Hugging Face reposted this
Flux.1-Dev like images but in fewer steps. Merging code (very simple), inference code, merged params: https://lnkd.in/gCWrTv3U Enjoy the Monday 🤗
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Hugging Face reposted this
Flux.1-Dev like images but in fewer steps. Merging code (very simple), inference code, merged params: https://lnkd.in/gCWrTv3U Enjoy the Monday 🤗
Hugging Face reposted this
FLUX.1 [schnell] is blowing minds across the SD & MidJourney art community.🤯 All attached images are FLUX-generated by the community and they are unbelievable. For example, some of these images actually look like real photographs! - Try out the new SOTA text-to-Image model today with a Gradio demo on Hugging Face Spaces for free!! - Flux Prompt handling is way better SD3 & Midjourney. - You might find it tough to believe these generated images are from Flux and not MidJourney ! - There are couple examples of artists generating character-sheets with similar looking characters out of the box - Some examples show FLUX being used as a Jewelry Design tool!😍 - FLUX -> More realistic and more detailed images than SD3 Gradio demo on Hugging Face Spaces: https://lnkd.in/dQSXrRpr
Hugging Face reposted this
🤖 Hacker-in-Residence @ Voxel51| 👨🏽💻 AI/ML Engineer | 👷🏽♀️ Developer Advocate | Building🏗️-Shipping🚢-Sharing🚀
Think you know the key to powerful AI? It's not just about fancy models; it's about the data. Voxel51's first-ever Data-Centric AI Competition on Hugging Face Spaces challenges you to prove that data eats models for lunch! The Challenge: Curate, Don't Just Compute Your mission is to create a smaller, more efficient subset of our provided dataset (65,986 images, 43 object classes) that maintains or improves the performance of a YOLOv8m object detection model. This means: ✅ Removing redundant or unhelpful data ✅ Fixing labeling errors ✅ Applying data augmentation 🚫 Don't use external data, add new annotations, or generate synthetic images. 🛠️Your Toolkit • FiftyOne: An open-source tool for dataset curation and analysis (tutorial provided!) • YOLOv8m model: From the Ultralytics Model Zoo • Your ingenuity! Winning Formula We're looking for the sweet spot between dataset size and model performance. Our scoring metric reflects this: Score = (mAP * log(N)) / N Where: mAP: Mean Average Precision on a hidden test set N: Number of images in your curated dataset Prizes Worth Fighting For 🥇 1st Place: $1,000 🥈 2nd Place: Top Tier Community Swag Package 🥉 3rd Place: Mid-Tier Community Swag Package Timeline 🚀 Launch: August 1, 2024 Submissions Open: September 1 - October 27, 2024 🏆 Winners Announced: November 6, 2024 Support System We've got your back! Benefit from: • Workshops: "Getting Started with FiftyOne" (August 21 & September 25) • Check-in/Office Hours: September 6, 13, & 20 Why Participate? • Level up your data curation skills • Make a real-world impact on AI development • Connect with the AI community • Gain recognition for your expertise #data #artificialintelligence #computervision #deeplearning
Hugging Face reposted this
CatVTON: A simple and most efficient virtual try-on diffusion model🤩 Lightweight (899.06M params only), Parameter-Efficient Training (49.57M parameters trainable), and Inference on less than 8G VRAM for 1024X768 resolution 😍 Gradio App locally: To deploy the Gradio App on your machine, visit CatVTON project, and just run the appdotpy file, and checkpoints will be automatically downloaded from Hugging Face Visit: https://lnkd.in/g-mbWPJY
Hugging Face reposted this
In case you didn't already know the awesome FLUX model from Black Forest Labs is supported in 🧨 Diffusers. The model has about 12B parameters along with two text encoders. So, it can be burdensome to run. So, we have put together a little gist guiding you on how to run Flux with limited resources. What a model! Check it out here: https://lnkd.in/gr3R9Eza
Hugging Face reposted this
SF3D is super-fast image to 3D. Generates 3D mesh in 0.5 second🤯 Attached video is not sped up! Demo + Key Details 👇 SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement - SF3D is from Stability AI and is based on their previous work TripoSR. - 🤩 Bonus: You can also select an HDR environment to light up your 3D model. - Great work by Mark Boss on the model development and release!🙌 🥳 You can launch a gradio demo in just 3 steps from the SF3D repo: https://lnkd.in/gvpevyez 🤗 OR access the demo on Hugging Face Spaces: https://lnkd.in/gdUFdU5d
Hugging Face reposted this
Absolutely wild! 🤯 Google DeepMind Gemma 2B outperforms OpenAI GPT-3.5 on LMSYS Chatbot arena with a score of 1130! 20 months ago, "ChatGPT is a revolution, the most powerful model ever made," and today, you can run a model more preferred than this literally on a toaster!🍞 🚀 Gemma 2B It also ranks higher than: > Microsoft Phi-3 Medium (14B version) > Mistral AI 8x7B Instruct > Mistral AI 7B fine-tunes > Meta Llama 2 70B Test it on Hugging Face: https://lnkd.in/dbn4ZGjg Leaderboard: https://lnkd.in/dA-2CiEi
Hugging Face reposted this
🚀 Segment Anything Model 2 (SAM 2) is a foundation model from AI at Meta , designed to address promptable visual segmentation in both images and videos. Deploy with Gradio👇 SAM 2 provides strong performance across a wide range of tasks and visual domains. Here is how you can deploy the model with Gradio: ✅ Google Colab: https://lnkd.in/gFjEyNeb ✅ Hugging Face Spaces demo by Piotr Skalski: https://lnkd.in/gwjkC8cs Have fun!!
Hugging Face reposted this
Trying to catch Andre De Grasse, the reigning 200m Olympic champ from Canada, using AI at Meta's SAM2! How many of you are working on building a demo for video segmentation for SAM2? Let us know how we can help😊
Hugging Face reposted this
🤗 🔥 Since joining Hugging Face, we've worked hard to ship 2.0. Data quality is what makes or breaks AI and Argilla 2.0 is the data-centric tool for AI makers ✨ What's the most exciting? 🐍 A new shiny Python SDK: unified API for datasets, workspaces, and user management (plus new docs 📚) 🚀 Launching Argilla on Spaces just got 10x faster: run data annotation projects with your team or the entire community in just two clicks! 🥞 Automatic task distribution: configure the number of responses per data point to complete high quality datasets in record time! 🤔 Never heard of Argilla before? We made this blog post for you: https://lnkd.in/dGJJefHU