Equinox Elahin's picture

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Equinox Elahin

EquinoxElahin

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reacted to ginipick's post with 🔥 6 days ago

🌈✨ FLUX 'Every Text Imaginator' Multilingual Text-Driven Image Generation and Editing Demo: https://huggingface.co/spaces/ginigen/Every-Text 📝 What is FLUX Text Imaginator? FLUX Text Imaginator is an innovative tool that leverages cutting-edge FLUX diffusion models to create and edit images with perfectly integrated multilingual text. Unlike other image generation models, FLUX possesses exceptional capability to naturally incorporate text in various languages including Korean, English, Chinese, Japanese, Russian, French, Spanish and more into images! ✨ FLUX's Multilingual Text Processing Strengths 🔤 Superior Multilingual Text Rendering: FLUX renders text with amazing accuracy, including non-English languages and special characters 🇰🇷 Perfect Korean Language Support: Accurately represents complex Korean combined characters 🈶 Excellent East Asian Language Handling: Naturally expresses complex Chinese characters and Japanese text 🔍 Sophisticated Text Placement: Precise text positioning using <text1>, <text2>, <text3> placeholders 🎭 Diverse Text Styles: Text representation in various styles including handwriting, neon, signage, billboards, and more 🔄 Automatic Translation Feature: Korean prompts are automatically translated to English for optimal results 🚀 How It Works Text Generation Mode: Enter your prompt (with optional text placeholders) Specify your desired text in any language Generate high-quality images with naturally integrated text using FLUX's powerful multilingual processing capabilities Get two different versions of your image for each generation Image Editing Mode: Upload any image Add editing instructions Specify new text to add or replace (multilingual support) Create naturally edited images with FLUX's sophisticated text processing abilities 💻 Technical Details FLUX's Core Technologies: -Text-Aware Diffusion Model -Multilingual Processing Engine -Korean-English Translation Pipeline -Optimized Pipeline

reacted to singhsidhukuldeep's post with 🔥 23 days ago

Exciting New Tool for Knowledge Graph Extraction from Plain Text! I just came across a groundbreaking new tool called KGGen that's solving a major challenge in the AI world - the scarcity of high-quality knowledge graph data. KGGen is an open-source Python package that leverages language models to extract knowledge graphs (KGs) from plain text. What makes it special is its innovative approach to clustering related entities, which significantly reduces sparsity in the extracted KGs. The technical approach is fascinating: 1. KGGen uses a multi-stage process involving an LLM (GPT-4o in their implementation) to extract entities and relations from source text 2. It aggregates graphs across sources to reduce redundancy 3. Most importantly, it applies iterative LM-based clustering to refine the raw graph The clustering stage is particularly innovative - it identifies which nodes and edges refer to the same underlying entities or concepts. This normalizes variations in tense, plurality, stemming, and capitalization (e.g., "labors" clustered with "labor"). The researchers from Stanford and University of Toronto also introduced MINE (Measure of Information in Nodes and Edges), the first benchmark for evaluating KG extractors. When tested against existing methods like OpenIE and GraphRAG, KGGen outperformed them by up to 18%. For anyone working with knowledge graphs, RAG systems, or KG embeddings, this tool addresses the fundamental challenge of data scarcity that's been holding back progress in graph-based foundation models. The package is available via pip install kg-gen, making it accessible to everyone. This could be a game-changer for knowledge graph applications!

reacted to burtenshaw's post with 🔥 27 days ago

Now the Hugging Face agent course is getting real! With frameworks like smolagents, LlamaIndex, and LangChain. 🔗 Follow the org for updates https://huggingface.co/agents-course This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers: - why should you use smolagents vs another library? - how to build agents that use code - build multiagents systems - use vision language models for browser use The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric.

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