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Wan Multitalk

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artokun
wan-multitalk

Build WAN MultiTalk audio-driven talking-avatar / lip-sync video workflows. MeiGen-AI MultiTalk on WAN 2.1 14B I2V via kijai WanVideoWrapper (portrait + audio → lip-synced video)

Overview

Publisherartokun
Repositorycomfyui-mcp
Skill namewan-multitalk
Stars
811
Forks
137
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by artokun on GitHub. Read the source before you install it.

Installation

Install the Wan Multitalk AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/wan-multitalk
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/artokun/comfyui-mcp.git /tmp/comfyui-mcp
mkdir -p .claude/skills
cp -r /tmp/comfyui-mcp/plugin/skills/wan-multitalk .claude/skills/wan-multitalk
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wan Multitalk in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wan Multitalk on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wan Multitalk is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

WAN MultiTalk — Audio-Driven Talking Avatar

Overview

MultiTalk (MeiGen-AI) drives a still portrait's lip-sync and head motion from an audio track. It runs on WAN 2.1 14B Image-to-Video via kijai's ComfyUI-WanVideoWrapper. Wav2Vec speech embeddings condition the WAN sampler so the mouth and expression follow the speech, while the lightx2v step-distill LoRA keeps it to a few sampling steps.

Use it for talking heads, dubbing, and single-speaker avatar clips (~10s at 480p). It is distinct from wan-animate (pose/motion-driven character animation). This is audio → lip-sync, not reference-video motion transfer.

Pack: wan-multitalk (480p, ~10s). Higher-res/longer variants exist in the source bundle (720p, long-context) as VRAM/duration knobs on the same graph.

Pipeline (node graph)

LoadImage (portrait) ─┐
LoadAudio ─ AudioSeparation ─ AudioCrop ─ DownloadAndLoadWav2VecModel ─ MultiTalkWav2VecEmbeds ─┐
                                                                                                 ▼
WanVideoModelLoader (WAN 2.1 14B I2V GGUF) ─ MultiTalkModelLoader ─ WanVideoLoraSelect (lightx2v)
   + LoadWanVideoT5TextEncoder (umt5) + WanVideoTextEncode + WanVideoClipVisionEncode (clip_vision_h)
   + WanVideoVAELoader ──────────────────────────────────────────────────────────────────────────┘
                                                     ▼
                       WanVideoImageToVideoMultiTalk ─ WanVideoSampler ─ WanVideoDecode ─ VHS_VideoCombine

Key nodes (all kijai WanVideoWrapper unless noted):

  • DownloadAndLoadWav2VecModel. Auto-downloads the Wav2Vec speech model on first run (no manifest entry needed).
  • MultiTalkWav2VecEmbeds. Turns the (separated, cropped) speech into the embeddings that steer the mouth and expression.
  • MultiTalkModelLoader + WanVideoImageToVideoMultiTalk. The MultiTalk head on top of the WAN I2V model.
  • AudioSeparation and AudioCrop (audio-separation-nodes-comfyui). Isolate the voice from music/noise before embedding and trim the segment you want to animate.
  • ImageResizeKJv2 (KJNodes), VHS_VideoCombine (VideoHelperSuite). Resize and mux to mp4.

Models

FileLoaderFolder
Wan2.1_14b_Image_to_Video_480p_GGUF_Q8.ggufWanVideoModelLoaderdiffusion_models/
WanVideo_2_1_Multitalk_14B_fp8_e4m3fn.safetensorsMultiTalkModelLoaderdiffusion_models/
umt5_xxl_fp16.safetensorsLoadWanVideoT5TextEncodertext_encoders/
Wan2_1_VAE_bf16.safetensorsWanVideoVAELoadervae/
clip_vision_h.safetensorsCLIPVisionLoaderclip_vision/
Wan21_I2V_14B_lightx2v_cfg_step_distill_lora_rank64.safetensorsWanVideoLoraSelectloras/

Sources: kijai Kijai/WanVideo_comfy, MeiGen-AI MeiGen-AI/MeiGen-MultiTalk, GGUF city96/Wan2.1-I2V-14B-480P-gguf, and Comfy-Org's repackaged UMT5. See packs/wan-multitalk/manifest.yaml (some URLs are best-effort; verify per mirror). Wav2Vec auto-downloads. The bundled WanVideoWrapper loader rejects the scaled_fp8 UMT5 checkpoint; use the UMT5 fp16 file above, not generic t5xxl_fp16 weights.

Inputs & key parameters

  • Portrait (LoadImage): front-facing, clear face, neutral-ish expression works best. Resized by ImageResizeKJv2 to the target (480p).
  • Audio (LoadAudio): the speech track. AudioSeparation isolates the voice; AudioCrop selects the segment (drives clip length).
  • Steps: low (the lightx2v distill LoRA is why; typically ~4 to 8). Raising steps rarely helps and costs time.
  • BlockSwap (WanVideoBlockSwap): trade VRAM for speed. Increase blocks swapped to CPU on lower-VRAM cards.

VRAM tiers (from the source bundle's variants)

TargetApprox VRAMLever
480p 10s~8–12 GBbase
480p low-VRAM~6–8.4 GBmore BlockSwap, GGUF quant, lower quality
720p 10s~11–16 GBhigher res

Pair with the VRAM launch-flags guidance (see troubleshooting): --use-sage-attention

  • appropriate --*vram mode; MultiTalk benefits from --reserve-vram headroom for the Wav2Vec + VAE round-trips.

Gotchas

  • Audio must be voice-isolated for good lip-sync. Skipping AudioSeparation on a music-heavy track makes the mouth chase the wrong signal.
  • One speaker. This graph is single-speaker; multi-speaker MultiTalk needs the multi-embed variant (not in this pack).
  • Wav2Vec first run downloads a model, so the first render is slower.
  • If lips look under-driven, check the MultiTalk embeds are actually wired into WanVideoImageToVideoMultiTalk (not bypassed), and that the audio isn't silent after AudioCrop.

Sources

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

Frequently asked questions

What does the Wan Multitalk AI skill do?

Build WAN MultiTalk audio-driven talking-avatar / lip-sync video workflows. MeiGen-AI MultiTalk on WAN 2.1 14B I2V via kijai WanVideoWrapper (portrait + audio → lip-synced video)

Why use Wan Multitalk on TypingMind?

Because you install it once and use it with any model. Wan Multitalk is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wan Multitalk in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/wan-multitalk. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wan Multitalk?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wan Multitalk?

As many as you like. As long as a model supports skills, you can use Wan Multitalk with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wan Multitalk AI skill free?

Yes. It is published on GitHub by artokun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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