#diffusionmodels 搜尋結果

That's... all wrong. Diffusion models are trained to learn patterns, both visual *and* conceptual. While they are trained on existing images, afterward they are able to create their own, chiseling them entirely out of gaussian noise, without even referencing any other images.

📢 Nov 24 (Mon): Diffusion Beats AR: Controllable Generation Discrete diffusion models offer far greater control over the generation process, making them a strictly more desirable alternative to autoregressive models. paper: arxiv.org/abs/2412.17780 presenters: Sophia Tang…

#𝗠𝗲𝘁𝗮𝗔𝗜 #3𝗗𝗥𝗲𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 #𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝗩𝗶𝘀𝗶𝗼𝗻 #𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲𝗔𝗜 #𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻𝗠𝗼𝗱𝗲𝗹𝘀 #𝗔𝗥 #𝗩𝗥 #𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 #𝗢𝗽𝗲𝗻𝗦𝗼𝘂𝗿𝗰𝗲 #𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻

NVIDIA's kernel optimizations pushed diffusion model training speeds into a new league. Creativity just got faster. #NVIDIAAI #DiffusionModels #MLTraining #AIPerformance

Diffusion Models History 1. Context: Why Diffusion Models? The 2015 paper “Deep Unsupervised Learning Using Nonequilibrium Thermodynamics” introduced diffusion models. At the time, deep learning was focused on classification, not generative modeling. Diffusion was a very…

“DiffGR: A Discrete Diffusion‑Based Model for Personalised Recommendation by Reconstructing User‑Item Bipartite Graphs” Meet DiffGR here 👇 link.springer.com/chapter/10.100… #GraphML #Recommendation #DiffusionModels

nice abridged summary of diffusion vs LLMs/autoregression models diffusion models have lagged behind in research maturity but purely from a scalability pov diffusion models are the way fwd - smaller sizes possible + relatively more quantizable

It can't do what you claim it does, because a diffusion model is not a collection of imagery. It is a latent map of weights representing an AI's generalization of concepts. It would be more accurate to compare training AI to a human learning to draw by drawing what they see.

I feel like you have a shallow understanding of this technology. A diffusion model is a latent map of weights, not a collection of imagery. It would be more accurate to compare the training process to a human learning to draw by drawing what they see. The AI generalizes concepts.

🚀 Developers don’t think in a straight line, and neither should AI ⚙️ Diffusion models generate code out of order, refining as they go – closer to how real coding happens. Learn more: jb.gg/ovup90

5/7 "Diffusion"? Latin diffundere ("pour out/apart"). In diffusion models (e.g., Stable Diffusion), it's spreading noise then reversing to generate. The "dis-" prefix with "difference," like pouring distinctions. GenAI is a controlled "pouring" of variance. #DiffusionModels

Here's an updated version of my talk about diffusion models, recorded at @M2lSchool 2025. It covers the basics from an intuitive perspective, as well as guidance, spectral autoregression, latents, etc. 📺 video: youtube.com/watch?v=R4VWB9… 🖼️ slides: docs.google.com/presentation/d…

Exciting news! Avisense’s latest paper “Conditional Diffusion Models: A Survey of Techniques, Applications, and Challenges” has been published in the IEEE ACCESS Journal! 🎉📚 #AI #Research #DiffusionModels #IEEEACCESS

No, it isn't. A diffusion model is a latent map of weights, representing an artificial neural network's generalization of concepts. It isn't a collection of imagery. It'd be more accurate to compare the training process to a human learning to draw by drawing what they see.

Diffolio: A Diffusion Model for Multivariate Probabilistic Financial Time-Series Forecasting and Portfolio Construction arxiv.org/abs/2511.07014…

🔬 Excited to share the publication "Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging". 👉 brnw.ch/21wWRlr #MedicalImaging #ZeroShotLearning #DiffusionModels #DeepLearning #ImageSegmentation

#diffusionmodels

Honored to deliver a keynote at DAGM GCPR 2025 (@gcpr_by_dagm) today, sharing our team’s recent work on fairness and controllability in GenAI. It was inspiring to engage with brilliant researchers advancing the frontiers of #ComputerVision, #DiffusionModels and #FairAI

Excited to share our NeurIPS 2025 paper Dynamic Diffusion Schrödinger Bridge in Astrophysical Observational Inversions! #neurips2025 #DiffusionModels #AstroML In this work, we introduce Astro-DSB, a novel diffusion bridge-based approach designed to tackle astrophysical inversion…

Despite the remarkable empirical success of #diffusion models, their fundamental mechanisms are still poorly understood. A new article on the SIAM News blog explores important questions about the #generalizability of #DiffusionModels: siam.org/publications/s…

🔥 Revolutionizing #DiffusionModels! 🔥 Unlike RL methods (e.g., Diffusion-DPO) or costly Diffusion #InferenceScaling, our Diffusion-Sharpening: ⚡ Converges faster during training ⚡ Slashes inference costs ⚡ Excels in text alignment, compositionality, and human preferences

Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models Somepalli et al.: arxiv.org/abs/2212.03860 #Artificialintelligence #Deeplearning #DiffusionModels

📢 Our CVPR’25 Oral work DiffFNO bridges diffusion models & neural operators for arbitrary-scale super-resolution. 💡 2–4 dB PSNR gain, faster inference, elegant spectral–spatial fusion. 🔗 arxiv.org/abs/2411.09911 #DiffusionModels #FNO #CVPR2025 #AIResearch

¿Quieres conocer más sobre #DiffusionModels, capaces de crear imágenes a partir de ruido mediante #denoising y aprendizaje automático? Podrás hacerlo desde un nuevo Madrid #MachineLearning Meetup. 🗓️8/02 🔗meetup.com/madrid-machine… #somosUPM #deeplearningc @urjc @MADRID

A tad late to the party, but happy to share that CycleNet has been accepted to #NeurIPS2023 @NeurIPSConf! Consistency has been a pain in text-guided image editing with #DiffusionModels and here is our solution to guarantee cycle consistency...🧵[1/n] 📍cyclenetweb.github.io

Very proud of my students' contributions at @icmlconf! Our 7 papers cover the power of #LLMs, #DataCentricAI, #DiffusionModels, #Meta-Learning, #Reinforcement Learning & more! All in service of our #RealityCentricAIagenda! Read all about our papers here: vanderschaar-lab.com/van-der-schaar…

Join us at #SIIM23 for @khosravi_bardia's presentation on conditional radiograph generation and learn what this video represents? #GenerativeAl #DiffusionModels Don't miss this opportunity! ⏰ June 16th, 8:00 AM CST 🏢 R9 / L3 @SIIM_Tweets

🎉 Thrilled to share our latest work on solving inverse problems via diffusion-based priors — without heuristic approximations of the measurement matching score! 📄 Link: arxiv.org/abs/2506.03979 (1/6) #DiffusionModels #InverseProblems #Guidance #SequentialMonteCarlo

Stable Target Field for Reduced Variance Score Estimation in Diffusion Models Xu et al.: arxiv.org/abs/2302.00670 #Artificialintelligence #DeepLearning #DiffusionModels

The Physics Principle That Inspired Modern AI Art bit.ly/3ZNd7rw via @QuantaMagazine #AIart #diffusionmodels

Reverse Stable Diffusion: What prompt was used to generate this image? #stablediffusion #diffusionmodels

You can generate any image you can imagine. Just say the words. Learn about how to use Diffusion based models for Image Generation. learnopencv.com/image-generati… #stablediffusion #imagegeneration #diffusionmodels #computervision #ai #deeplearning #machinelearning

1/5 GoodDrag: A groundbreaking study that explores good practices for drag editing with diffusion models. This research aims to enhance the user experience and empower creators in the realm of image editing. #DiffusionModels #ImageEditing

Check this newly published article "Ultra-Low Bitrate Predictive Portrait #VideoCompression with #DiffusionModels" at brnw.ch/21wW2pC Authors: Xinyi Chen et al. #mdpisymmetry @ComSciMath_Mdpi

Paper alert! Our new paper entitled "Compensation Sampling for Improved Convergence in #DiffusionModels" (by #huilu, #ronaldpoppe and me) will be presented at #ECCV2024. Paper: arxiv.org/abs/2312.06285 Code: github.com/hotfinda/Compe… @UUBeta @IAPR_TC12