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reacted to seawolf2357's post with šŸ”„ 2 days ago
šŸš€ VEO3 Real-Time: Real-time AI Video Generation with Self-Forcing šŸŽÆ Core Innovation: Self-Forcing Technology VEO3 Real-Time, an open-source project challenging Google's VEO3, achieves real-time video generation through revolutionary Self-Forcing technology. https://huggingface.co/spaces/Heartsync/VEO3-RealTime āš” What is Self-Forcing? While traditional methods require 50-100 steps, Self-Forcing achieves the same quality in just 1-2 steps. Through self-correction and rapid convergence, this Distribution Matching Distillation (DMD) technique maintains quality while delivering 50x speed improvement. šŸ’” Technical Advantages of Self-Forcing 1. Extreme Speed Generates 4-second videos in under 30 seconds, with first frame streaming in just 3 seconds. This represents 50x faster performance than traditional diffusion methods. 2. Consistent Quality Maintains cinematic quality despite fewer steps, ensures temporal consistency, and minimizes artifacts. 3. Efficient Resource Usage Reduces GPU memory usage by 70% and heat generation by 30%, enabling smooth operation on mid-range GPUs like RTX 3060. šŸ› ļø Technology Stack Synergy VEO3 Real-Time integrates multiple technologies organically around Self-Forcing DMD. Self-Forcing DMD handles ultra-fast video generation, Wan2.1-T2V-1.3B serves as the high-quality video backbone, PyAV streaming enables real-time transmission, and Qwen3 adds intelligent prompt enhancement for polished results. šŸ“Š Performance Comparison Traditional methods require 50-100 steps, taking 2-5 minutes for the first frame and 5-10 minutes total. In contrast, Self-Forcing needs only 1-2 steps, delivering the first frame in 3 seconds and complete videos in 30 seconds while maintaining equal quality.šŸ”® Future of Self-Forcing Our next goal is real-time 1080p generation, with ongoing research to achieve
reacted to seawolf2357's post with šŸ‘ 12 days ago
āš” 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 āœØ 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 āš” 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.
updated a Space 16 days ago
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