},{"url":"/paper/d-2-dpm-dual-denoising-for-quantized","slug":"d-2-dpm-dual-denoising-for-quantized","title":"D$^2$-DPM: Dual Denoising for Quantized Diffusion Probabilistic Models","date":"2025-01-14","arxiv_id":"2501.08180","repositories_listed":1,"syntology":null},{"url":"/paper/religious-bias-landscape-in-language-and-text","slug":"religious-bias-landscape-in-language-and-text","title":"Religious Bias Landscape in Language and Text-to-Image Models: Analysis, Detection, and Debiasing Strategies","date":"2025-01-14","arxiv_id":"2501.08441","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-text-to-image-generation-via","slug":"boosting-text-to-image-generation-via","title":"Boosting Text-To-Image Generation via Multilingual Prompting in Large Multimodal Models","date":"2025-01-13","arxiv_id":"2501.07086","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-image-generation-fidelity-via","slug":"enhancing-image-generation-fidelity-via","title":"Enhancing Image Generation Fidelity via Progressive Prompts","date":"2025-01-13","arxiv_id":"2501.07070","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/enhancing-image-generation-fidelity-via#ran","syntology_url":"https://syntology.ai/paper/2501.07070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.07070"}},"official":{"repos":["zhenxiong-dl/icassp2025-rcac"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/padding-tone-a-mechanistic-analysis-of","slug":"padding-tone-a-mechanistic-analysis-of","title":"Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models","date":"2025-01-12","arxiv_id":"2501.06751","repositories_listed":1,"syntology":null},{"url":"/paper/divtrackee-versus-dyntracker-promoting","slug":"divtrackee-versus-dyntracker-promoting","title":"DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial Recognition against Dynamic FR Strategy","date":"2025-01-11","arxiv_id":"2501.06533","repositories_listed":1,"syntology":null},{"url":"/paper/poetry-in-pixels-prompt-tuning-for-poem-image","slug":"poetry-in-pixels-prompt-tuning-for-poem-image","title":"Poetry in Pixels: Prompt Tuning for Poem Image Generation via Diffusion Models","date":"2025-01-10","arxiv_id":"2501.05839","repositories_listed":1,"syntology":null},{"url":"/paper/3dis-flux-simple-and-efficient-multi-instance","slug":"3dis-flux-simple-and-efficient-multi-instance","title":"3DIS-FLUX: simple and efficient multi-instance generation with DiT rendering","date":"2025-01-09","arxiv_id":"2501.05131","repositories_listed":1,"syntology":null},{"url":"/paper/the-gan-is-dead-long-live-the-gan-a-modern","slug":"the-gan-is-dead-long-live-the-gan-a-modern","title":"The GAN is dead; long live the GAN! A Modern GAN Baseline","date":"2025-01-09","arxiv_id":"2501.05441","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":6,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":11,"phrase":"9 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/the-gan-is-dead-long-live-the-gan-a-modern#ran","syntology_url":"https://syntology.ai/paper/2501.05441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.05441"}},"official":{"repos":["brownvc/r3gan"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":6,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/layermix-enhanced-data-augmentation-through","slug":"layermix-enhanced-data-augmentation-through","title":"LayerMix: Enhanced Data Augmentation through Fractal Integration for Robust Deep Learning","date":"2025-01-08","arxiv_id":"2501.04861","repositories_listed":1,"syntology":null},{"url":"/paper/face-makeup-multimodal-facial-prompts-for","slug":"face-makeup-multimodal-facial-prompts-for","title":"Face-MakeUp: Multimodal Facial Prompts for Text-to-Image Generation","date":"2025-01-05","arxiv_id":"2501.02523","repositories_listed":1,"syntology":null},{"url":"/paper/eligen-entity-level-controlled-image","slug":"eligen-entity-level-controlled-image","title":"EliGen: Entity-Level Controlled Image Generation with Regional Attention","date":"2025-01-02","arxiv_id":"2501.01097","repositories_listed":1,"syntology":null},{"url":"/paper/projectedex-enhancing-generation-in","slug":"projectedex-enhancing-generation-in","title":"ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer","date":"2025-01-02","arxiv_id":"2501.01392","repositories_listed":1,"syntology":null},{"url":"/paper/anatomical-consistency-and-adaptive-prior","slug":"anatomical-consistency-and-adaptive-prior","title":"Anatomical Consistency and Adaptive Prior-informed Transformation for Multi-contrast MR Image Synthesis via Diffusion Model","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/autopresent-designing-structured-visuals-from","slug":"autopresent-designing-structured-visuals-from","title":"AutoPresent: Designing Structured Visuals from Scratch","date":"2025-01-01","arxiv_id":"2501.00912","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/autopresent-designing-structured-visuals-from#ran","syntology_url":"https://syntology.ai/paper/2501.00912","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.00912"}},"official":{"repos":["para-lost/AutoPresent"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deterministic-image-to-image-translation-via","slug":"deterministic-image-to-image-translation-via","title":"Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/early-bird-diffusion-investigating-and","slug":"early-bird-diffusion-investigating-and","title":"Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/finding-local-diffusion-schrodinger-bridge-1","slug":"finding-local-diffusion-schrodinger-bridge-1","title":"Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold Network","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/imaginefsl-self-supervised-pretraining","slug":"imaginefsl-self-supervised-pretraining","title":"ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-partonomic-3d-reconstruction-from","slug":"learning-partonomic-3d-reconstruction-from","title":"Learning Partonomic 3D Reconstruction from Image Collections","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/let-s-verify-and-reinforce-image-generation","slug":"let-s-verify-and-reinforce-image-generation","title":"Let's Verify and Reinforce Image Generation Step by Step","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/spherical-manifold-guided-diffusion-model-for","slug":"spherical-manifold-guided-diffusion-model-for","title":"Spherical Manifold Guided Diffusion Model for Panoramic Image Generation","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dual-diffusion-for-unified-image-generation","slug":"dual-diffusion-for-unified-image-generation","title":"Dual Diffusion for Unified Image Generation and Understanding","date":"2024-12-31","arxiv_id":"2501.00289","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":1,"n_no_contract":6,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dual-diffusion-for-unified-image-generation#ran","syntology_url":"https://syntology.ai/paper/2501.00289","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.00289"}},"official":null}},{"url":"/paper/token-pruning-for-caching-better-9-times","slug":"token-pruning-for-caching-better-9-times","title":"Token Pruning for Caching Better: 9 Times Acceleration on Stable Diffusion for Free","date":"2024-12-31","arxiv_id":"2501.00375","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-diffusion-model-for-quark-and-gluon","slug":"quantum-diffusion-model-for-quark-and-gluon","title":"Quantum Diffusion Model for Quark and Gluon Jet Generation","date":"2024-12-30","arxiv_id":"2412.21082","repositories_listed":1,"syntology":null},{"url":"/paper/vmix-improving-text-to-image-diffusion-model","slug":"vmix-improving-text-to-image-diffusion-model","title":"VMix: Improving Text-to-Image Diffusion Model with Cross-Attention Mixing Control","date":"2024-12-30","arxiv_id":"2412.20800","repositories_listed":1,"syntology":null},{"url":"/paper/fairdiffusion-enhancing-equity-in-latent","slug":"fairdiffusion-enhancing-equity-in-latent","title":"FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation","date":"2024-12-29","arxiv_id":"2412.20374","repositories_listed":1,"syntology":null},{"url":"/paper/motion-transfer-driven-intra-class-data","slug":"motion-transfer-driven-intra-class-data","title":"Motion Transfer-Driven intra-class data augmentation for Finger Vein Recognition","date":"2024-12-29","arxiv_id":"2412.20327","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-image-restoration-using-few-step","slug":"zero-shot-image-restoration-using-few-step","title":"Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)","date":"2024-12-29","arxiv_id":"2412.20596","repositories_listed":1,"syntology":null},{"url":"/paper/evalmuse-40k-a-reliable-and-fine-grained","slug":"evalmuse-40k-a-reliable-and-fine-grained","title":"EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model Evaluation","date":"2024-12-24","arxiv_id":"2412.18150","repositories_listed":1,"syntology":null},{"url":"/paper/extract-free-dense-misalignment-from-clip","slug":"extract-free-dense-misalignment-from-clip","title":"Extract Free Dense Misalignment from CLIP","date":"2024-12-24","arxiv_id":"2412.18404","repositories_listed":1,"syntology":null},{"url":"/paper/distilled-decoding-1-one-step-sampling-of","slug":"distilled-decoding-1-one-step-sampling-of","title":"Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching","date":"2024-12-22","arxiv_id":"2412.17153","repositories_listed":1,"syntology":null},{"url":"/paper/human-guided-image-generation-for-expanding","slug":"human-guided-image-generation-for-expanding","title":"Human-Guided Image Generation for Expanding Small-Scale Training Image Datasets","date":"2024-12-22","arxiv_id":"2412.16839","repositories_listed":1,"syntology":null},{"url":"/paper/bs-ldm-effective-bone-suppression-in-high","slug":"bs-ldm-effective-bone-suppression-in-high","title":"BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models","date":"2024-12-20","arxiv_id":"2412.15670","repositories_listed":1,"syntology":null},{"url":"/paper/personalized-representation-from-personalized","slug":"personalized-representation-from-personalized","title":"Personalized Representation from Personalized Generation","date":"2024-12-20","arxiv_id":"2412.16156","repositories_listed":1,"syntology":{"n":15,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"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) · 5 unverified","sample_list":"/paper/personalized-representation-from-personalized#ran","syntology_url":"https://syntology.ai/paper/2412.16156","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.16156"}},"official":{"repos":["ssundaram21/personalized-rep"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/personamagic-stage-regulated-high-fidelity","slug":"personamagic-stage-regulated-high-fidelity","title":"PersonaMagic: Stage-Regulated High-Fidelity Face Customization with Tandem Equilibrium","date":"2024-12-20","arxiv_id":"2412.15674","repositories_listed":1,"syntology":null},{"url":"/paper/flowar-scale-wise-autoregressive-image","slug":"flowar-scale-wise-autoregressive-image","title":"FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching","date":"2024-12-19","arxiv_id":"2412.15205","repositories_listed":1,"syntology":null},{"url":"/paper/next-patch-prediction-for-autoregressive","slug":"next-patch-prediction-for-autoregressive","title":"Next Patch Prediction for Autoregressive Visual Generation","date":"2024-12-19","arxiv_id":"2412.15321","repositories_listed":1,"syntology":null},{"url":"/paper/autoregressive-video-generation-without","slug":"autoregressive-video-generation-without","title":"Autoregressive Video Generation without Vector Quantization","date":"2024-12-18","arxiv_id":"2412.14169","repositories_listed":1,"syntology":{"n":26,"n_ran":19,"n_constructed":18,"n_ran_checked":19,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":0,"phrase":"19 ran (of which 18 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/autoregressive-video-generation-without#ran","syntology_url":"https://syntology.ai/paper/2412.14169","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.14169"}},"official":{"repos":["baaivision/nova"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":18,"n_ran_no_instrument_failure":19,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/surrealistic-like-image-generation-with","slug":"surrealistic-like-image-generation-with","title":"Surrealistic-like Image Generation with Vision-Language Models","date":"2024-12-18","arxiv_id":"2412.14366","repositories_listed":1,"syntology":null},{"url":"/paper/artaug-enhancing-text-to-image-generation","slug":"artaug-enhancing-text-to-image-generation","title":"ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction","date":"2024-12-17","arxiv_id":"2412.12888","repositories_listed":1,"syntology":null},{"url":"/paper/attentive-eraser-unleashing-diffusion-model-s","slug":"attentive-eraser-unleashing-diffusion-model-s","title":"Attentive Eraser: Unleashing Diffusion Model's Object Removal Potential via Self-Attention Redirection Guidance","date":"2024-12-17","arxiv_id":"2412.12974","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/attentive-eraser-unleashing-diffusion-model-s#ran","syntology_url":"https://syntology.ai/paper/2412.12974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.12974"}},"official":{"repos":["anonym0u3/attentiveeraser"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/guided-and-variance-corrected-fusion-with-one","slug":"guided-and-variance-corrected-fusion-with-one","title":"Guided and Variance-Corrected Fusion with One-shot Style Alignment for Large-Content Image Generation","date":"2024-12-17","arxiv_id":"2412.12771","repositories_listed":1,"syntology":null},{"url":"/paper/3d-2-actor-learning-pose-conditioned-3d-aware","slug":"3d-2-actor-learning-pose-conditioned-3d-aware","title":"3D$^2$-Actor: Learning Pose-Conditioned 3D-Aware Denoiser for Realistic Gaussian Avatar Modeling","date":"2024-12-16","arxiv_id":"2412.11599","repositories_listed":1,"syntology":null},{"url":"/paper/causal-diffusion-transformers-for-generative","slug":"causal-diffusion-transformers-for-generative","title":"Causal Diffusion Transformers for Generative Modeling","date":"2024-12-16","arxiv_id":"2412.12095","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":3,"n_ran_checked":6,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":10,"phrase":"8 ran (of which 3 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/causal-diffusion-transformers-for-generative#ran","syntology_url":"https://syntology.ai/paper/2412.12095","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.12095"}},"official":{"repos":["causalfusion/causalfusion"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":3,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/fedcar-cross-client-adaptive-re-weighting-for","slug":"fedcar-cross-client-adaptive-re-weighting-for","title":"FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning","date":"2024-12-16","arxiv_id":"2412.11463","repositories_listed":1,"syntology":null},{"url":"/paper/idprotector-an-adversarial-noise-encoder-to","slug":"idprotector-an-adversarial-noise-encoder-to","title":"IDProtector: An Adversarial Noise Encoder to Protect Against ID-Preserving Image Generation","date":"2024-12-16","arxiv_id":"2412.11638","repositories_listed":1,"syntology":null},{"url":"/paper/relation-guided-adversarial-learning-for-data","slug":"relation-guided-adversarial-learning-for-data","title":"Relation-Guided Adversarial Learning for Data-free Knowledge Transfer","date":"2024-12-16","arxiv_id":"2412.11380","repositories_listed":1,"syntology":null},{"url":"/paper/versagen-unleashing-versatile-visual-control","slug":"versagen-unleashing-versatile-visual-control","title":"VersaGen: Unleashing Versatile Visual Control for Text-to-Image Synthesis","date":"2024-12-16","arxiv_id":"2412.11594","repositories_listed":1,"syntology":null},{"url":"/paper/plug-and-play-priors-as-a-score-based-method","slug":"plug-and-play-priors-as-a-score-based-method","title":"Plug-and-Play Priors as a Score-Based Method","date":"2024-12-15","arxiv_id":"2412.11108","repositories_listed":1,"syntology":null},{"url":"/paper/grid-visual-layout-generation","slug":"grid-visual-layout-generation","title":"Grid: Omni Visual Generation","date":"2024-12-14","arxiv_id":"2412.10718","repositories_listed":1,"syntology":null},{"url":"/paper/softvq-vae-efficient-1-dimensional-continuous","slug":"softvq-vae-efficient-1-dimensional-continuous","title":"SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer","date":"2024-12-14","arxiv_id":"2412.10958","repositories_listed":1,"syntology":null},{"url":"/paper/financial-fine-tuning-a-large-time-series","slug":"financial-fine-tuning-a-large-time-series","title":"Financial Fine-tuning a Large Time Series Model","date":"2024-12-13","arxiv_id":"2412.09880","repositories_listed":1,"syntology":null},{"url":"/paper/simple-guidance-mechanisms-for-discrete","slug":"simple-guidance-mechanisms-for-discrete","title":"Simple Guidance Mechanisms for Discrete Diffusion Models","date":"2024-12-13","arxiv_id":"2412.10193","repositories_listed":1,"syntology":{"n":23,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":17,"n_pointer_only":3,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 1 honoured, 0 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/simple-guidance-mechanisms-for-discrete#ran","syntology_url":"https://syntology.ai/paper/2412.10193","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.10193"}},"official":{"repos":["kuleshov-group/discrete-diffusion-guidance"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/diffusion-enhanced-test-time-adaptation-with","slug":"diffusion-enhanced-test-time-adaptation-with","title":"Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation","date":"2024-12-12","arxiv_id":"2412.09706","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"4 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/diffusion-enhanced-test-time-adaptation-with#ran","syntology_url":"https://syntology.ai/paper/2412.09706","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.09706"}},"official":{"repos":["chunmeifeng/difftpt"],"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","unlocated"]}}},{"url":"/paper/fast-prompt-alignment-for-text-to-image","slug":"fast-prompt-alignment-for-text-to-image","title":"Fast Prompt Alignment for Text-to-Image Generation","date":"2024-12-11","arxiv_id":"2412.08639","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/fast-prompt-alignment-for-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2412.08639","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.08639"}},"official":{"repos":["tiktok/fast_prompt_alignment"],"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/generative-modeling-with-explicit-memory","slug":"generative-modeling-with-explicit-memory","title":"Generative Modeling with Explicit Memory","date":"2024-12-11","arxiv_id":"2412.08781","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":8,"n_instrument":5,"n_unverified":2,"n_honours":2,"n_violates":1,"n_no_contract":5,"n_pointer_only":15,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 1 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/generative-modeling-with-explicit-memory#ran","syntology_url":"https://syntology.ai/paper/2412.08781","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.08781"}},"official":{"repos":["lins-lab/gmem"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/invdiff-invariant-guidance-for-bias","slug":"invdiff-invariant-guidance-for-bias","title":"InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models","date":"2024-12-11","arxiv_id":"2412.08480","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/invdiff-invariant-guidance-for-bias#ran","syntology_url":"https://syntology.ai/paper/2412.08480","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.08480"}},"official":{"repos":["hundredl/invdiff"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-flow-fields-in-attention-for","slug":"learning-flow-fields-in-attention-for","title":"Learning Flow Fields in Attention for Controllable Person Image Generation","date":"2024-12-11","arxiv_id":"2412.08486","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":1,"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/learning-flow-fields-in-attention-for#ran","syntology_url":"https://syntology.ai/paper/2412.08486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.08486"}},"official":{"repos":["franciszzj/leffa"],"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/multimodal-latent-language-modeling-with-next","slug":"multimodal-latent-language-modeling-with-next","title":"Multimodal Latent Language Modeling with Next-Token Diffusion","date":"2024-12-11","arxiv_id":"2412.08635","repositories_listed":1,"syntology":null},{"url":"/paper/acdit-interpolating-autoregressive","slug":"acdit-interpolating-autoregressive","title":"ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer","date":"2024-12-10","arxiv_id":"2412.07720","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/acdit-interpolating-autoregressive#ran","syntology_url":"https://syntology.ai/paper/2412.07720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.07720"}},"official":{"repos":["thunlp/acdit"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cap-evaluation-of-persuasive-and-creative","slug":"cap-evaluation-of-persuasive-and-creative","title":"CAP: Evaluation of Persuasive and Creative Image Generation","date":"2024-12-10","arxiv_id":"2412.10426","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-alignment-for-post-unlearning-text","slug":"boosting-alignment-for-post-unlearning-text","title":"Boosting Alignment for Post-Unlearning Text-to-Image Generative Models","date":"2024-12-09","arxiv_id":"2412.07808","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":6,"n_instrument":7,"n_unverified":3,"n_honours":3,"n_violates":1,"n_no_contract":2,"n_pointer_only":16,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 3 honoured, 1 violated, 2 with no contract checked; 7 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/boosting-alignment-for-post-unlearning-text#ran","syntology_url":"https://syntology.ai/paper/2412.07808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.07808"}},"official":{"repos":["reds-lab/restricted_gradient_diversity_unlearning"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/emov2-pushing-5m-vision-model-frontier","slug":"emov2-pushing-5m-vision-model-frontier","title":"EMOv2: Pushing 5M Vision Model Frontier","date":"2024-12-09","arxiv_id":"2412.06674","repositories_listed":1,"syntology":null},{"url":"/paper/precise-fast-and-low-cost-concept-erasure-in","slug":"precise-fast-and-low-cost-concept-erasure-in","title":"Precise, Fast, and Low-cost Concept Erasure in Value Space: Orthogonal Complement Matters","date":"2024-12-09","arxiv_id":"2412.06143","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":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) · 2 unverified","sample_list":"/paper/precise-fast-and-low-cost-concept-erasure-in#ran","syntology_url":"https://syntology.ai/paper/2412.06143","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.06143"}},"official":{"repos":["wyuan1001/adavd"],"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/proactive-agents-for-multi-turn-text-to-image","slug":"proactive-agents-for-multi-turn-text-to-image","title":"Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty","date":"2024-12-09","arxiv_id":"2412.06771","repositories_listed":1,"syntology":null},{"url":"/paper/bidm-pushing-the-limit-of-quantization-for","slug":"bidm-pushing-the-limit-of-quantization-for","title":"BiDM: Pushing the Limit of Quantization for Diffusion Models","date":"2024-12-08","arxiv_id":"2412.05926","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":6,"n_instrument":8,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":4,"n_pointer_only":16,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 8 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/bidm-pushing-the-limit-of-quantization-for#ran","syntology_url":"https://syntology.ai/paper/2412.05926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.05926"}},"official":{"repos":["xingyu-zheng/bidm"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/flexdit-dynamic-token-density-control-for","slug":"flexdit-dynamic-token-density-control-for","title":"FlexDiT: Dynamic Token Density Control for Diffusion Transformer","date":"2024-12-08","arxiv_id":"2412.06028","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 4 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) · 2 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/flexdit-dynamic-token-density-control-for#ran","syntology_url":"https://syntology.ai/paper/2412.06028","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.06028"}},"official":{"repos":["changsn/FlexDiT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/lora-rar-learning-to-merge-loras-via","slug":"lora-rar-learning-to-merge-loras-via","title":"LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation","date":"2024-12-06","arxiv_id":"2412.05148","repositories_listed":1,"syntology":null},{"url":"/paper/safeguarding-text-to-image-generation-via","slug":"safeguarding-text-to-image-generation-via","title":"Safeguarding Text-to-Image Generation via Inference-Time Prompt-Noise Optimization","date":"2024-12-05","arxiv_id":"2412.03876","repositories_listed":1,"syntology":null},{"url":"/paper/structure-aware-stylized-image-synthesis-for","slug":"structure-aware-stylized-image-synthesis-for","title":"Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation","date":"2024-12-05","arxiv_id":"2412.04296","repositories_listed":1,"syntology":null},{"url":"/paper/zipar-accelerating-autoregressive-image","slug":"zipar-accelerating-autoregressive-image","title":"ZipAR: Accelerating Auto-regressive Image Generation through Spatial Locality","date":"2024-12-05","arxiv_id":"2412.04062","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":4,"n_pointer_only":12,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/zipar-accelerating-autoregressive-image#ran","syntology_url":"https://syntology.ai/paper/2412.04062","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.04062"}},"official":{"repos":["ThisisBillhe/ZipAR"],"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/is-jpeg-ai-going-to-change-image-forensics","slug":"is-jpeg-ai-going-to-change-image-forensics","title":"Is JPEG AI going to change image forensics?","date":"2024-12-04","arxiv_id":"2412.03261","repositories_listed":1,"syntology":null},{"url":"/paper/mrgen-diffusion-based-controllable-data","slug":"mrgen-diffusion-based-controllable-data","title":"MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated Modalities","date":"2024-12-04","arxiv_id":"2412.04106","repositories_listed":1,"syntology":null},{"url":"/paper/mv-adapter-multi-view-consistent-image","slug":"mv-adapter-multi-view-consistent-image","title":"MV-Adapter: Multi-view Consistent Image Generation Made Easy","date":"2024-12-04","arxiv_id":"2412.03632","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"6 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mv-adapter-multi-view-consistent-image#ran","syntology_url":"https://syntology.ai/paper/2412.03632","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.03632"}},"official":null}},{"url":"/paper/patchdpo-patch-level-dpo-for-finetuning-free","slug":"patchdpo-patch-level-dpo-for-finetuning-free","title":"PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation","date":"2024-12-04","arxiv_id":"2412.03177","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":11,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/patchdpo-patch-level-dpo-for-finetuning-free#ran","syntology_url":"https://syntology.ai/paper/2412.03177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.03177"}},"official":{"repos":["hqhqaq/patchdpo"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/accdiffusion-v2-towards-more-accurate-higher","slug":"accdiffusion-v2-towards-more-accurate-higher","title":"AccDiffusion v2: Towards More Accurate Higher-Resolution Diffusion Extrapolation","date":"2024-12-03","arxiv_id":"2412.02099","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"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) · 1 unverified","sample_list":"/paper/accdiffusion-v2-towards-more-accurate-higher#ran","syntology_url":"https://syntology.ai/paper/2412.02099","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.02099"}},"official":{"repos":["lzhxmu/accdiffusion_v2"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-attention-head-position-patterns-can","slug":"cross-attention-head-position-patterns-can","title":"Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models","date":"2024-12-03","arxiv_id":"2412.02237","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":1,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 1 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) · 1 unverified","sample_list":"/paper/cross-attention-head-position-patterns-can#ran","syntology_url":"https://syntology.ai/paper/2412.02237","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.02237"}},"official":{"repos":["snu-drl/hrv"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-photography-scene-consistent","slug":"generative-photography-scene-consistent","title":"Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image Synthesis","date":"2024-12-03","arxiv_id":"2412.02168","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":2,"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/generative-photography-scene-consistent#ran","syntology_url":"https://syntology.ai/paper/2412.02168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.02168"}},"official":null}},{"url":"/paper/scimage-how-good-are-multimodal-large","slug":"scimage-how-good-are-multimodal-large","title":"ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation?","date":"2024-12-03","arxiv_id":"2412.02368","repositories_listed":1,"syntology":null},{"url":"/paper/concept-replacer-replacing-sensitive-concepts","slug":"concept-replacer-replacing-sensitive-concepts","title":"Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision Localization","date":"2024-12-02","arxiv_id":"2412.01244","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-vae-with-a-diffusion-based","slug":"hierarchical-vae-with-a-diffusion-based","title":"Hierarchical VAE with a Diffusion-based VampPrior","date":"2024-12-02","arxiv_id":"2412.01373","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":2,"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/hierarchical-vae-with-a-diffusion-based#ran","syntology_url":"https://syntology.ai/paper/2412.01373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.01373"}},"official":{"repos":["akuzina/dvp_vae"],"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/iqa-adapter-exploring-knowledge-transfer-from","slug":"iqa-adapter-exploring-knowledge-transfer-from","title":"IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative Models","date":"2024-12-02","arxiv_id":"2412.01794","repositories_listed":1,"syntology":null},{"url":"/paper/mftf-mask-free-training-free-object-level","slug":"mftf-mask-free-training-free-object-level","title":"MFTF: Mask-free Training-free Object Level Layout Control Diffusion Model","date":"2024-12-02","arxiv_id":"2412.01284","repositories_listed":1,"syntology":null},{"url":"/paper/mulan-adapting-multilingual-diffusion-models","slug":"mulan-adapting-multilingual-diffusion-models","title":"MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages with Negligible Cost","date":"2024-12-02","arxiv_id":"2412.01271","repositories_listed":1,"syntology":null},{"url":"/paper/omniflow-any-to-any-generation-with-multi","slug":"omniflow-any-to-any-generation-with-multi","title":"OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows","date":"2024-12-02","arxiv_id":"2412.01169","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/omniflow-any-to-any-generation-with-multi#ran","syntology_url":"https://syntology.ai/paper/2412.01169","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.01169"}},"official":{"repos":["jacklishufan/omniflows"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/textssr-diffusion-based-data-synthesis-for","slug":"textssr-diffusion-based-data-synthesis-for","title":"TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition","date":"2024-12-02","arxiv_id":"2412.01137","repositories_listed":1,"syntology":null}],"record_sha256":"0b6f40f065f9d8762c52a5d5894669e6ed3544d28aaf8a61f31c6250555a6b79","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}