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fahrizalfarid

akahana
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https://fahrizalfarid.com

AI & ML interests

NLP

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reacted to seawolf2357's post with 🔥 about 4 hours ago
⚡ FusionX Enhanced Wan 2.1 I2V (14B) 🎬 🚀 Revolutionary Image-to-Video Generation Model Generate cinematic-quality videos in just 8 steps! https://huggingface.co/spaces/Heartsync/WAN2-1-fast-T2V-FusioniX ✨ Key Features 🎯 Ultra-Fast Generation: Premium quality in just 8-10 steps 🎬 Cinematic Quality: Smooth motion with detailed textures 🔥 FusionX Technology: Enhanced with CausVid + MPS Rewards LoRA 📐 Optimized Resolution: 576×1024 default settings ⚡ 50% Speed Boost: Faster rendering compared to base models 🛠️ Technical Stack Base Model: Wan2.1 I2V 14B Enhancement Technologies: 🔗 CausVid LoRA (1.0 strength) - Motion modeling 🔗 MPS Rewards LoRA (0.7 strength) - Detail optimization Scheduler: UniPC Multistep (flow_shift=8.0) Auto Prompt Enhancement: Automatic cinematic keyword injection 🎨 How to Use Upload Image - Select your starting image Enter Prompt - Describe desired motion and style Adjust Settings - 8 steps, 2-5 seconds recommended Generate - Complete in just minutes! 💡 Optimization Tips ✅ Recommended Settings: 8-10 steps, 576×1024 resolution ✅ Prompting: Use "cinematic motion, smooth animation" keywords ✅ Duration: 2-5 seconds for optimal quality ✅ Motion: Emphasize natural movement and camera work 🏆 FusionX Enhanced vs Standard Models Performance Comparison: While standard models typically require 15-20 inference steps to achieve decent quality, our FusionX Enhanced version delivers premium results in just 8-10 steps - that's more than 50% faster! The rendering speed has been dramatically improved through optimized LoRA fusion, allowing creators to iterate quickly without sacrificing quality. Motion quality has been significantly enhanced with advanced causal modeling, producing smoother, more realistic animations compared to base implementations. Detail preservation is substantially better thanks to MPS Rewards training, maintaining crisp textures and consistent temporal coherence throughout the generated sequences.
reacted to DualityAI-RebekahBogdanoff's post with 👍 about 4 hours ago
Can AI models trained solely on 100% synthetic data achieve top-tier accuracy in real-world object detection? 👉 Sergio Sanz, PhD just proved it while winning Duality AI’s Synthetic-to-Real Object Detection Challenge using Falcon-generated imagery. His model achieved perfect real-world detection accuracy without a single real image in the training loop. In this blog, Dr. Sanz walks us through his method, which includes the design and training of an advanced pipeline to achieve 100% detection accuracy. His full technical breakdown covers: 📍 Synthetic-only training 📍 Data augmentation with an ensemble learning approach for better generalization 📍 Custom occlusion generation 📍 A Faster R-CNN model fine-tuned with Falcon generated data 📍 And much more! The results speak for themselves! 📖 Read the blog here: https://www.duality.ai/blog/leveraging-synthetic-data-for-real-world-object-detection Congratulations Sergio! We can't wait to see what you do next. 🔔 Ready to take on the next Synthetic-to-Real challenge? The third edition of our Kaggle competition—Multi-Instance Object Detection Challenge—is now live: https://www.kaggle.com/competitions/multi-instance-object-detection-challenge
updated a dataset 11 days ago
akahana/anti-spoofing-nuaaaa
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