Easy Apply Fooocus Inpaint

Documentation

• Class name: easy applyFooocusInpaint
• Category: EasyUse/Inpaint
• Output node: False

The applyFooocusInpaint node is designed to integrate inpainting capabilities into a model's pipeline by applying specific inpainting heads and patches. This process involves loading and applying pre-trained components to modify the model in a way that it can perform inpainting tasks, which is the process of filling in missing or corrupted parts of images.

Input types

Required

• model
  • The model parameter represents the base model to which inpainting capabilities will be added. It is crucial for defining the starting point of the inpainting process.
  • Comfy dtype: MODEL
  • Python dtype: torch.nn.Module
• latent
  • The latent parameter contains the encoded representation of the image to be inpainted, serving as a critical input for the inpainting process.
  • Comfy dtype: LATENT
  • Python dtype: torch.Tensor
• head
  • The head parameter specifies the inpainting head to be used, determining the specific method and characteristics of the inpainting process.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• patch
  • The patch parameter indicates the specific inpainting patch to apply, further customizing the inpainting behavior.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• model
  • Comfy dtype: MODEL
  • The modified model with inpainting capabilities integrated, ready for performing inpainting tasks.
  • Python dtype: torch.nn.Module

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class applyFooocusInpaint:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model": ("MODEL",),
                "latent": ("LATENT",),
                "head": (list(FOOOCUS_INPAINT_HEAD.keys()),),
                "patch": (list(FOOOCUS_INPAINT_PATCH.keys()),),
            },
        }

    RETURN_TYPES = ("MODEL",)
    RETURN_NAMES = ("model",)
    CATEGORY = "EasyUse/Inpaint"
    FUNCTION = "apply"

    def apply(self, model, latent, head, patch):

        head_file = get_local_filepath(FOOOCUS_INPAINT_HEAD[head]["model_url"], INPAINT_DIR)
        inpaint_head_model = InpaintHead()
        sd = torch.load(head_file, map_location='cpu')
        inpaint_head_model.load_state_dict(sd)

        patch_file = get_local_filepath(FOOOCUS_INPAINT_PATCH[patch]["model_url"], INPAINT_DIR)
        inpaint_lora = comfy.utils.load_torch_file(patch_file, safe_load=True)

        patch = (inpaint_head_model, inpaint_lora)
        worker = InpaintWorker(node_name="easy kSamplerInpainting")
        cloned = model.clone()

        m, = worker.patch(cloned, latent, patch)
        return (m,)