fahrizalfarid

akahana

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NLP

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reacted to seawolf2357's post with šŸ”„ 28 days ago
⚔ FusionX Enhanced Wan 2.1 I2V (14B) šŸŽ¬ šŸš€ 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 šŸŽÆ Ultra-Fast Generation: Premium quality in just 8-10 steps šŸŽ¬ Cinematic Quality: Smooth motion with detailed textures šŸ”„ FusionX Technology: Enhanced with CausVid + MPS Rewards LoRA šŸ“ Optimized Resolution: 576Ɨ1024 default settings ⚔ 50% Speed Boost: Faster rendering compared to base models šŸ› ļø Technical Stack Base Model: Wan2.1 I2V 14B Enhancement Technologies: šŸ”— CausVid LoRA (1.0 strength) - Motion modeling šŸ”— MPS Rewards LoRA (0.7 strength) - Detail optimization Scheduler: UniPC Multistep (flow_shift=8.0) Auto Prompt Enhancement: Automatic cinematic keyword injection šŸŽØ 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! šŸ’” Optimization Tips āœ… Recommended Settings: 8-10 steps, 576Ɨ1024 resolution āœ… Prompting: Use "cinematic motion, smooth animation" keywords āœ… Duration: 2-5 seconds for optimal quality āœ… Motion: Emphasize natural movement and camera work šŸ† 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.
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