WAN Context Windows (Manual) - ComfyUI Built-in Node Documentation

WAN Context Windows (Manual) node is a specialized context window manual control node optimized for WAN (Wide Attention Network) models. It automatically converts standard context parameters to WAN-compatible format, implementing sliding window sampling for ultra-long sequence generation.

The WAN Context Windows (Manual) node is specifically designed for WAN models. It automatically adjusts parameters to allow WAN models to better handle long content.

How it works: Like using multiple segmented samples to generate different segments of a video, and finally synthesizing a complete video**

• Each window looks at a part (window processing)
• Windows overlap to ensure coherence (overlap areas)
• Finally, all parts are spliced into a complete image (result fusion)

Input Parameters

Parameter Name	Function	Simple Explanation
model	WAN Model Input	The WAN model you want to process
context_length	Window Size	Length of each small window, WAN will automatically adjust to multiples of 4. The larger the range the more memory it occupies
context_overlap	Overlap Size	Overlapping part between windows, more overlap means smoother effect but slower processing
context_schedule	Processing Method	Choose how windows move, four modes available
context_stride	Movement Step	How much the window moves each time, only useful in uniform mode
closed_loop	Loop Mode	Whether to connect the last window back to the first, only useful in loop mode
fuse_method	Merge Method	Choose how overlapping parts merge, affects final effect
dim	Processing Dimension	Fixed at 2, specifically optimized for WAN models

Scheduling Strategy Selection

Scheduling Strategy	Working Method
STATIC_STANDARD	Window positions are fixed
UNIFORM_STANDARD	Windows move uniformly
UNIFORM_LOOPED	Windows move in a loop
BATCHED	Multiple windows processed simultaneously

Merge Methods

Merge Method	Weight Distribution Method	Characteristics
PYRAMID	Middle part most important, decreasing toward edges	Most natural effect, recommended
RELATIVE	Intelligent weight adjustment	Automatically adjusts based on position, natural effect
FLAT	Average weight distribution	Simple and direct, suitable for uniform content
OVERLAP_LINEAR	Linear gradient	Smoother transition

Output Results

Parameter Name	Data Type	Description
model	MODEL	Processed WAN model, can be directly connected to sampler for use

WAN Parameter Automatic Adjustment

Typical Usage

Usage Steps:

1. Load WAN model
2. Connect to WAN Context Windows Manual node
3. Connect to sampler to generate results