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AI & ML interests
AI Agent, LLM
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4 days ago
VividFlow: AI Image-to-Video Generation 🎬✨
Bring your images to life with cinematic motion! VividFlow transforms any static image—portraits, artwork, products, or landscapes, into dynamic videos with professional animation quality.
The system supports both curated motion templates and custom natural language prompts, giving you complete creative freedom to describe camera movements, subject actions, and atmospheric effects in your own words.
What's Inside?
🎭 Smart Motion Templates — 8 curated categories from fashion cinematography to wildlife animations, each with tested prompts that prevent common artifacts like phantom hands in portraits
⚡ Optimized Engine — Powered by Wan2.2-I2V-A14B with Lightning LoRA distillation and FP8 quantization for memory-efficient inference
🎯 Full Creative Control — Seed-based reproducibility for consistent results, adjustable duration from half a second to five seconds, optional AI prompt expansion with Qwen2.5 for enhanced descriptions, and real-time resolution preview
Current Performance & Development Roadmap
VividFlow runs on ZeroGPU with generation taking about 3-4 minutes for 3-second videos. While I am actively optimizing the pipeline to reduce this time, the current version prioritizes output stability and quality, results are worth the wait!
Future development focuses on dedicated GPU deployment for faster processing, batch generation to create multiple variations at once, and expanding our motion template library based on what the community wants to see.
👉 Try it now: https://huggingface.co/spaces/DawnC/VividFlow
If VividFlow brings motion to your creative vision, please show your support with a ❤️, your engagement influences future development priorities!
#AI #ImageToVideo #GenerativeAI #VideoGeneration #DeepLearning
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PawMatchAI — Smarter, Safer, and More Thoughtful Recommendations 🐕✨
🐾 Recommendation system update — deeper reasoning, safer decisions
Over the past weeks, user feedback led me to rethink how PawMatchAI handles description-based breed recommendations. Instead of only matching surface-level preferences, the system now implements a multi-dimensional semantic reasoning architecture that emphasizes real-life compatibility and risk awareness.
Key technical improvements:
- SBERT-powered semantic understanding with dynamic weight allocation across six constraint dimensions (space, activity, noise, grooming, experience, family)
- Hierarchical constraint management distinguishing critical safety constraints from flexible preferences, with progressive relaxation when needed
-Multi-head scoring system combining semantic matching (15%), lifestyle compatibility (70%), constraint adherence (10%), and confidence calibration (5%)
-Intelligent risk filtering that applies graduated penalties (-10% to -40%) for genuine incompatibilities while preserving user choice
The goal: 👉 Not just dogs that sound good on paper, but breeds people will actually thrive with long-term.
What's improved?
- 🎯 Clearer separation of must-have safety constraints versus flexible preferences
- 🧠 Bidirectional semantic matching evaluating compatibility from both user and breed perspectives
- 🔍 Context-aware prioritization where critical factors (safety, space, noise) automatically receive higher weighting
What's next?
- 🐕 Expanding behavioral and temperament analysis dimensions
- 🐾 Extension to additional species with transfer learning
- 📱 Mobile-optimized deployment for easier access
- 🧩 Enhanced explainability showing why specific breeds are recommended
👉 Try PawMatchAI: https://huggingface.co/spaces/DawnC/PawMatchAI
#AIProduct #SBERT #RecommendationSystems #DeepLearning #MachineLearning #NLP
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