LTXVContextWindows - ComfyUI Built-in Node Documentation

This node sets context windows for LTXV-like models during sampling. It divides the video generation process into overlapping windows to manage memory usage and improve temporal coherence.

Inputs

Parameter	Description	Data Type	Required	Range
`model`	The model to apply context windows to during sampling.	MODEL	Yes	-
`context_length`	The length of the context window in real frames. Must be 8*n + 1. (default: 145)	INT	Yes	Minimum: 1 Maximum: nodes.MAX_RESOLUTION Step: 8
`context_overlap`	The overlap of the context window in real frames. (default: 40)	INT	Yes	Minimum: 0 Step: 8
`context_schedule`	Step-dependent scheduling algorithm for context windows. (default: UNIFORM_STANDARD)	COMBO	Yes	`STATIC_STANDARD` `UNIFORM_STANDARD` `UNIFORM_LOOPED` `BATCHED`
`context_stride`	The stride of the context window; only applicable to uniform schedules. (default: 1)	INT	No	Minimum: 1
`closed_loop`	Whether to close the context window loop; only applicable to looped schedules. (default: False)	BOOLEAN	No	True False
`fuse_method`	The method to use to fuse the context windows. (default: PYRAMID)	COMBO	Yes	Options from comfy.context_windows.ContextFuseMethods.LIST_STATIC
`freenoise`	Whether to apply FreeNoise noise shuffling, improves window blending. (default: True)	BOOLEAN	No	True False
`retain_first_frame`	Retain the first latent frame in every context window (may help retain initial reference). (default: False)	BOOLEAN	No	True False
`split_conds_to_windows`	Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index. (default: False)	BOOLEAN	No	True False

Note: The context_length parameter must follow the formula 8*n + 1, where n is a positive integer. The node automatically adjusts the value to meet this requirement by converting real frames to latent frames. The context_overlap is also converted from real frames to latent frames (divided by 8).

Outputs

Output Name	Description	Data Type
`MODEL`	The model with context windows applied for sampling.	MODEL

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

Source fingerprint (SHA-256): ad0b8c54acaab1853f6fe98dbad675478f033caf867df954b40b7db5208f5ae4

​Inputs

​Outputs

Inputs

Outputs