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Hugging Face - Blog

Waypoint-1.5: Higher-Fidelity Interactive Worlds for Everyday GPUs ALTK‑Evolve: On‑the‑Job Learning for AI Agents Safetensors is Joining the PyTorch Foundation Holo3: Breaking the Computer Use Frontier Any Custom Frontend with Gradio's Backend A New Framework for Evaluating Voice Agents (EVA) Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations One-Shot Any Web App with Gradio's gr.HTML CUGA on Hugging Face: Democratizing Configurable AI Agents New in llama.cpp: Model Management Building Deep Research: How we Achieved State of the Art OVHcloud on Hugging Face Inference Providers 🔥 20x Faster TRL Fine-tuning with RapidFire AI Building for an Open Future - our new partnership with Google Cloud Aligning to What? Rethinking Agent Generalization in MiniMax M2 Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac Sentence Transformers is joining Hugging Face! Unlock the power of images with AI Sheets Supercharge your OCR Pipelines with Open Models Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face Get your VLM running in 3 simple steps on Intel CPUs Nemotron-Personas-India: Synthesized Data for Sovereign AI Introducing RTEB: A New Standard for Retrieval Evaluation Accelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models VibeGame: Exploring Vibe Coding Games Nemotron-Personas-Japan: ソブリン AI のための合成データセット Swift Transformers Reaches 1.0 – and Looks to the Future Smol2Operator: Post-Training GUI Agents for Computer Use SyGra: The One-Stop Framework for Building Data for LLMs and SLMs Gaia2 and ARE: Empowering the community to study agents Scaleway on Hugging Face Inference Providers 🔥 Democratizing AI Safety with RiskRubric.ai Public AI on Hugging Face Inference Providers 🔥 `LeRobotDataset:v3.0`: Bringing large-scale datasets to `lerobot` Visible Watermarking with Gradio Introducing the Palmyra-mini family: Powerful, lightweight, and ready to reason! 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Transformers backend integration in SGLang (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware Groq on Hugging Face Inference Providers 🔥 How Long Prompts Block Other Requests - Optimizing LLM Performance Learn the Hugging Face Kernel Hub in 5 Minutes Convert Transformers to ONNX with Hugging Face Optimum Intel and Hugging Face Partner to Democratize Machine Learning Hardware Acceleration Director of Machine Learning Insights [Part 3: Finance Edition] The Annotated Diffusion Model Deep Q-Learning with Space Invaders Graphcore and Hugging Face Launch New Lineup of IPU-Ready Transformers Introducing Pull Requests and Discussions 🥳 Efficient Table Pre-training without Real Data: An Introduction to TAPEX An Introduction to Q-Learning Part 2/2 How Sempre Health is leveraging the Expert Acceleration Program to accelerate their ML roadmap
Diffusion Models Live Event
Lewis Tunstall, Jonathan Whitaker · 2022-11-25 · via Hugging Face - Blog

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We are excited to share that the Diffusion Models Class with Hugging Face and Jonathan Whitaker will be released on November 28th 🥳! In this free course, you will learn all about the theory and application of diffusion models -- one of the most exciting developments in deep learning this year. If you've never heard of diffusion models, here's a demo to give you a taste of what they can do:

To go with this release, we are organising a live community event on November 30th to which you are invited! The program includes exciting talks from the creators of Stable Diffusion, researchers at Stability AI and Meta, and more!

To register, please fill out this form. More details on the speakers and talks are provided below.

Live Talks

The talks will focus on a high-level presentation of diffusion models and the tools we can use to build applications with them.

David Ha: Collective Intelligence and Creative AI

David Ha is the Head of Strategy at Stability AI. He previously worked as a Research Scientist at Google, working in the Brain team in Japan. His research interests include complex systems, self-organization, and creative applications of machine learning. Prior to joining Google, He worked at Goldman Sachs as a Managing Director, where he co-ran the fixed-income trading business in Japan. He obtained undergraduate and masters degrees from the University of Toronto, and a PhD from the University of Tokyo.

Devi Parikh: Make-A-Video: Diffusion Models for Text-to-Video Generation without Text-Video Data

Devi Parikh is a Research Director at the Fundamental AI Research (FAIR) lab at Meta, and an Associate Professor in the School of Interactive Computing at Georgia Tech. She has held visiting positions at Cornell University, University of Texas at Austin, Microsoft Research, MIT, Carnegie Mellon University, and Facebook AI Research. She received her M.S. and Ph.D. degrees from the Electrical and Computer Engineering department at Carnegie Mellon University in 2007 and 2009 respectively. Her research interests are in computer vision, natural language processing, embodied AI, human-AI collaboration, and AI for creativity.

Patrick Esser: Food for Diffusion

Patrick Esser is a Principal Research Scientist at Runway, leading applied research efforts including the core model behind Stable Diffusion, otherwise known as High-Resolution Image Synthesis with Latent Diffusion Models.

Justin Pinkney: Beyond text - giving Stable Diffusion new abilities

Justin is a Senior Machine Learning Researcher at Lambda Labs working on image generation and editing, particularly for artistic and creative applications. He loves to play and tweak pre-trained models to add new capabilities to them, and is probably best known for models like: Toonify, Stable Diffusion Image Variations, and Text-to-Pokemon.

Apolinário Passos: DALL-E 2 is cool but... what will come after the generative media hype?

Apolinário Passos is a Machine Learning Art Engineer at Hugging Face and an artist who focuses on generative art and generative media. He founded the platform multimodal.art and the corresponding Twitter account, and works on the organization, aggregation, and platformization of open-source generative media machine learning models.