Wan T2v Video
Build WAN 2.2 Text-to-Video workflows.
Install
npx skills add artokun/comfyui-mcp --skill wan-t2v-video -g -a claude-code -y- Repository
- artokun/comfyui-mcp883
- Category
- Media
- Updated
- 9d ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Build WAN 2.2 Text-to-Video workflows. Dual hi-lo models, lightning LoRAs, VACE modules, and KSamplerAdvanced two-pass
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 |
Wan2_2_Fun_VACE_module_A14B_LOW_bf16.safetensors | 5.8GB | LowNoise VACE module |
VACE modules add reference image / pose / depth conditioning to T2V. See WanVideoWrapper section below.
