wan-t2v-video

# WAN 2.2 Text-to-Video (T2V) Workflows ## Overview WAN 2.2 T2V generates videos from text prompts using a 14B parameter MoE (Mixture of Experts) architecture split across two specialized models: - HighNoise model handles early denoising. It establishes structure, motion, composition - LowNoise model handles late denoising. It refines details, sharpens output This dual-model technique is the same as FLF/I2V (see wan-flf-video skill) but without image conditioning nodes. Key difference from I2V/FLF: T2V does NOT use `CLIPVisionEncode`, `WanFirstLastFrameToVideo`, or any image input. It uses `EmptyHunyuanLatentVideo` for latent initialization and text-only conditioning. ## Models ### UNET (Installed) | Model | Loader | Notes | |-------|--------|-------| | `Wan2_2-T2V-A14B_HIGH_fp8_e4m3fn_scaled_KJ.safetensors` | `UNETLoader` | HighNoise expert, 14.3GB FP8 | | `Wan2_2-T2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors` | `UNETLoader` | LowNoise expert, 14.3GB FP8 | ### Text Encoder | Component | Node | Model | Notes | |-----------|------|-------|-------| | **CLIP (T5)** | `CLIPLoader` (type=`wan`) | `umt5_xxl_fp8_e4m3fn_scaled.safetensors` | UMT5-XXL fp8, in clip/ | ### VAE | Component | Node | Model | |-----------|------|-------| | **VAE** | `VAELoader` | `wan_2.1_vae.safetensors` | ### VACE Modules (Installed — For Advanced Control) | Model | Size | Notes | |-------|------|-------| | `Wan2_2_Fun_VACE_module_A14B_HIGH_bf16.safetensors` | 5.8GB | HighNoise VACE module |...