{"url":"/method/diffusion","slug":"diffusion","name":"Diffusion","full_name":"Diffusion","full_name_withheld":false,"description_markdown":"Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).","description_state":"present","introduced_year":null,"introduced_by":{"title":"Denoising Diffusion Probabilistic Models","paper":"/paper/denoising-diffusion-probabilistic-models","first_author":"Jonathan Ho","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/denoising-diffusion-probabilistic-models"},"source":{"url":"https://arxiv.org/abs/2006.11239v2","title":"Denoising Diffusion Probabilistic Models","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Natural Language Processing","area_id":"natural-language-processing","collection":"Language Models","url":"/methods/category/language-models","pwc_aliases":[]},{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Generation Models","url":"/methods/category/image-generation-models","pwc_aliases":[]}],"n_papers_tagged":13848,"archive_num_papers":13850,"papers_newest_first":[{"paper":null,"title":"Similarity-Guided Diffusion for Contrastive Sequential Recommendation","date":"2025-07-16","arxiv_id":"2507.11866","n_code_links":0,"syntology":null},{"paper":null,"title":"AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air","date":"2025-07-15","arxiv_id":"2507.11515","n_code_links":0,"syntology":null},{"paper":null,"title":"CATVis: Context-Aware Thought Visualization","date":"2025-07-15","arxiv_id":"2507.11522","n_code_links":0,"syntology":null},{"paper":null,"title":"Diffusion Decoding for Peptide De Novo Sequencing","date":"2025-07-15","arxiv_id":"2507.10955","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-the-robustness-of-tractoracle","title":"Exploring the robustness of TractOracle methods in RL-based tractography","date":"2025-07-15","arxiv_id":"2507.11486","n_code_links":1,"syntology":null},{"paper":null,"title":"HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing","date":"2025-07-15","arxiv_id":"2507.11474","n_code_links":0,"syntology":null},{"paper":"/paper/implementing-adaptations-for-vision","title":"Implementing Adaptations for Vision AutoRegressive Model","date":"2025-07-15","arxiv_id":"2507.11441","n_code_links":1,"syntology":{"ran":0,"of":8,"unverified":8,"pointer_only":8}},{"paper":null,"title":"Latent Space Consistency for Sparse-View CT Reconstruction","date":"2025-07-15","arxiv_id":"2507.11152","n_code_links":0,"syntology":null},{"paper":null,"title":"When and Where do Data Poisons Attack Textual Inversion?","date":"2025-07-11","arxiv_id":"2507.10578","n_code_links":0,"syntology":null},{"paper":"/paper/a-survey-on-latent-reasoning","title":"A Survey on Latent Reasoning","date":"2025-07-08","arxiv_id":"2507.06203","n_code_links":1,"syntology":null},{"paper":null,"title":"ADMC: Attention-based Diffusion Model for Missing Modalities Feature Completion","date":"2025-07-08","arxiv_id":"2507.05624","n_code_links":0,"syntology":null},{"paper":null,"title":"Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques","date":"2025-07-08","arxiv_id":"2507.06275","n_code_links":0,"syntology":null},{"paper":"/paper/cultureclip-empowering-clip-with-cultural","title":"CultureCLIP: Empowering CLIP with Cultural Awareness through Synthetic Images and Contextualized Captions","date":"2025-07-08","arxiv_id":"2507.06210","n_code_links":1,"syntology":{"ran":0,"of":11,"unverified":11,"pointer_only":11}},{"paper":null,"title":"Diffusion Dataset Condensation: Training Your Diffusion Model Faster with Less Data","date":"2025-07-08","arxiv_id":"2507.05914","n_code_links":0,"syntology":null},{"paper":null,"title":"DreamArt: Generating Interactable Articulated Objects from a Single Image","date":"2025-07-08","arxiv_id":"2507.05763","n_code_links":0,"syntology":null},{"paper":"/paper/few-shot-learning-by-explicit-physics","title":"Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport","date":"2025-07-08","arxiv_id":"2507.06062","n_code_links":1,"syntology":null},{"paper":null,"title":"LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion","date":"2025-07-08","arxiv_id":"2507.05678","n_code_links":0,"syntology":null},{"paper":"/paper/modern-methods-in-associative-memory","title":"Modern Methods in Associative Memory","date":"2025-07-08","arxiv_id":"2507.06211","n_code_links":2,"syntology":{"ran":0,"of":6,"unverified":6,"pointer_only":0}},{"paper":"/paper/normalizing-diffusion-kernels-with-optimal","title":"Normalizing Diffusion Kernels with Optimal Transport","date":"2025-07-08","arxiv_id":"2507.06161","n_code_links":0,"syntology":{"ran":0,"of":26,"unverified":26,"pointer_only":0}},{"paper":"/paper/omni-video-democratizing-unified-video","title":"Omni-Video: Democratizing Unified Video Understanding and Generation","date":"2025-07-08","arxiv_id":"2507.06119","n_code_links":1,"syntology":{"ran":3,"of":18,"unverified":15,"pointer_only":18}},{"paper":"/paper/prompt-free-conditional-diffusion-for-multi","title":"Prompt-Free Conditional Diffusion for Multi-object Image Augmentation","date":"2025-07-08","arxiv_id":"2507.06146","n_code_links":1,"syntology":null},{"paper":"/paper/scoreadv-score-based-targeted-generation-of","title":"ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion Models","date":"2025-07-08","arxiv_id":"2507.06078","n_code_links":1,"syntology":null},{"paper":"/paper/semi-supervised-defect-detection-via","title":"Semi-Supervised Defect Detection via Conditional Diffusion and CLIP-Guided Noise Filtering","date":"2025-07-08","arxiv_id":"2507.05588","n_code_links":1,"syntology":null},{"paper":null,"title":"SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning","date":"2025-07-08","arxiv_id":"2507.05798","n_code_links":0,"syntology":null},{"paper":"/paper/t-lora-single-image-diffusion-model","title":"T-LoRA: Single Image Diffusion Model Customization Without Overfitting","date":"2025-07-08","arxiv_id":"2507.05964","n_code_links":1,"syntology":{"ran":0,"of":14,"unverified":14,"pointer_only":0}},{"paper":null,"title":"TextPixs: Glyph-Conditioned Diffusion with Character-Aware Attention and OCR-Guided Supervision","date":"2025-07-08","arxiv_id":"2507.06033","n_code_links":0,"syntology":null},{"paper":null,"title":"Tora2: Motion and Appearance Customized Diffusion Transformer for Multi-Entity Video Generation","date":"2025-07-08","arxiv_id":"2507.05963","n_code_links":0,"syntology":null},{"paper":null,"title":"Towards Solar Altitude Guided Scene Illumination","date":"2025-07-08","arxiv_id":"2507.05812","n_code_links":0,"syntology":null},{"paper":null,"title":"Unconditional Diffusion for Generative Sequential Recommendation","date":"2025-07-08","arxiv_id":"2507.06121","n_code_links":0,"syntology":null},{"paper":"/paper/ai-driven-cytomorphology-image-synthesis-for","title":"AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics","date":"2025-07-07","arxiv_id":"2507.05063","n_code_links":1,"syntology":null}],"papers_shown":30,"tasks":[{"task":"/task/image-generation","name":"Image Generation","papers":2525},{"task":"/task/denoising","name":"Denoising","papers":2447},{"task":"/task/video-generation","name":"Video Generation","papers":666},{"task":"/task/diversity","name":"Diversity","papers":549},{"task":"/task/text-to-image-generation","name":"Text-to-Image Generation","papers":543},{"task":"/task/text-to-image-generation-1","name":"Text to Image Generation","papers":485},{"task":"/task/super-resolution","name":"Super-Resolution","papers":399},{"task":"/task/object","name":"Object","papers":345},{"task":"/task/model","name":"model","papers":333},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":326},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":318},{"task":"/task/language-modelling","name":"Language Modelling","papers":313},{"task":"/task/decoder","name":"Decoder","papers":310},{"task":"/task/segmentation","name":"Segmentation","papers":283},{"task":"/task/3d-generation","name":"3D Generation","papers":263},{"task":"/task/language-modeling","name":"Language Modeling","papers":235},{"task":"/task/attribute","name":"Attribute","papers":222},{"task":null,"name":"GPU","papers":203},{"task":"/task/image-restoration","name":"Image Restoration","papers":201},{"task":"/task/text-to-3d","name":"Text to 3D","papers":197}],"tasks_shown":20,"n_tasks":1474,"usage_by_year":[{"year":"2020","papers":167},{"year":"2021","papers":434},{"year":"2022","papers":857},{"year":"2023","papers":3354},{"year":"2024","papers":5829},{"year":"2025","papers":3207}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/diffusion"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}