HunyuanImageToVideo - ComfyUI Built-in Node Documentation

The HunyuanImageToVideo node converts images into video latent representations using the Hunyuan video model.

The HunyuanImageToVideo node converts images into video latent representations using the Hunyuan video model. It takes conditioning inputs and optional starting images to generate video latents that can be further processed by video generation models. The node supports different guidance types for controlling how the starting image influences the video generation process.

Inputs

Parameter	Description	Data Type	Required	Range
positive	Positive conditioning input for guiding the video generation	CONDITIONING	Yes	-
vae	VAE model used for encoding images into latent space	VAE	Yes	-
width	Width of the output video in pixels (default: 848, step: 16)	INT	Yes	16 to MAX_RESOLUTION
height	Height of the output video in pixels (default: 480, step: 16)	INT	Yes	16 to MAX_RESOLUTION
length	Number of frames in the output video (default: 53, step: 4)	INT	Yes	1 to MAX_RESOLUTION
batch_size	Number of videos to generate simultaneously (default: 1)	INT	Yes	1 to 4096
guidance_type	Method for incorporating the starting image into video generation (default: “v1 (concat)“)	COMBO	Yes	”v1 (concat)" "v2 (replace)" "custom”
start_image	Optional starting image to initialize the video generation	IMAGE	No	-

Note: When start_image is provided, the node uses different guidance methods based on the selected guidance_type:

• “v1 (concat)”: Concatenates the image latent with the video latent and applies a mask to blend the image into the video
• “v2 (replace)”: Replaces initial video frames with the image latent and applies a noise mask
• “custom”: Uses the image as a reference latent for guidance

Outputs

Output Name	Description	Data Type
positive	Modified positive conditioning with image guidance applied when start_image is provided	CONDITIONING
latent	Video latent representation ready for further processing by video generation models	LATENT

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): 171e60e0ff5bbe2715b83693212e91dd9a3e2236e7b4437c7e33929d6143ae4f