{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/ismap","entry":"ismap","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":70,"n_papers_ran":70,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":2,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":70,"n_places_pointer_only":22,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":0},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2607.06590","paper":"/paper/arxiv-2607-06590","title":"AI for Cultural Heritage Textiles: Fine-Tuned Latent Diffusion for Novel Ulos Motif Synthesis","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"CompVis/latent-diffusion","path":"ldm/util.py","file_url":"https://github.com/CompVis/latent-diffusion/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2508.10298","paper":"/paper/arxiv-2508-10298","title":"SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"MichaelMaiii/SynBrain","path":"src/ldm/util.py","file_url":"https://github.com/MichaelMaiii/SynBrain/blob/HEAD/src/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2412.11549","paper":"/paper/mpq-dm-mixed-precision-quantization-for","title":"MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models","date":"2024-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cantbebetter2/mpq-dm","path":"ldm/util.py","file_url":"https://github.com/cantbebetter2/mpq-dm/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2412.11058","paper":"/paper/shmt-self-supervised-hierarchical-makeup","title":"SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models","date":"2024-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Snowfallingplum/SHMT","path":"ldm/util.py","file_url":"https://github.com/Snowfallingplum/SHMT/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2412.08580","paper":"/paper/laion-sg-an-enhanced-large-scale-dataset-for","title":"LAION-SG: An Enhanced Large-Scale Dataset for Training Complex Image-Text Models with Structural Annotations","date":"2024-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangling0818/sgdiff","path":"ldm/util.py","file_url":"https://github.com/yangling0818/sgdiff/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2412.05926","paper":"/paper/bidm-pushing-the-limit-of-quantization-for","title":"BiDM: Pushing the Limit of Quantization for Diffusion Models","date":"2024-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xingyu-zheng/bidm","path":"bidm-cifar/models/util_ldm.py","file_url":"https://github.com/xingyu-zheng/bidm/blob/HEAD/bidm-cifar/models/util_ldm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2411.16969","paper":"/paper/zoomldm-latent-diffusion-model-for-multi","title":"ZoomLDM: Latent Diffusion Model for multi-scale image generation","date":"2024-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvlab-stonybrook/ZoomLDM","path":"ldm/util.py","file_url":"https://github.com/cvlab-stonybrook/ZoomLDM/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2411.07462","paper":"/paper/mureobjectstitch-multi-reference-image","title":"MureObjectStitch: Multi-reference Image Composition","date":"2024-11-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bcmi/mureobjectstitch-image-composition","path":"ldm/util.py","file_url":"https://github.com/bcmi/mureobjectstitch-image-composition/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2410.21638","paper":"/paper/adapting-diffusion-models-for-improved-prompt","title":"Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis","date":"2024-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeepakSridhar/fgdm","path":"controlnet/ldm/util.py","file_url":"https://github.com/DeepakSridhar/fgdm/blob/HEAD/controlnet/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2410.17918","paper":"/paper/addressing-asynchronicity-in-clinical","title":"Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation","date":"2024-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenliu-svg/ddl-cxr","path":"ldm/util.py","file_url":"https://github.com/chenliu-svg/ddl-cxr/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2410.12266","paper":"/paper/flashaudio-rectified-flows-for-fast-and-high","title":"FlashAudio: Rectified Flows for Fast and High-Fidelity Text-to-Audio Generation","date":"2024-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Text-to-Audio/AudioLCM","path":"ldm/util.py","file_url":"https://github.com/Text-to-Audio/AudioLCM/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2410.01153","paper":"/paper/text2pde-latent-diffusion-models-for","title":"Text2PDE: Latent Diffusion Models for Accessible Physics Simulation","date":"2024-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anthonyzhou-1/ldm_pdes","path":"modules/utils.py","file_url":"https://github.com/anthonyzhou-1/ldm_pdes/blob/HEAD/modules/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2409.15278","paper":"/paper/pixwizard-versatile-image-to-image-visual","title":"PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions","date":"2024-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"afeng-x/pixwizard","path":"models/clip/util.py","file_url":"https://github.com/afeng-x/pixwizard/blob/HEAD/models/clip/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2409.12346","paper":"/paper/simultaneous-music-separation-and-generation","title":"Simultaneous Music Separation and Generation Using Multi-Track Latent Diffusion Models","date":null,"month_inferred_from_arxiv_id":"2024-09","title_source":"archive","repo":"karchkha/msg-ld","path":"src/latent_diffusion/util.py","file_url":"https://github.com/karchkha/msg-ld/blob/HEAD/src/latent_diffusion/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2409.09144","paper":"/paper/primedepth-efficient-monocular-depth","title":"PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage","date":"2024-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vislearn/PrimeDepth","path":"ldm/util.py","file_url":"https://github.com/vislearn/PrimeDepth/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2409.08260","paper":"/paper/improving-text-guided-object-inpainting-with","title":"Improving Text-guided Object Inpainting with Semantic Pre-inpainting","date":"2024-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nnn-s/catdiffusion","path":"ldm/util.py","file_url":"https://github.com/nnn-s/catdiffusion/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2408.03748","paper":"/paper/data-generation-scheme-for-thermal-modality","title":"Data Generation Scheme for Thermal Modality with Edge-Guided Adversarial Conditional Diffusion Model","date":"2024-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lengmo1996/ECDM","path":"ecdm/util.py","file_url":"https://github.com/lengmo1996/ECDM/blob/HEAD/ecdm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2407.09299","paper":"/paper/pid-physics-informed-diffusion-model-for","title":"PID: Physics-Informed Diffusion Model for Infrared Image Generation","date":"2024-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fangyuanmao/pid","path":"ldm/util.py","file_url":"https://github.com/fangyuanmao/pid/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2406.02485","paper":"/paper/stable-pose-leveraging-transformers-for-pose","title":"Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation","date":"2024-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ai-med/stablepose","path":"ldm/util.py","file_url":"https://github.com/ai-med/stablepose/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2406.00320","paper":"/paper/frieren-efficient-video-to-audio-generation","title":"Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching","date":"2024-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cyanbx/Frieren-V2A","path":"Frieren/cfm/util.py","file_url":"https://github.com/cyanbx/Frieren-V2A/blob/HEAD/Frieren/cfm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2405.15863","paper":"/paper/quality-aware-masked-diffusion-transformer","title":"Quality-aware Masked Diffusion Transformer for Enhanced Music Generation","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ivcylc/openmusic","path":"audioldm_train/utilities/model_util.py","file_url":"https://github.com/ivcylc/openmusic/blob/HEAD/audioldm_train/utilities/model_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2405.04534","paper":"/paper/tactile-augmented-radiance-fields","title":"Tactile-Augmented Radiance Fields","date":"2024-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dou-yiming/tarf","path":"img2touch/ldm/util.py","file_url":"https://github.com/dou-yiming/tarf/blob/HEAD/img2touch/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2403.12658","paper":"/paper/tuning-free-image-customization-with-image","title":"Tuning-Free Image Customization with Image and Text Guidance","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zrealli/TIGIC","path":"ldm/util.py","file_url":"https://github.com/zrealli/TIGIC/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2403.06951","paper":"/paper/deadiff-an-efficient-stylization-diffusion","title":"DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/deadiff","path":"ldm/util.py","file_url":"https://github.com/bytedance/deadiff/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2403.05053","paper":"/paper/primecomposer-faster-progressively-combined","title":"PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steering","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"codegoat24/primecomposer","path":"ldm/util.py","file_url":"https://github.com/codegoat24/primecomposer/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2403.01852","paper":"/paper/place-adaptive-layout-semantic-fusion-for","title":"PLACE: Adaptive Layout-Semantic Fusion for Semantic Image Synthesis","date":"2024-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cszy98/place","path":"ldm/util.py","file_url":"https://github.com/cszy98/place/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2402.11846","paper":"/paper/unlearncanvas-a-stylized-image-dataset-to","title":"UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"optml-group/unlearncanvas","path":"diffusion_model_finetuning/ldm/util.py","file_url":"https://github.com/optml-group/unlearncanvas/blob/HEAD/diffusion_model_finetuning/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2402.00627","paper":"/paper/caphuman-capture-your-moments-in-parallel","title":"CapHuman: Capture Your Moments in Parallel Universes","date":"2024-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vamosc/caphuman","path":"ldm/util.py","file_url":"https://github.com/vamosc/caphuman/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2312.15736","paper":"/paper/towards-real-world-blind-face-restoration-1","title":"Towards Real-World Blind Face Restoration with Generative Diffusion Prior","date":"2023-12-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenxx89/bfrffusion","path":"ldm/util.py","file_url":"https://github.com/chenxx89/bfrffusion/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2312.08872","paper":"/paper/semantic-driven-initial-image-construction","title":"The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization","date":"2023-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UT-Mao/Initial-Noise-Construction","path":"ldm/util.py","file_url":"https://github.com/UT-Mao/Initial-Noise-Construction/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2312.06607","paper":"/paper/diad-a-diffusion-based-framework-for-multi","title":"DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection","date":"2023-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lewandofskee/DiAD","path":"ldm/util.py","file_url":"https://github.com/lewandofskee/DiAD/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2312.06573","paper":"/paper/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","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vislearn/ControlNet-XS","path":"ldm/util.py","file_url":"https://github.com/vislearn/ControlNet-XS/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2312.04831","paper":"/paper/towards-stable-and-faithful-inpainting","title":"Towards Enhanced Image Inpainting: Mitigating Unwanted Object Insertion and Preserving Color Consistency","date":"2023-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yikai-wang/asuka-misato","path":"asuka-flux/ldm/util.py","file_url":"https://github.com/yikai-wang/asuka-misato/blob/HEAD/asuka-flux/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2312.02201","paper":"/paper/imagedream-image-prompt-multi-view-diffusion","title":"ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation","date":"2023-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/imagedream","path":"extern/ImageDream/imagedream/ldm/util.py","file_url":"https://github.com/bytedance/imagedream/blob/HEAD/extern/ImageDream/imagedream/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2311.18405","paper":"/paper/cat-dm-controllable-accelerated-virtual-try","title":"CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zengjianhao/cat-dm","path":"ldm/util.py","file_url":"https://github.com/zengjianhao/cat-dm/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2311.00265","paper":"/paper/adaptive-latent-diffusion-model-for-3d","title":"Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study","date":"2023-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jongdory/aldm","path":"LDM/ldm/util.py","file_url":"https://github.com/jongdory/aldm/blob/HEAD/LDM/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2311.12832","paper":"/paper/toward-effective-protection-against-diffusion","title":"Toward effective protection against diffusion based mimicry through score distillation","date":"2023-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xavihart/Diff-Protect","path":"code/ldm/util.py","file_url":"https://github.com/xavihart/Diff-Protect/blob/HEAD/code/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2310.07222","paper":"/paper/uni-paint-a-unified-framework-for-multimodal","title":"Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion Model","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ysy31415/unipaint","path":"ldm/util.py","file_url":"https://github.com/ysy31415/unipaint/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"aeb5ac8bdb47387c","mcp_get_code":{"code_sha256":"aeb5ac8bdb47387c"}},{"arxiv_id":"2310.04687","paper":"/paper/understanding-and-improving-adversarial","title":"Targeted Attack Improves Protection against Unauthorized Diffusion Customization","date":"2023-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"psyker-team/mist-v2","path":"ldm/util.py","file_url":"https://github.com/psyker-team/mist-v2/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2310.03270","paper":"/paper/efficientdm-efficient-quantization-aware-fine","title":"EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models","date":"2023-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ThisisBillhe/EfficientDM","path":"ldm/util.py","file_url":"https://github.com/ThisisBillhe/EfficientDM/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2309.00748","paper":"/paper/pathldm-text-conditioned-latent-diffusion","title":"PathLDM: Text conditioned Latent Diffusion Model for Histopathology","date":"2023-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvlab-stonybrook/pathldm","path":"ldm/util.py","file_url":"https://github.com/cvlab-stonybrook/pathldm/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2308.11408","paper":"/paper/matfuse-controllable-material-generation-with","title":"MatFuse: Controllable Material Generation with Diffusion Models","date":"2023-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"giuvecchio/matfuse-sd","path":"src/ldm/util.py","file_url":"https://github.com/giuvecchio/matfuse-sd/blob/HEAD/src/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2308.10040","paper":"/paper/controlcom-controllable-image-composition","title":"ControlCom: Controllable Image Composition using Diffusion Model","date":"2023-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bcmi/controlcom-image-composition","path":"ldm/util.py","file_url":"https://github.com/bcmi/controlcom-image-composition/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2308.09991","paper":"/paper/altdiffusion-a-multilingual-text-to-image","title":"AltDiffusion: A Multilingual Text-to-Image Diffusion Model","date":"2023-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"superhero-7/altdiffuson","path":"src/ldm/util.py","file_url":"https://github.com/superhero-7/altdiffuson/blob/HEAD/src/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2308.06101","paper":"/paper/taming-the-power-of-diffusion-models-for-high","title":"Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow","date":"2023-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bcmi/DCI-VTON-Virtual-Try-On","path":"ldm/util.py","file_url":"https://github.com/bcmi/DCI-VTON-Virtual-Try-On/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2308.02228","paper":"/paper/painterly-image-harmonization-using-diffusion","title":"Painterly Image Harmonization using Diffusion Model","date":"2023-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bcmi/phdiffusion-painterly-image-harmonization","path":"ldm/util.py","file_url":"https://github.com/bcmi/phdiffusion-painterly-image-harmonization/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2308.01546","paper":"/paper/musicldm-enhancing-novelty-in-text-to-music","title":"MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies","date":"2023-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"retrocirce/musicldm","path":"interface/src/latent_diffusion/util.py","file_url":"https://github.com/retrocirce/musicldm/blob/HEAD/interface/src/latent_diffusion/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2307.12499","paper":"/paper/advdiff-generating-unrestricted-adversarial","title":"AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models","date":"2023-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EricDai0/advdiff","path":"ldm/util.py","file_url":"https://github.com/EricDai0/advdiff/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2307.12493","paper":"/paper/tf-icon-diffusion-based-training-free-cross","title":"TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition","date":"2023-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Shilin-LU/TF-ICON","path":"ldm/util.py","file_url":"https://github.com/Shilin-LU/TF-ICON/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2307.08585","paper":"/paper/identity-preserving-aging-of-face-images-via","title":"Identity-Preserving Aging of Face Images via Latent Diffusion Models","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sudban3089/ID-Preserving-Facial-Aging","path":"ldm/util.py","file_url":"https://github.com/sudban3089/ID-Preserving-Facial-Aging/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2306.04632","paper":"/paper/designing-a-better-asymmetric-vqgan-for","title":"Designing a Better Asymmetric VQGAN for StableDiffusion","date":"2023-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"buxiangzhiren/asymmetric_vqgan","path":"ldm/util.py","file_url":"https://github.com/buxiangzhiren/asymmetric_vqgan/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2306.00971","paper":"/paper/vico-detail-preserving-visual-condition-for","title":"ViCo: Plug-and-play Visual Condition for Personalized Text-to-image Generation","date":"2023-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoosz/vico","path":"ldm/util.py","file_url":"https://github.com/haoosz/vico/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2305.16225","paper":"/paper/prospect-expanded-conditioning-for-the","title":"ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models","date":"2023-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyxElsa/ProSpect","path":"ldm/util.py","file_url":"https://github.com/zyxElsa/ProSpect/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2305.02034","paper":"/paper/samrs-scaling-up-remote-sensing-segmentation","title":"SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model","date":"2023-05-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenquanlu/handrefiner","path":"ldm/util.py","file_url":"https://github.com/wenquanlu/handrefiner/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2304.03246","paper":"/paper/inst-inpaint-instructing-to-remove-objects","title":"Inst-Inpaint: Instructing to Remove Objects with Diffusion Models","date":"2023-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abyildirim/inst-inpaint","path":"ldm/util.py","file_url":"https://github.com/abyildirim/inst-inpaint/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2303.18181","paper":"/paper/a-closer-look-at-parameter-efficient-tuning","title":"A Closer Look at Parameter-Efficient Tuning in Diffusion Models","date":"2023-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Xiang-cd/unet-finetune","path":"ldm/util.py","file_url":"https://github.com/Xiang-cd/unet-finetune/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2303.09508","paper":"/paper/ldmvfi-video-frame-interpolation-with-latent","title":"LDMVFI: Video Frame Interpolation with Latent Diffusion Models","date":"2023-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"danielism97/ldmvfi","path":"ldm/util.py","file_url":"https://github.com/danielism97/ldmvfi/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2303.00836","paper":"/paper/generating-initial-conditions-for-ensemble","title":"Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models","date":"2023-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rybchuk/latent-diffusion-3d-atmospheric-boundary-layer","path":"ldm/util.py","file_url":"https://github.com/rybchuk/latent-diffusion-3d-atmospheric-boundary-layer/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2212.04489","paper":"/paper/sine-single-image-editing-with-text-to-image","title":"SINE: SINgle Image Editing with Text-to-Image Diffusion Models","date":"2022-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhang-zx/sine","path":"ldm/util.py","file_url":"https://github.com/zhang-zx/sine/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2211.09800","paper":"/paper/instructpix2pix-learning-to-follow-image","title":"InstructPix2Pix: Learning to Follow Image Editing Instructions","date":"2022-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuduo35/InstructPix2Pix","path":"ldm/util.py","file_url":"https://github.com/xuduo35/InstructPix2Pix/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2210.12965","paper":"/paper/high-resolution-image-editing-via-multi-stage","title":"High-Resolution Image Editing via Multi-Stage Blended Diffusion","date":"2022-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pfnet-research/multi-stage-blended-diffusion","path":"multi-scale-blended-diffusion/ldm/util.py","file_url":"https://github.com/pfnet-research/multi-stage-blended-diffusion/blob/HEAD/multi-scale-blended-diffusion/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2208.13753","paper":"/paper/frido-feature-pyramid-diffusion-for-complex","title":"Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis","date":"2022-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davidhalladay/frido","path":"frido/util.py","file_url":"https://github.com/davidhalladay/frido/blob/HEAD/frido/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2206.02779","paper":"/paper/blended-latent-diffusion","title":"Blended Latent Diffusion","date":"2022-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"omriav/blended-latent-diffusion","path":"ldm/util.py","file_url":"https://github.com/omriav/blended-latent-diffusion/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"2204.06125","paper":"/paper/hierarchical-text-conditional-image","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","date":"2022-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liyinqi/un2clip","path":"ldm/util.py","file_url":"https://github.com/liyinqi/un2clip/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"ijcai2025_1126","paper":null,"title":"arXiv:ijcai2025_1126","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ivcylc/OpenMusic","path":"audioldm_train/utilities/model_util.py","file_url":"https://github.com/ivcylc/OpenMusic/blob/HEAD/audioldm_train/utilities/model_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"aaai_30144","paper":null,"title":"arXiv:aaai_30144","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"kodenii/ORES","path":"TIN/ldm/util.py","file_url":"https://github.com/kodenii/ORES/blob/HEAD/TIN/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"aaai_28503","paper":null,"title":"arXiv:aaai_28503","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yuhongwei22/MFA","path":"ldm/util.py","file_url":"https://github.com/yuhongwei22/MFA/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"aaai_27912","paper":null,"title":"arXiv:aaai_27912","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"danier97/LDMVFI","path":"ldm/util.py","file_url":"https://github.com/danier97/LDMVFI/blob/HEAD/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"Jiang_Diffuse3D_Wide-Angle_3D_Photography_via_Bilateral_Diffusion_ICCV_2023_paper","paper":null,"title":"arXiv:Jiang_Diffuse3D_Wide-Angle_3D_Photography_via_Bilateral_Diffusion_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yutaojiang1/Diffuse3D","path":"BilateralDiffusion/ldm/util.py","file_url":"https://github.com/yutaojiang1/Diffuse3D/blob/HEAD/BilateralDiffusion/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}},{"arxiv_id":"Dombrowski_Foreground-Background_Separation_through_Concept_Distillation_from_Generative_Image_Foundation_Models_ICCV_2023_paper","paper":null,"title":"arXiv:Dombrowski_Foreground-Background_Separation_through_Concept_Distillation_from_Generative_Image_Foundation_Models_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MischaD/fobadiffusion","path":"finetune-stable-diffusion/ldm/util.py","file_url":"https://github.com/MischaD/fobadiffusion/blob/HEAD/finetune-stable-diffusion/ldm/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d72762b700feee6f","mcp_get_code":{"code_sha256":"d72762b700feee6f"}}]}