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Wan Vace To Video The Wan Vace To Video node allows you to generate videos through text prompts and supports multiple input methods, including text, images, videos, masks, and control signals. This node combines input conditions (prompts), control videos, and masks to generate high-quality videos. It first preprocesses and encodes the inputs, then applies the conditional information to generate the final video latent representation. When a reference image is provided, it serves as the initial reference for the video. Control videos and masks can be used to guide the generation process, making the generated video more aligned with expectations.

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Parameter Description

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Required Parameters

ParameterTypeDefaultRangeDescription
positiveCONDITIONING--Positive prompt condition
negativeCONDITIONING--Negative prompt condition
vaeVAE--VAE model for encoding/decoding
widthINT83216-MAX_RESOLUTIONVideo width, step size 16
heightINT48016-MAX_RESOLUTIONVideo height, step size 16
lengthINT811-MAX_RESOLUTIONNumber of video frames, step size 4
batch_sizeINT11-4096Batch size
strengthFLOAT1.00.0-1000.0Condition strength, step size 0.01

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Optional Parameters

ParameterTypeDescription
control_videoIMAGEControl video for guiding the generation process
control_masksMASKControl masks defining which areas should be controlled
reference_imageIMAGEReference image as starting point or reference (single image)

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Output Parameters

ParameterTypeDescription
positiveCONDITIONINGProcessed positive prompt condition
negativeCONDITIONINGProcessed negative prompt condition
latentLATENTGenerated video latent representation
trim_latentINTParameter for trimming latent representation, default value is 0. When a reference image is provided, this value is set to the shape size of the reference image in latent space. It indicates how much content from the reference image downstream nodes should trim from the generated latent representation to ensure proper control of the reference image’s influence in the final video output.

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Source Code

[Source code update time: 2025-05-15] 
class WanVaceToVideo:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"positive": ("CONDITIONING", ),
                             "negative": ("CONDITIONING", ),
                             "vae": ("VAE", ),
                             "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                             "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
                },
                "optional": {"control_video": ("IMAGE", ),
                             "control_masks": ("MASK", ),
                             "reference_image": ("IMAGE", ),
                }}

    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT", "INT")
    RETURN_NAMES = ("positive", "negative", "latent", "trim_latent")
    FUNCTION = "encode"

    CATEGORY = "conditioning/video_models"

    EXPERIMENTAL = True

    def encode(self, positive, negative, vae, width, height, length, batch_size, strength, control_video=None, control_masks=None, reference_image=None):
        latent_length = ((length - 1) // 4) + 1
        if control_video is not None:
            control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
            if control_video.shape[0] < length:
                control_video = torch.nn.functional.pad(control_video, (0, 0, 0, 0, 0, 0, 0, length - control_video.shape[0]), value=0.5)
        else:
            control_video = torch.ones((length, height, width, 3)) * 0.5

        if reference_image is not None:
            reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
            reference_image = vae.encode(reference_image[:, :, :, :3])
            reference_image = torch.cat([reference_image, comfy.latent_formats.Wan21().process_out(torch.zeros_like(reference_image))], dim=1)

        if control_masks is None:
            mask = torch.ones((length, height, width, 1))
        else:
            mask = control_masks
            if mask.ndim == 3:
                mask = mask.unsqueeze(1)
            mask = comfy.utils.common_upscale(mask[:length], width, height, "bilinear", "center").movedim(1, -1)
            if mask.shape[0] < length:
                mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, 0, 0, length - mask.shape[0]), value=1.0)

        control_video = control_video - 0.5
        inactive = (control_video * (1 - mask)) + 0.5
        reactive = (control_video * mask) + 0.5

        inactive = vae.encode(inactive[:, :, :, :3])
        reactive = vae.encode(reactive[:, :, :, :3])
        control_video_latent = torch.cat((inactive, reactive), dim=1)
        if reference_image is not None:
            control_video_latent = torch.cat((reference_image, control_video_latent), dim=2)

        vae_stride = 8
        height_mask = height // vae_stride
        width_mask = width // vae_stride
        mask = mask.view(length, height_mask, vae_stride, width_mask, vae_stride)
        mask = mask.permute(2, 4, 0, 1, 3)
        mask = mask.reshape(vae_stride * vae_stride, length, height_mask, width_mask)
        mask = torch.nn.functional.interpolate(mask.unsqueeze(0), size=(latent_length, height_mask, width_mask), mode='nearest-exact').squeeze(0)

        trim_latent = 0
        if reference_image is not None:
            mask_pad = torch.zeros_like(mask[:, :reference_image.shape[2], :, :])
            mask = torch.cat((mask_pad, mask), dim=1)
            latent_length += reference_image.shape[2]
            trim_latent = reference_image.shape[2]

        mask = mask.unsqueeze(0)
        positive = node_helpers.conditioning_set_values(positive, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength})
        negative = node_helpers.conditioning_set_values(negative, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength})

        latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device())
        out_latent = {}
        out_latent["samples"] = latent
        return (positive, negative, out_latent, trim_latent)