#diffusionmodels kết quả tìm kiếm

The models leverage multiple open source works including works from Alibaba, BFL, HuggingFace, and research repositories that we acknowledge on the model pages (e.g. pruna.ai/p-image). While these models remain standard diffusion architectures, the key novelty of these…

How to make sure this phenomenon is not happening? Maybe this does not happen at a larger scale ? Happy to hear thoughts from diffusion folks #diffusionmodels #NeurIPS2025

Read more: parsa-ra.github.io/auggen/ #NeurIPS2025 #NeurIPS #DiffusionModels #SyntheticData #GenerativeAI

We discovered how to fix diffusion model's diversity issues using interpretability! It's all in the first time-step!⏱️ Turns out the concepts to be diverse are present in the model - it simply doesn't use them! Checkout our @wacv_official work - we added theoretical evidence👇

Diffusion models are static. After training, they stay the exact same, even after a trillion generations. You have to retrain them, intentionally, for it to learn anything new, and that's an expensive, complex process.

🎉 Excited to share our new NeurIPS paper: FairImagen — a post-hoc debiasing framework for text-to-image diffusion models. Paper & code: arxiv.org/abs/2510.21363 github.com/fuzihaofzh/Fai… #NeurIPS #FairAI #DiffusionModels

Diffusion models are reinterpreted for Maximum Entropy Reinforcement Learning by minimizing KL divergence between diffusion and optimal policies. This yields DiffSAC, DiffPPO, and DiffWP

生成AIで主流のStable Diffusionにおける拡散モデルは、少なくとも絵描き歌じゃないですよ。 ノイズを加えながらそのデータを差分を記録していく方法です。（意訳） Diffusion models　で検索してみてください。

No, again, diffusion models don't make creative decisions. They're deterministic, and they don't even have access to their training data to begin with. You clearly need to read up some. Or take a nap

I've already clearly explained that a diffusion model is a latent map of weights, not a collection of imagery. Thus, the AI is not simply copying existing works, as you imply here. At this point, it doesn't seem like you're engaging in good faith.

It isn't directly derivative, as a diffusion model is a latent map of weights, not a collection of pixels. It'd be like saying that our brains are directly derivative of the works we consume. As I've mentioned, I don't think you actually understand how these tools work.

Ever wonder how diffusion models craft high-res images? 🖼️ They add tiny Gaussian noise over 1000+ steps, then learn to reverse it with a U-Net trained on a reweighted ELBO. Slow? Yes. But the math and tech behind it is pure genius! theaisummer.com/diffusion-mode…

ARMs (Auto regressive models) write one token at a time. But dLLMs sketch the whole thought, then “denoise” it into clean text. This parallelism adds massive perf gains. Fewer steps, fewer stalls, faster output. #LLMs #DiffusionModels

Diffusion models basically train by adding noise and resolving backwards to recreate the original... Having spun up a few models in my day, the nerd in me hopes they retrain on the good shit... The artist in me says F' em!

Language에서 diffusion 모델은 토큰들을 MASK 로 바꾸는 형태의 노이즈를 가한 뒤 이를 복구하는 형태. 문제는 여러 MASK를 동시에 복구하면 품질이 떨어져 매번 한 토큰만 복구해야 함. 그런데 이러면 autoregressive랑 속도 차이가 안남.

Diffusion models in ESGtoolkit + announcements thierrymoudiki.github.io/blog/2023/10/0… #Techtonique #DataScience #Python #rstats #MachineLearning

Except this is not exactly accurate. A diffusion model is a latent map of weights, representing an AI's generalization of concepts. It is not a collection of imagery. The training process would more accurately be compared to a human learning to draw by drawing what they see.

A diffusion model is a latent map of weights, not a collection of imagery. So, your premise here is fundamentally flawed. It seems like you don't actually understand how these tools function.

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…

#diffusionmodels

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

🔥 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

🔬 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

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…

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

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

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

¿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

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…

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

🎉 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

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

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

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

Day 288 ReflectionFlow: Smarter Image Generation Through Self-Reflection 🧠🖼️ Today’s text-to-image models are great, but struggle with complex scenes and fine details. #AI #DiffusionModels #ReflectionFlow #TextToImage #ImageSynthesis #GenAI #FLUX #MachineLearning

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

From RNA to Proteins! 🧬 Scientists enhance #DiffusionModels with #ReinforcementLearning algorithms to meet specific biological goals, revolutionizing how we solve complex biological problems. Discover More: cbirt.net/optimizing-dif… #Bioinformatics #Docking #ML #AI #sciencenews

📣 [High View Paper] Find our newly published article here! Artificial-Intelligence-Generated Content with Diffusion Models: A Literature Review buff.ly/4er09af #generativemodels; #computervision; #diffusionmodels; #AI #MDPIOpenAccess @ComSciMath_Mdpi