workflow-templates-broken-zap-2026-06-08/site/knowledge/nodes/custom/WanInfiniteTalkToVideo.md

WanInfiniteTalkToVideo

Category: conditioning/video_models

Display Name: model

Description

This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! Edit on GitHub

The WanInfiniteTalkToVideo node generates video sequences from audio input. It uses a video diffusion model, conditioned on audio features extracted from one or two speakers, to produce a latent representation of a talking head video. The node can generate a new sequence or extend an existing one using previous frames for motion context.

Inputs

Parameter	Type	Default	Description
mode	DynamicCombo	—
audio_encoder_output_2	AudioEncoderOutput	—
mask_1	Mask	—	Mask for the first speaker, required if using two audio inputs.
mask_2	Mask	—	Mask for the second speaker, required if using two audio inputs.
model	Model	—
model_patch	ModelPatch	—
positive	Conditioning	—
negative	Conditioning	—
vae	Vae	—
width	Int	832
height	Int	480
length	Int	81
clip_vision_output	ClipVisionOutput	—
start_image	Image	—
audio_encoder_output_1	AudioEncoderOutput	—
motion_frame_count	Int	9	Number of previous frames to use as motion context.
audio_scale	Float	1.0
previous_frames	Image	—

Outputs

Output Name	Data Type	Description
model	MODEL	The patched model with audio conditioning applied.
positive	CONDITIONING	The positive conditioning, potentially modified with additional context (e.g., start image, CLIP vision).
negative	CONDITIONING	The negative conditioning, potentially modified with additional context.
latent	LATENT	The generated video sequence in latent space.
trim_image	INT	The number of frames from the start of the motion context that should be trimmed when extending a sequence.

Source: comfy_extras/nodes_wan.py

Used in 1 template(s)