Apply Mask Sequence to Latent

Documentation

• Class name: JWMaskSequenceApplyToLatent
• Category: jamesWalker55
• Output node: False

This node applies a mask sequence to a latent representation, modifying the latent samples by incorporating the mask sequence into their structure. It's designed to integrate specific mask patterns into the latent space, enabling targeted modifications or enhancements.

Input types

Required

• samples
  • The latent representation to which the mask sequence will be applied. This parameter is crucial for determining the base structure that will be modified by the mask.
  • Comfy dtype: LATENT
  • Python dtype: dict
• mask_sequence
  • The mask sequence to be applied to the latent samples. This parameter defines the specific modifications or enhancements to be made to the latent structure.
  • Comfy dtype: MASK_SEQUENCE
  • Python dtype: torch.Tensor

Output types

• latent
  • Comfy dtype: LATENT
  • The modified latent representation after applying the mask sequence. This output reflects the integration of the mask pattern into the original latent samples.
  • Python dtype: dict

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

@register_node("JWMaskSequenceFromMask", "Mask Sequence From Mask")
class _:
    CATEGORY = "jamesWalker55"
    INPUT_TYPES = lambda: {
        "required": {
            "mask": ("MASK",),
            "batch_size": ("INT", {"default": 1, "min": 1, "step": 1}),
        }
    }
    RETURN_TYPES = ("MASK_SEQUENCE",)
    FUNCTION = "execute"

    def execute(
        self,
        mask: torch.Tensor,
        batch_size: int,
    ):
        assert isinstance(mask, torch.Tensor)
        assert isinstance(batch_size, int)
        assert len(mask.shape) == 2

        mask_seq = mask.reshape((1, 1, *mask.shape))
        mask_seq = mask_seq.repeat(batch_size, 1, 1, 1)

        return (mask_seq,)