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Wan Multitalk Agent Skill

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)

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
481
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/artokun/comfyui-mcp --skill wan-multitalk

The instruction itself

7 sections, as written by the author

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/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/expression.

  • MultiTalkModelLoader + WanVideoImageToVideoMultiTalk — the MultiTalk head

on top of the WAN I2V model.

  • AudioSeparation — isolate the voice from music/noise before embedding (cleaner

lip-sync). AudioCrop — trim to the segment you want to animate.

  • ImageResizeKJv2 (KJNodes), VHS_VideoCombine (VideoHelperSuite) — resize +

mux to mp4.

Models

| File | Loader | Folder |

|------|--------|--------|

| Wan2.1_14b_Image_to_Video_480p_GGUF_Q8.gguf | WanVideoModelLoader | diffusion_models/ |

| WanVideo_2_1_Multitalk_14B_fp32.safetensors | MultiTalkModelLoader | diffusion_models/ |

| umt5_xxl_fp8_e4m3fn_scaled.safetensors | LoadWanVideoT5TextEncoder | text_encoders/ |

| Wan2_1_VAE_bf16.safetensors | WanVideoVAELoader | vae/ |

| clip_vision_h.safetensors | CLIPVisionLoader | clip_vision/ |

| Wan21_I2V_14B_lightx2v_cfg_step_distill_lora_rank64_fixed.safetensors | WanVideoLoraSelect | loras/ |

Sources: kijai Kijai/WanVideo_comfy, MeiGen-AI MeiGen-AI/MeiGen-MultiTalk, GGUF

city96/Wan2.1-I2V-14B-480P-gguf. See packs/wan-multitalk/manifest.yaml (some URLs

are best-effort — verify per mirror). Wav2Vec auto-downloads.

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 ~4–8). 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)

| Target | Approx VRAM | Lever |

|--------|-------------|-------|

| 480p 10s | ~8–12 GB | base |

| 480p low-VRAM | ~6–8.4 GB | more BlockSwap, GGUF quant, lower quality |

| 720p 10s | ~11–16 GB | higher 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 — 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.

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How to use it

Copy the folder

Take artokun/wan-multitalk from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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