القائمة
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)
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | wan-multitalk |
| description | 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) |
| globs | ["**/*.json"] |
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.
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):
| 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.
| 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
--*vram mode; MultiTalk benefits from --reserve-vram headroom for
the Wav2Vec + VAE round-trips.WanVideoImageToVideoMultiTalk (not bypassed), and that the audio isn't silent
after AudioCrop.Drive the LTX Director (Timeline) node — its Add Image/Text/Audio buttons are DOM-only and cannot be clicked by an agent; edit the hidden timeline_data JSON widget instead. Load when a workflow contains LTXDirector / LTXDirectorGuide / PromptRelayEncodeTimeline, or when asked to add, move, retime, or remove timeline segments (image / text / audio / motion).
Train a character/identity LoRA locally on FLUX.1-dev via the comfyui-mcp train_* tools (GPU Docker + ostris ai-toolkit). Use when the user wants to train a LoRA of a person/character from their photos on the local GPU — covers dataset prep, launch, monitoring, and using the result in ComfyUI. For WAN/Z-Image training via the ai-toolkit UI see ai-toolkit-trainer.
Core ComfyUI knowledge — workflow format, node types, pipeline patterns, and MCP tool usage
Discover Civitai models with the BUILT-IN search_civitai_models tool and install/generate them locally — find a checkpoint/LoRA/embedding on Civitai, download it into ComfyUI, and use its trigger words. Optionally pair the official Civitai MCP for community features (images browsing, posting, collections).
Run the ComfyUI agent locally for FREE — no subscription, no API key, fully offline — using our gemma4 models fine-tuned on the comfyui-mcp tool suite via Ollama. Use when the user asks about running locally, running for free, offline use, avoiding API costs, Ollama setup, or which local model to pick.
Debug a WRONG or imperfect render (not a hard error) by inspecting inputs and intermediate steps with run-to-node — render one branch up to an output, preview-tap latents/masks/preprocessor maps, localize the first bad stage, then fix. Use when a final image/video comes out wrong — artifacts, wrong subject/pose/composition/color, blur, a ControlNet/IPAdapter/mask/LoRA not taking, a two-stage refiner or upscale degrading the result — rather than the run failing with an error (for errors/OOM/missing nodes use the troubleshooting skill).