HunyuanVideo15LatentUpscaleWithModel - ComfyUI Built-in Node Documentation

Complete documentation for the HunyuanVideo15LatentUpscaleWithModel node in ComfyUI. Learn its inputs, outputs, parameters and usage.

The Hunyuan Video 15 Latent Upscale With Model node increases the resolution of a latent image representation. It first upscales the latent samples to a specified size using a chosen interpolation method, then refines the upscaled result using a specialized Hunyuan Video 1.5 upscale model to improve quality.

Inputs

Parameter	Description	Data Type	Required	Range
model	The Hunyuan Video 1.5 latent upscale model used to refine the upscaled samples.	LATENT_UPSCALE_MODEL	Yes	N/A
samples	The latent image representation to be upscaled.	LATENT	Yes	N/A
upscale_method	The interpolation algorithm used for the initial upscaling step (default: "bilinear").	COMBO	No	"nearest-exact" "bilinear" "area" "bicubic" "bislerp"
width	The target width for the upscaled latent, in pixels. A value of 0 will calculate the width automatically based on the target height and the original aspect ratio. The final output width will be a multiple of 16 (default: 1280).	INT	No	0 to 16384
height	The target height for the upscaled latent, in pixels. A value of 0 will calculate the height automatically based on the target width and the original aspect ratio. The final output height will be a multiple of 16 (default: 720).	INT	No	0 to 16384
crop	Determines how the upscaled latent is cropped to fit the target dimensions.	COMBO	No	"disabled" "center"

Note on Dimensions: If both width and height are set to 0, the node returns the input samples unchanged. If only one dimension is set to 0, the other dimension is calculated to preserve the original aspect ratio. The final dimensions are always adjusted to be at least 64 pixels and are divisible by 16.

Outputs

Output Name	Description	Data Type
LATENT	The upscaled and model-refined latent image representation.	LATENT

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

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