Agent skill

Wan T2v Video

by artokun in artokun/comfyui-mcp

Build WAN 2.2 Text-to-Video workflows. An agent skill from artokun/comfyui-mcp.

MITAuto-check passedAI & LLM Engineering

Install Wan T2v Video

skills CLI
$ npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install artokun/comfyui-mcp wan-t2v-video --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/wan-t2v-video .claude/skills/wan-t2v-video && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
wan-t2v-video
GitHub stars
803
Token cost
~3.3k tokens
SKILL.md length
864 words
Files
1
Skills in repo
42
Repo updated
First seen
Licence
MIT

At a glance

Build WAN 2.2 Text-to-Video workflows. An agent skill from artokun/comfyui-mcp.

  • Tasks that involve Fine-tuning
  • SKILL.md covers Overview, Models, Lightning LoRAs (Installed) and Sampler Settings, plus 9 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve AI video generation

What it does

Wan T2v Video is an agent skill from artokun/comfyui-mcp. Build WAN 2.2 Text-to-Video workflows. Dual hi-lo models, lightning LoRAs, VACE modules, and KSamplerAdvanced two-pass

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Fine-tuning and AI video generation. The repository describes itself as: Local-first, agent-native control plane for ComfyUI — MCP server + sidebar agent that generates images, video & audio, authors and runs workflows, and edits your live graph in… The licence is MIT.

When your agent uses it

  • Tasks that involve Fine-tuning
  • Tasks that involve AI video generation

Example prompts

  • “/wan-t2v-video”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 6ad6fc0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Wan T2v Video loads about 3.3k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 864 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from artokun/comfyui-mcp at commit 6ad6fc0, republished under its MIT licence (© artokun). 864 words, ~3,296 tokens.

Download SKILL.mdSave it as .claude/skills/wan-t2v-video/SKILL.md (or your agent's skills folder).
name
wan-t2v-video
description
Build WAN 2.2 Text-to-Video workflows. Dual hi-lo models, lightning LoRAs, VACE modules, and KSamplerAdvanced two-pass
globs
**/*.json

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)
ModelLoaderNotes
Wan2_2-T2V-A14B_HIGH_fp8_e4m3fn_scaled_KJ.safetensorsUNETLoaderHighNoise expert, 14.3GB FP8
Wan2_2-T2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensorsUNETLoaderLowNoise expert, 14.3GB FP8
Text Encoder
ComponentNodeModelNotes
CLIP (T5)CLIPLoader (type=wan)umt5_xxl_fp8_e4m3fn_scaled.safetensorsUMT5-XXL fp8, in clip/
VAE
ComponentNodeModel
VAEVAELoaderwan_2.1_vae.safetensors
VACE Modules (Installed — For Advanced Control)
ModelSizeNotes
Wan2_2_Fun_VACE_module_A14B_HIGH_bf16.safetensors5.8GBHighNoise VACE module
Wan2_2_Fun_VACE_module_A14B_LOW_bf16.safetensors5.8GBLowNoise 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)
LoRAApplies ToPath
wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noiseHighNoise UNETUnknown/no tags/
wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noiseLowNoise UNETUnknown/no tags/
T2V Lightning Seko V2.0 (Alternative Paired)
LoRAPath
Wan2.2_HN_T2V_Lightning_4steps-lora-rank64-Seko_V2.0_HIGHRoot loras/
Wan2.2_HN_T2V_Lightning_4steps-lora-rank64-Seko_V2.0_LOWRoot loras/
T2V CFG-Step Distill (Higher Quality)
LoRAPathNotes
lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank128_bf16Root loras/CFG+step distilled, use with more steps

Sampler Settings

ParameterPass 1 (Hi)Pass 2 (Lo)
modelHi + Hi Lightning LoRALo + Lo Lightning LoRA
add_noiseenabledisable
steps44
cfg1.01.0
sampler_nameeulereuler
schedulersimplesimple
start_at_step02
end_at_step24
return_with_leftover_noiseenabledisable
Standard (20-Step, Full Quality)
ParameterPass 1 (Hi)Pass 2 (Lo)
modelHi + ModelSamplingSD3 (shift=8)Lo + ModelSamplingSD3 (shift=8)
add_noiseenabledisable
steps2020
cfg3.53.5
sampler_nameeulereuler
schedulersimplesimple
start_at_step010
end_at_step1020
return_with_leftover_noiseenabledisable
ModelSamplingSD3

Required for WAN 2.2 flow matching. Apply to BOTH models:

json
{
  "class_type": "ModelSamplingSD3",
  "inputs": { "model": ["<unet>", 0], "shift": 8 }
}

T2V shift values:

  • Standard: shift=8 (good balance of motion and detail)
  • Lightning: shift=5 (lower shift for distilled models)
  • Range 6 to 9: higher shift = more detail, lower shift = stronger motion

EmptyHunyuanLatentVideo

Creates the initial video latent for T2V (no image input):

json
{
  "class_type": "EmptyHunyuanLatentVideo",
  "inputs": {
    "width": 832,
    "height": 480,
    "length": 81,
    "batch_size": 1
  }
}

This replaces WanFirstLastFrameToVideo (which is for FLF/I2V only). The latent goes directly to KSamplerAdvanced Pass 1.

Negative Prompt

The tones are vibrant, overexposed, static, details are unclear, subtitles, style, work, painting, image, still, overall grayish, worst quality, low quality, JPEG compression artifacts, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, distorted limbs, merged fingers, motionless image, cluttered background, three legs, many people in the background, walking backwards

Pipeline Flow

UNETLoader (HIGH T2V) → ModelSamplingSD3 (shift) → LoraLoaderModelOnly (Hi Lightning) → MODEL_HI
UNETLoader (LOW T2V) → ModelSamplingSD3 (shift) → LoraLoaderModelOnly (Lo Lightning) → MODEL_LO
CLIPLoader (wan) → CLIP
  ├─ CLIPTextEncode (positive) → CONDITIONING
  └─ CLIPTextEncode (negative) → CONDITIONING
VAELoader → VAE

EmptyHunyuanLatentVideo (832x480, 81 frames) → LATENT

KSamplerAdvanced (Hi: MODEL_HI, steps 0-2, add_noise=enable, return_leftover=enable)
  → noisy LATENT
KSamplerAdvanced (Lo: MODEL_LO, steps 2-4, add_noise=disable, return_leftover=disable)
  → final LATENT

VAEDecode → IMAGE → VHS_VideoCombine → MP4

Complete Workflow: T2V Lightning (4-Step)

json
{
  "1": { "class_type": "UNETLoader", "inputs": { "unet_name": "Wan2_2-T2V-A14B_HIGH_fp8_e4m3fn_scaled_KJ.safetensors", "weight_dtype": "default" }, "_meta": { "title": "UNET HighNoise T2V" }},
  "2": { "class_type": "UNETLoader", "inputs": { "unet_name": "Wan2_2-T2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors", "weight_dtype": "default" }, "_meta": { "title": "UNET LowNoise T2V" }},
  "3": { "class_type": "ModelSamplingSD3", "inputs": { "model": ["1", 0], "shift": 5 }, "_meta": { "title": "Hi Shift" }},
  "4": { "class_type": "ModelSamplingSD3", "inputs": { "model": ["2", 0], "shift": 5 }, "_meta": { "title": "Lo Shift" }},
  "5": { "class_type": "LoraLoaderModelOnly", "inputs": {
    "model": ["3", 0],
    "lora_name": "Unknown\\no tags\\wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors",
    "strength_model": 1.0
  }, "_meta": { "title": "Hi Lightning" }},
  "6": { "class_type": "LoraLoaderModelOnly", "inputs": {
    "model": ["4", 0],
    "lora_name": "Unknown\\no tags\\wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors",
    "strength_model": 1.0
  }, "_meta": { "title": "Lo Lightning" }},
  "7": { "class_type": "CLIPLoader", "inputs": { "clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors", "type": "wan" }},
  "8": { "class_type": "VAELoader", "inputs": { "vae_name": "wan_2.1_vae.safetensors" }},
  "9": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["7", 0], "text": "<positive prompt describing the video scene and motion>" }, "_meta": { "title": "Positive" }},
  "10": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["7", 0], "text": "The tones are vibrant, overexposed, static, details are unclear, subtitles, worst quality, low quality, motionless image" }, "_meta": { "title": "Negative" }},
  "11": { "class_type": "EmptyHunyuanLatentVideo", "inputs": {
    "width": 832, "height": 480, "length": 81, "batch_size": 1
  }},
  "12": { "class_type": "KSamplerAdvanced", "inputs": {
    "model": ["5", 0],
    "positive": ["9", 0],
    "negative": ["10", 0],
    "latent_image": ["11", 0],
    "add_noise": "enable", "noise_seed": 0, "steps": 4, "cfg": 1,
    "sampler_name": "euler", "scheduler": "simple",
    "start_at_step": 0, "end_at_step": 2, "return_with_leftover_noise": "enable"
  }, "_meta": { "title": "Hi Pass" }},
  "13": { "class_type": "KSamplerAdvanced", "inputs": {
    "model": ["6", 0],
    "positive": ["9", 0],
    "negative": ["10", 0],
    "latent_image": ["12", 0],
    "add_noise": "disable", "noise_seed": 0, "steps": 4, "cfg": 1,
    "sampler_name": "euler", "scheduler": "simple",
    "start_at_step": 2, "end_at_step": 4, "return_with_leftover_noise": "disable"
  }, "_meta": { "title": "Lo Pass" }},
  "14": { "class_type": "VAEDecode", "inputs": { "samples": ["13", 0], "vae": ["8", 0] }},
  "15": { "class_type": "VHS_VideoCombine", "inputs": {
    "images": ["14", 0], "frame_rate": 16, "loop_count": 0,
    "filename_prefix": "wan_t2v", "format": "video/h264-mp4",
    "pingpong": false, "save_output": true,
    "pix_fmt": "yuv420p", "crf": 19, "save_metadata": true, "trim_to_audio": false
  }}
}

Complete Workflow: T2V Standard (20-Step)

Same structure as above but replace the LoRA and sampler settings:

  • Remove LoRA nodes (5 and 6). Connect ModelSamplingSD3 outputs directly to KSamplerAdvanced
  • Change shift to 8 in ModelSamplingSD3 nodes
  • Change KSamplerAdvanced settings:
    • steps: 20, cfg: 3.5
    • Pass 1: start_at_step: 0, end_at_step: 10
    • Pass 2: start_at_step: 10, end_at_step: 20

WanVideoWrapper Approach (Advanced)

For more control, use the WanVideoWrapper custom node pack. Key differences from native:

  • Uses WanVideoModelLoader → WANVIDEOMODEL type
  • Uses WanVideoSampler with built-in shift parameter
  • Supports TeaCache, context windows, block swap for VRAM management
  • LoRAs load via WanVideoLoraSelect → WanVideoModelLoader lora input
Show full SKILL.md (389 more words)Show less
⚠️ CRITICAL: merge_loras=false with fp8-scaled models

When a LoRA (e.g. the lightx2v 4-step lightning hi/lo LoRAs) is loaded onto an fp8-quantized model (quantization=fp8_e4m3fn_scaled) via WanVideoLoraSelect, set the node's merge_loras widget to false. The default merge_loras=true tries to bake the LoRA into the already-quantized fp8 weights and hard-crashes ComfyUI during LoRA loading with no Python traceback (the process dies, which looks like an unexplained restart/OOM). merge_loras=false applies the LoRA as a runtime patch instead, which is fp8-safe. Use merge_loras=true only on non-quantized bf16/fp16 models. Pairs cleanly with WanVideoBlockSwap for fp8 14B on 24GB cards.

WanVideoWrapper T2V Pipeline
WanVideoModelLoader (T2V model) → WANVIDEOMODEL
WanVideoVAELoader → WANVAE
WanVideoTextEncode (positive + negative prompts) → WANVIDEOTEXTEMBEDS
WanVideoImageToVideoEncode (no images — creates empty embeds for T2V)
  → WANVIDIMAGE_EMBEDS

WanVideoSampler (model, image_embeds, text_embeds, steps, cfg, shift, scheduler)
  → LATENT

WanVideoDecode → IMAGE → VHS_VideoCombine → MP4
WanVideoSampler T2V Settings
ParameterStandardLightningNotes
steps304
cfg6.01.0
shift5.05.0Flow matching shift
schedulerunipceuler
force_offloadtruetrue

Concept LoRAs (Installed)

Located in loras/Wan Video 2.2 T2V-A14B/:

  • concept/PussyLoRA_HighNoise_Wan2.2_HearmemanAI.safetensors + LowNoise pair

Apply concept LoRAs the same way as lightning LoRAs. Match hi/lo to the correct model pass. Use LoraLoaderModelOnly with strength 0.5 to 1.0.

Resolution & Frame Count

Resolutions
AspectResolutionNotes
Landscape 16:9832x480Default, recommended
Portrait 9:16480x832
720p landscape1280x720Higher quality, more VRAM
720p portrait720x1280

Width and height must be divisible by 16.

Frame Count (4n + 1)
  • 81 frames at 16fps = ~5 seconds (default, recommended)
  • 49 frames at 16fps = ~3 seconds (faster)
  • 121 frames at 16fps = ~7.5 seconds (longer, more VRAM)
Frame Rate

Standard: 16 fps for WAN 2.2 output.

VRAM Considerations

ConfigVRAMNotes
Dual FP8 models + UMT5 fp8~22-24GBTight on RTX 4090
Single FP8 model (no dual)~14-16GBLower quality but safer
With VACE modules+5.8GB per moduleVery tight, may need block swap
  • Always clear_vram before switching to WAN T2V from another model family
  • Lightning (4 steps) cuts generation time to ~70s vs ~5-10 min for 20 steps
  • Only one UNET is active during each pass. They swap in/out

Prompt Tips

Describe motion and temporal progression in addition to the scene:

Good: "A beautiful young woman slowly walks through a blooming cherry blossom garden, petals drifting in the breeze, soft sunlight filtering through branches, cinematic slow motion, 4K quality"
Bad: "woman in garden"

Include motion cues: "slowly walks", "camera pans", "wind blowing", "gradually reveals"

T2V vs I2V/FLF Comparison

FeatureT2VI2V/FLF
InputText onlyText + start/end images
Latent initEmptyHunyuanLatentVideoWanFirstLastFrameToVideo
CLIPVisionNot usedRequired
ModelsT2V-specific (HIGH/LOW)I2V-specific (HIGH/LOW)
Lightning LoRAsT2V-specificI2V-specific
CreativityFull creative freedomConstrained by input frames
Use caseOriginal contentTransitions, animations

Sources

  • Official: none found.
  • Empirical: sampler values, wiring, and prompt notes from working graphs in packs/ and observed renders; not a vendor prompting guide.

© artokun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugin/skills/wan-t2v-video of artokun/comfyui-mcp.

Open the folder on GitHubat commit 6ad6fc0

Compare with similar skills

Wan T2v Video next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Nsfw VideoLeoYeAI/openclaw-master-skills2.2k—~4.6kAutomated safety check: PassMIT
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Questions about Wan T2v Video

What does Wan T2v Video do?

Build WAN 2.2 Text-to-Video workflows. An agent skill from artokun/comfyui-mcp. Wan T2v Video is an agent skill from artokun/comfyui-mcp.2 Text-to-Video workflows.

When should I use Wan T2v Video?

Wan T2v Video fits situations like: tasks that involve Fine-tuning; tasks that involve AI video generation.

How do I install Wan T2v Video in Claude Code?

Run `npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a claude-code`. Or copy the skill folder (plugin/skills/wan-t2v-video in artokun/comfyui-mcp) into .claude/skills/wan-t2v-video in your project. Claude Code loads it when a task matches its description.

How do I install Wan T2v Video in Codex?

Run `npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a codex`. Or copy the skill folder (plugin/skills/wan-t2v-video in artokun/comfyui-mcp) into .agents/skills/wan-t2v-video in your project. Codex loads it when a task matches its description.

Can I use Wan T2v Video in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/wan-t2v-video, .gemini/skills/wan-t2v-video, .github/skills/wan-t2v-video and .opencode/skills/wan-t2v-video in your project.

What does Wan T2v Video need to run?

SKILL.md names no scripts, command-line tools or credentials: Wan T2v Video is instructions for the agent only. Our summary lists: Python 3.

Does Wan T2v Video access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Wan T2v Video safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Wan T2v Video use?

Wan T2v Video is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Wan T2v Video use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Wan T2v Video?

Skills that share tags, products or a category with Wan T2v Video: Gemini Live API (google/skills, 21k stars), Tao Finetune Cosmos Embed (NVIDIA/skills, 3.6k stars), Nsfw Video (LeoYeAI/openclaw-master-skills, 2.2k stars) and Flux Image (danielmeppiel/agentic-sdlc-handbook, 158 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Wan T2v Video?

artokun (a GitHub user) maintains it in artokun/comfyui-mcp, which has 803 GitHub stars. The repository holds 42 skills in this directory. The repository was last updated on October 5, 2026.

Source: artokun/comfyui-mcp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.