fahrizalfarid

akahana

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NLP

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reacted to seawolf2357's post with ๐Ÿ”ฅ 28 days ago
โšก FusionX Enhanced Wan 2.1 I2V (14B) ๐ŸŽฌ ๐Ÿš€ Revolutionary Image-to-Video Generation Model Generate cinematic-quality videos in just 8 steps! https://huggingface.co/spaces/Heartsync/WAN2-1-fast-T2V-FusioniX โœจ Key Features ๐ŸŽฏ Ultra-Fast Generation: Premium quality in just 8-10 steps ๐ŸŽฌ Cinematic Quality: Smooth motion with detailed textures ๐Ÿ”ฅ FusionX Technology: Enhanced with CausVid + MPS Rewards LoRA ๐Ÿ“ Optimized Resolution: 576ร—1024 default settings โšก 50% Speed Boost: Faster rendering compared to base models ๐Ÿ› ๏ธ Technical Stack Base Model: Wan2.1 I2V 14B Enhancement Technologies: ๐Ÿ”— CausVid LoRA (1.0 strength) - Motion modeling ๐Ÿ”— MPS Rewards LoRA (0.7 strength) - Detail optimization Scheduler: UniPC Multistep (flow_shift=8.0) Auto Prompt Enhancement: Automatic cinematic keyword injection ๐ŸŽจ 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! ๐Ÿ’ก Optimization Tips โœ… Recommended Settings: 8-10 steps, 576ร—1024 resolution โœ… Prompting: Use "cinematic motion, smooth animation" keywords โœ… Duration: 2-5 seconds for optimal quality โœ… Motion: Emphasize natural movement and camera work ๐Ÿ† 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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