TwoSamplersForMask

Documentation

• Class name: TwoSamplersForMask
• Category: ImpactPack/Sampler
• Output node: False

This node applies two distinct sampling processes to a latent image using a mask. Initially, it applies an inverse mask to the latent image with one sampler, then applies the original mask with another sampler, effectively blending two different sampling effects based on the mask's pattern.

Input types

Required

• latent_image
  • The latent representation of an image to be processed. It serves as the base for applying the sampling effects.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]
• base_sampler
  • A sampler that applies its effect to the areas of the latent image not covered by the mask.
  • Comfy dtype: KSAMPLER
  • Python dtype: KSAMPLER
• mask_sampler
  • A sampler that applies its effect specifically to the areas of the latent image covered by the mask.
  • Comfy dtype: KSAMPLER
  • Python dtype: KSAMPLER
• mask
  • A binary mask determining the areas of the latent image to be affected by the mask_sampler.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

Output types

• latent
  • Comfy dtype: LATENT
  • The modified latent image after applying the two sampling processes.
  • Python dtype: Dict[str, torch.Tensor]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class TwoSamplersForMask:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
                     "latent_image": ("LATENT", ),
                     "base_sampler": ("KSAMPLER", ),
                     "mask_sampler": ("KSAMPLER", ),
                     "mask": ("MASK", )
                     },
                }

    RETURN_TYPES = ("LATENT", )
    FUNCTION = "doit"

    CATEGORY = "ImpactPack/Sampler"

    def doit(self, latent_image, base_sampler, mask_sampler, mask):
        inv_mask = torch.where(mask != 1.0, torch.tensor(1.0), torch.tensor(0.0))

        latent_image['noise_mask'] = inv_mask
        new_latent_image = base_sampler.sample(latent_image)

        new_latent_image['noise_mask'] = mask
        new_latent_image = mask_sampler.sample(new_latent_image)

        del new_latent_image['noise_mask']

        return (new_latent_image, )