{"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/chunk","entry":"chunk","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":52,"n_papers_ran":48,"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":11,"n_samples_ran":7,"n_samples_fingerprinted":3,"n_places":52,"n_places_pointer_only":35,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":4,"unverified":4},"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":"2504.01855","paper":"/paper/enhanced-diffusion-sampling-via-extrapolation","title":"Enhanced Diffusion Sampling via Extrapolation with Multiple ODE Solutions","date":"2025-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"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":"makeup_inference_model2.py","file_url":"https://github.com/Snowfallingplum/SHMT/blob/HEAD/makeup_inference_model2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2410.15618","paper":"/paper/erasing-undesirable-concepts-in-diffusion","title":"Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation","date":"2024-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tuananhbui89/Erasing-Adversarial-Preservation","path":"generate_images_ldm.py","file_url":"https://github.com/tuananhbui89/Erasing-Adversarial-Preservation/blob/HEAD/generate_images_ldm.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2410.03489","paper":"/paper/gradient-based-jailbreak-images-for","title":"Gradient-based Jailbreak Images for Multimodal Fusion Models","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/multimodal-fusion-jailbreaks","path":"src/fusion_jailbreaks/utils.py","file_url":"https://github.com/facebookresearch/multimodal-fusion-jailbreaks/blob/HEAD/src/fusion_jailbreaks/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"2230390d89a3e149","mcp_get_code":{"code_sha256":"2230390d89a3e149"}},{"arxiv_id":"2408.05868","paper":"/paper/lawa-using-latent-space-for-in-generation","title":"LaWa: Using Latent Space for In-Generation Image Watermarking","date":"2024-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2407.19547","paper":"/paper/temporal-feature-matters-a-framework-for","title":"Temporal Feature Matters: A Framework for Diffusion Model Quantization","date":"2024-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"modeltc/tfmq-dm","path":"txt2img.py","file_url":"https://github.com/modeltc/tfmq-dm/blob/HEAD/txt2img.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2407.13998","paper":"/paper/rag-qa-arena-evaluating-domain-robustness-for","title":"RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering","date":"2024-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"awslabs/rag-qa-arena","path":"code/utils.py","file_url":"https://github.com/awslabs/rag-qa-arena/blob/HEAD/code/utils.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":"5ecb780bdf037ba8","mcp_get_code":{"code_sha256":"5ecb780bdf037ba8"}},{"arxiv_id":"2407.03588","paper":"/paper/feedback-guided-domain-synthesis-with-multi","title":"FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain Generalization","date":"2024-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2403.03463","paper":"/paper/flame-diffuser-grounded-wildfire-image","title":"FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion","date":"2024-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIS-Clemson/FLAME_SD","path":"Flame_diffuser_binary_mask.py","file_url":"https://github.com/AIS-Clemson/FLAME_SD/blob/HEAD/Flame_diffuser_binary_mask.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"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":"nupurkmr9/concept-ablation","path":"compvis/sample.py","file_url":"https://github.com/nupurkmr9/concept-ablation/blob/HEAD/compvis/sample.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2402.03666","paper":"/paper/quest-low-bit-diffusion-model-quantization","title":"QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hatchetProject/QuEST","path":"txt2img.py","file_url":"https://github.com/hatchetProject/QuEST/blob/HEAD/txt2img.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2401.06187","paper":"/paper/scissorhands-scrub-data-influence-via","title":"Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2312.13236","paper":"/paper/diffusion-models-with-learned-adaptive-noise","title":"Diffusion Models With Learned Adaptive Noise","date":"2023-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2312.02145","paper":"/paper/repurposing-diffusion-based-image-generators","title":"Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"damaggu/tadp","path":"TADP/tadp_seg.py","file_url":"https://github.com/damaggu/tadp/blob/HEAD/TADP/tadp_seg.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2312.00853","paper":"/paper/motion-guided-latent-diffusion-for-temporally","title":"Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2310.20329","paper":"/paper/instructcoder-empowering-language-models-for","title":"InstructCoder: Instruction Tuning Large Language Models for Code Editing","date":"2023-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qishenghu/CodeInstruct","path":"src/request_helper.py","file_url":"https://github.com/qishenghu/CodeInstruct/blob/HEAD/src/request_helper.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"18f3d41c3a3ca45f","mcp_get_code":{"code_sha256":"18f3d41c3a3ca45f"}},{"arxiv_id":"2310.12004","paper":"/paper/image-super-resolution-via-latent-diffusion-a","title":"Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach","date":"2023-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2310.10640","paper":"/paper/llm-blueprint-enabling-text-to-image","title":"LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hananshafi/llmblueprint","path":"composition_module/my_paint_by_example.py","file_url":"https://github.com/hananshafi/llmblueprint/blob/HEAD/composition_module/my_paint_by_example.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2309.13415","paper":"/paper/dream-the-impossible-outlier-imagination-with-1","title":"Dream the Impossible: Outlier Imagination with Diffusion Models","date":"2023-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2309.05569","paper":"/paper/iti-gen-inclusive-text-to-image-generation","title":"ITI-GEN: Inclusive Text-to-Image Generation","date":"2023-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"humansensinglab/ITI-GEN","path":"generation.py","file_url":"https://github.com/humansensinglab/ITI-GEN/blob/HEAD/generation.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2308.10882","paper":"/paper/giraffe-adventures-in-expanding-context","title":"Giraffe: Adventures in Expanding Context Lengths in LLMs","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abacusai/long-context","path":"python/train/finetune_context.py","file_url":"https://github.com/abacusai/long-context/blob/HEAD/python/train/finetune_context.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":"0e92b47ad30b6534","mcp_get_code":{"code_sha256":"0e92b47ad30b6534"}},{"arxiv_id":"2308.10510","paper":"/paper/frequency-compensated-diffusion-model-for","title":"Frequency Compensated Diffusion Model for Real-scene Dehazing","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"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":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2307.10829","paper":"/paper/exact-diffusion-inversion-via-bi-directional","title":"Exact Diffusion Inversion via Bi-directional Integration Approximation","date":"2023-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"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":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2307.00619","paper":"/paper/solving-linear-inverse-problems-provably-via-1","title":"Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models","date":"2023-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2306.04675","paper":"/paper/exposing-flaws-of-generative-model-evaluation-1","title":"Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models","date":"2023-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"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":"txt2img.py","file_url":"https://github.com/buxiangzhiren/asymmetric_vqgan/blob/HEAD/txt2img.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2304.08818","paper":"/paper/align-your-latents-high-resolution-video","title":"Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models","date":"2023-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"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":"sample.py","file_url":"https://github.com/Xiang-cd/unet-finetune/blob/HEAD/sample.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2303.13516","paper":"/paper/ablating-concepts-in-text-to-image-diffusion","title":"Ablating Concepts in Text-to-Image Diffusion Models","date":"2023-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2303.10137","paper":"/paper/a-recipe-for-watermarking-diffusion-models","title":"A Recipe for Watermarking Diffusion Models","date":"2023-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunqing-me/watermarkdm","path":"sd_watermark/stable_txt2img.py","file_url":"https://github.com/yunqing-me/watermarkdm/blob/HEAD/sd_watermark/stable_txt2img.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2302.07121","paper":"/paper/universal-guidance-for-diffusion-models","title":"Universal Guidance for Diffusion Models","date":"2023-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2301.00704","paper":"/paper/muse-text-to-image-generation-via-masked","title":"Muse: Text-To-Image Generation via Masked Generative Transformers","date":"2023-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2212.00932","paper":"/paper/objectstitch-generative-object-compositing","title":"ObjectStitch: Generative Object Compositing","date":"2022-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2211.13227","paper":"/paper/paint-by-example-exemplar-based-image-editing","title":"Paint by Example: Exemplar-based Image Editing with Diffusion Models","date":"2022-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"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":"stable_txt2img.py","file_url":"https://github.com/xuduo35/InstructPix2Pix/blob/HEAD/stable_txt2img.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2210.09477","paper":"/paper/unitune-text-driven-image-editing-by-fine","title":"UniTune: Text-Driven Image Editing by Fine Tuning a Diffusion Model on a Single Image","date":"2022-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuduo35/UniTune","path":"gr_stable_txt2img.py","file_url":"https://github.com/xuduo35/UniTune/blob/HEAD/gr_stable_txt2img.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2210.06886","paper":"/paper/imaginarynet-learning-object-detectors","title":"ImaginaryNet: Learning Object Detectors without Real Images and Annotations","date":"2022-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2210.01185","paper":"/paper/contragen-effective-contrastive-learning-for","title":"ContraCLM: Contrastive Learning For Causal Language Model","date":"2022-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-science/contraclm","path":"preprocess/preprocess_bq.py","file_url":"https://github.com/amazon-science/contraclm/blob/HEAD/preprocess/preprocess_bq.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"290fcdb620dd0473","mcp_get_code":{"code_sha256":"290fcdb620dd0473"}},{"arxiv_id":"2209.12330","paper":"/paper/personalizing-text-to-image-generation-via","title":"Personalizing Text-to-Image Generation via Aesthetic Gradients","date":"2022-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vicgalle/stable-diffusion-aesthetic-gradients","path":"scripts/txt2img.py","file_url":"https://github.com/vicgalle/stable-diffusion-aesthetic-gradients/blob/HEAD/scripts/txt2img.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2208.12242","paper":"/paper/dreambooth-fine-tuning-text-to-image","title":"DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation","date":"2022-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2206.13843","paper":"/paper/manu-a-cloud-native-vector-database","title":"Manu: A Cloud Native Vector Database Management System","date":null,"month_inferred_from_arxiv_id":"2022-06","title_source":"archive","repo":"milvus-io/milvus","path":"internal/core/build-support/lintutils.py","file_url":"https://github.com/milvus-io/milvus/blob/HEAD/internal/core/build-support/lintutils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"30851f17e68cade0","mcp_get_code":{"code_sha256":"30851f17e68cade0"}},{"arxiv_id":"2204.11824","paper":"/paper/retrieval-augmented-diffusion-models","title":"Semi-Parametric Neural Image Synthesis","date":"2022-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2112.10752","paper":"/paper/high-resolution-image-synthesis-with-latent","title":"High-Resolution Image Synthesis with Latent Diffusion Models","date":"2021-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"2106.14490","paper":"/paper/making-images-real-again-a-comprehensive","title":"Making Images Real Again: A Comprehensive Survey on Deep Image Composition","date":"2021-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"arxiv_id":"1904.02882","paper":"/paper/libritts-a-corpus-derived-from-librispeech","title":"LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech","date":null,"month_inferred_from_arxiv_id":"2019-04","title_source":"archive","repo":"skakouros/bert-prosody","path":"convert_dataset.py","file_url":"https://github.com/skakouros/bert-prosody/blob/HEAD/convert_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f50aeb5281088ff3","mcp_get_code":{"code_sha256":"f50aeb5281088ff3"}},{"arxiv_id":"1805.08574","paper":"/paper/breaking-the-activation-function-bottleneck","title":"Breaking the Activation Function Bottleneck through Adaptive Parameterization","date":"2018-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"flennerhag/alstm","path":"alstm/utils.py","file_url":"https://github.com/flennerhag/alstm/blob/HEAD/alstm/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"9b1460555de721f1","mcp_get_code":{"code_sha256":"9b1460555de721f1"}},{"arxiv_id":"1711.00043","paper":"/paper/unsupervised-machine-translation-using","title":"Unsupervised Machine Translation Using Monolingual Corpora Only","date":"2017-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"barnerwothers/MUSE","path":"src/alignment_functions.py","file_url":"https://github.com/barnerwothers/MUSE/blob/HEAD/src/alignment_functions.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"40cdf0f2f7b382ee","mcp_get_code":{"code_sha256":"40cdf0f2f7b382ee"}},{"arxiv_id":"1710.04087","paper":"/paper/word-translation-without-parallel-data","title":"Word Translation Without Parallel Data","date":"2017-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"40cdf0f2f7b382ee","mcp_get_code":{"code_sha256":"40cdf0f2f7b382ee"}},{"arxiv_id":"1705.09792","paper":"/paper/deep-complex-networks","title":"Deep Complex Networks","date":"2017-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ispamm/htorch","path":"htorch/quaternion.py","file_url":"https://github.com/ispamm/htorch/blob/HEAD/htorch/quaternion.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d45ce81b9e40c437","mcp_get_code":{"code_sha256":"d45ce81b9e40c437"}},{"arxiv_id":"Huang_TFMQ-DM_Temporal_Feature_Maintenance_Quantization_for_Diffusion_Models_CVPR_2024_paper","paper":null,"title":"arXiv:Huang_TFMQ-DM_Temporal_Feature_Maintenance_Quantization_for_Diffusion_Models_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ModelTC/TFMQ-DM","path":"txt2img.py","file_url":"https://github.com/ModelTC/TFMQ-DM/blob/HEAD/txt2img.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8241c0562bc710fd","mcp_get_code":{"code_sha256":"8241c0562bc710fd"}}]}