Regional Prompt By Color Mask (Inspire)

Documentation

• Class name: RegionalPromptColorMask __Inspire
• Category: InspirePack/Regional
• Output node: False

The RegionalPromptColorMask node is designed to generate regional prompts based on a color mask. It applies a color-based segmentation to an image, creating distinct regions that can be individually prompted for creative or targeted image generation tasks. This node facilitates the customization of image generation processes by allowing users to influence specific areas of an image through color-coded masks.

Input types

Required

• basic_pipe
  • The foundational pipeline for image processing or generation, setting the stage for subsequent regional prompt application.
  • Comfy dtype: BASIC_PIPE
  • Python dtype: object
• color_mask
  • An image input that serves as a mask, where specific colors indicate regions for applying distinct prompts.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• mask_color
  • A string specifying the color used in the color_mask to define the regions of interest.
  • Comfy dtype: STRING
  • Python dtype: str
• cfg
  • A floating-point value that adjusts the configuration strength, influencing the intensity of the applied prompts.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sampler_name
  • Specifies the sampling method to be used in the generation process, affecting the diversity and quality of the output.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• scheduler
  • Determines the scheduling algorithm for controlling the generation process, impacting how prompts are applied over time.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• wildcard_prompt
  • A string input for dynamic, user-defined prompts that can be applied to the specified regions, enhancing creative control.
  • Comfy dtype: STRING
  • Python dtype: str
• controlnet_in_pipe
  • A boolean indicating whether to keep or override the control network within the pipeline, affecting the final image generation.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• sigma_factor
  • A floating-point value that adjusts the sigma factor, fine-tuning the application of prompts to the regions.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• regional_prompts
  • Comfy dtype: REGIONAL_PROMPTS
  • The generated regional prompts, tailored to the specified areas of the input image.
  • Python dtype: object
• mask
  • Comfy dtype: MASK
  • The mask generated from the specified color, used to delineate regions for prompt application.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class RegionalPromptColorMask:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "basic_pipe": ("BASIC_PIPE",),
                "color_mask": ("IMAGE",),
                "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
                "scheduler": (common.SCHEDULERS,),
                "wildcard_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "placeholder": "wildcard prompt"}),
                "controlnet_in_pipe": ("BOOLEAN", {"default": False, "label_on": "Keep", "label_off": "Override"}),
                "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
            },
        }

    RETURN_TYPES = ("REGIONAL_PROMPTS", "MASK")
    FUNCTION = "doit"

    CATEGORY = "InspirePack/Regional"

    def doit(self, basic_pipe, color_mask, mask_color, cfg, sampler_name, scheduler, wildcard_prompt, controlnet_in_pipe=False, sigma_factor=1.0):
        mask = color_to_mask(color_mask, mask_color)
        rp = RegionalPromptSimple().doit(basic_pipe, mask, cfg, sampler_name, scheduler, wildcard_prompt, controlnet_in_pipe, sigma_factor=sigma_factor)[0]
        return (rp, mask)