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dayona 
posted an update about 24 hours ago
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626
Wan 2.2 I2V 14B Lightning

Generate ultra-realistic, cinematic motion videos in 4–8 steps with 70%+ ZeroGPU quota savings
⚡ 4-Step Lightning Inference 🚀 FP8 Quantized & AoT C++ Compiled 🎬 Prompt Relay Multi-Event 👤 Lock Face Identity (CPU 0 Quota) 📥 Custom Civitai LoRA Downloader 🎬 RIFE 32 FPS (2s GPU ➔ 4s Video)
⚡ Wan 2.2 4-Step Lightning & AoT C++ Compilation
Base Wan 2.2 14B model pre-compiled with NVIDIA C++ AOT kernels (~2.08s/step), rendering videos in 4 steps (10x faster than 40 steps).
🎬 Prompt Relay (Multi-Event Timeline Control)
Exclusive feature routing multi-scene storylines ([0s-2s] Scene A ➔ [2s-4s] Scene B) in a single render with 0 extra GPU quota.
👤 Lock Face Identity (CPU Face Swap)
Preserves and re-applies exact face identity from input image across frames using InsightFace on CPU (0 GPU quota used).
📥 Custom Civitai LoRA Downloader & CPU Pre-Download
Paste any Civitai direct URL to download LoRAs to cache. Features a CPU Pre-Download button to prevent GPU quota waste on network drops.
🎬 RIFE 32 FPS Fluidity & Duration Extension
Renders 2s on GPU (~8.3s quota), then CPU interpolates frames into a ultra-smooth 4s video with 0 extra GPU quota.
🎯 Smart ZeroGPU Quota Auto-Calibration
Auto-calibrated GPU quota reservation formula adjusting for CFG scale & 3D attention, preventing premature quota exhaustion.

https://huggingface.co/spaces/dayona/wan2.2-smart-i2v

The speedup is compelling, especially with AoT compilation and four-step inference. I would also publish a small quality-versus-quota table across prompts and motion complexity so users can see where the 70% savings hold. How stable is face identity across longer multi-event timelines?

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face identity remains very stable across longer multi-event timelines.
Also, I've just added the Sulphur model hosted on a private server specifically to enhance multi-event consistency without consuming any ZeroGPU quota!

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