Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sandyup/comfyui-mcp --skill wan-t2v-videogit clone --depth 1 https://github.com/sandyup/comfyui-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sandyup/comfyui-mcp/wan-t2v-video)<a href="https://agentmods.dev/skills/sandyup/comfyui-mcp/wan-t2v-video"><img src="https://agentmods.dev/badge/skills/sandyup/comfyui-mcp/wan-t2v-video/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sandyup/comfyui-mcp/wan-t2v-video"><img src="https://agentmods.dev/badge/skills/sandyup/comfyui-mcp/wan-t2v-video.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.04152 |
| Opus 5 | $0.00019 | $0.02076 |
| Sonnet 5 | $0.00008 | $0.00830 |
| Haiku 4.5 | $0.00004 | $0.00415 |
Grade A, and why
wan-t2v-video scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to wan-t2v-video — 53 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 335 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 — establishes structure, motion, composition
- LowNoise model: Handles late denoising — 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.
Lightning LoRAs (Installed)
T2V Lightning v1.1 (Paired Hi/Lo)
| LoRA | Applies To | Path |
|---|---|---|
wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise |
HighNoise UNET | Unknown/no tags/ |
wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise |
LowNoise UNET | Unknown/no tags/ |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 335 lines · 38 tokens per session scan A 0eae77cd730a
wan-t2v-video is a skill published in the GitHub repository sandyup/comfyui-mcp (1 stars, last pushed 2mo ago), licensed MIT. It adds 38 tokens to every session and 4,152 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to wan-t2v-video, differing in 53 lines, and is treated as a copy.
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