Regional Prompt Simple (Inspire)

Documentation

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

The RegionalPromptSimple node is designed to facilitate the creation of region-specific prompts within an image generation pipeline. It leverages regional information to tailor the generation process, ensuring that the output images are aligned with the specified regional characteristics and constraints.

Input types

Required

• basic_pipe
  • Represents the foundational components of the image generation pipeline, including models and initial conditions. It's essential for the node to function within the broader generation context.
  • Comfy dtype: BASIC_PIPE
  • Python dtype: Tuple[Model, ClipModel, VAEModel, List[Tuple[str, Dict[str, Any]]], List[Tuple[str, Dict[str, Any]]]]
• mask
  • An image mask defining the regions of interest. It is used to apply regional prompts specifically to areas highlighted by the mask.
  • Comfy dtype: MASK
  • Python dtype: Image
• cfg
  • Controls the conditioning factor for generation, affecting how strongly the regional prompts influence the output.
  • Comfy dtype: FLOAT
  • Python dtype: float
• sampler_name
  • Determines the sampling method used during image generation, impacting the diversity and quality of the output.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• scheduler
  • Defines the scheduling algorithm for controlling the generation process, influencing the progression and variation of the output.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• wildcard_prompt
  • Allows for the inclusion of dynamic or variable text elements within the prompts, adding flexibility to the generation process.
  • Comfy dtype: STRING
  • Python dtype: str
• controlnet_in_pipe
  • Indicates whether control networks are included in the pipeline, affecting the application of regional prompts.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• sigma_factor
  • Adjusts the noise level in the generation process, influencing the variability and creativity of the output.
  • Comfy dtype: FLOAT
  • Python dtype: float

Optional

• variation_seed
  • A seed for introducing variations in the generation process, providing a way to explore different outcomes.
  • Comfy dtype: INT
  • Python dtype: int
• variation_strength
  • Determines the strength of the variations introduced by the variation seed, affecting the diversity of the generated output.
  • Comfy dtype: FLOAT
  • Python dtype: float
• variation_method
  • Specifies the method used for applying variations, influencing how changes are integrated into the generation process.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• scheduler_func_opt
  • An optional scheduling function that can be used to further customize the generation process.
  • Comfy dtype: SCHEDULER_FUNC
  • Python dtype: SchedulerFunc

Output types

• regional_prompts
  • Comfy dtype: REGIONAL_PROMPTS
  • The generated regional prompts, tailored to specific areas of the image as defined by the input mask.
  • Python dtype: List[RegionalPrompt]

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class RegionalPromptSimple:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "basic_pipe": ("BASIC_PIPE",),
                "mask": ("MASK",),
                "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}),
            },
            "optional": {
                "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                "variation_method": (["linear", "slerp"],),
                "scheduler_func_opt": ("SCHEDULER_FUNC",),
            }
        }

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

    CATEGORY = "InspirePack/Regional"

    @staticmethod
    def doit(basic_pipe, mask, cfg, sampler_name, scheduler, wildcard_prompt,
             controlnet_in_pipe=False, sigma_factor=1.0, variation_seed=0, variation_strength=0.0, variation_method='linear', scheduler_func_opt=None):
        if 'RegionalPrompt' not in nodes.NODE_CLASS_MAPPINGS:
            utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack',
                                          "To use 'RegionalPromptSimple' node, 'Impact Pack' extension is required.")
            raise Exception(f"[ERROR] To use RegionalPromptSimple, you need to install 'ComfyUI-Impact-Pack'")

        model, clip, vae, positive, negative = basic_pipe

        iwe = nodes.NODE_CLASS_MAPPINGS['ImpactWildcardEncode']()
        kap = nodes.NODE_CLASS_MAPPINGS['KSamplerAdvancedProvider']()
        rp = nodes.NODE_CLASS_MAPPINGS['RegionalPrompt']()

        if wildcard_prompt != "":
            model, clip, new_positive, _ = iwe.doit(model=model, clip=clip, populated_text=wildcard_prompt, seed=None)

            if controlnet_in_pipe:
                prev_cnet = None
                for t in positive:
                    if 'control' in t[1] and 'control_apply_to_uncond' in t[1]:
                        prev_cnet = t[1]['control'], t[1]['control_apply_to_uncond']
                        break

                if prev_cnet is not None:
                    for t in new_positive:
                        t[1]['control'] = prev_cnet[0]
                        t[1]['control_apply_to_uncond'] = prev_cnet[1]

        else:
            new_positive = positive

        basic_pipe = model, clip, vae, new_positive, negative

        sampler = kap.doit(cfg, sampler_name, scheduler, basic_pipe, sigma_factor=sigma_factor, scheduler_func_opt=scheduler_func_opt)[0]
        try:
            regional_prompts = rp.doit(mask, sampler, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method)[0]
        except:
            raise Exception("[Inspire-Pack] ERROR: Impact Pack is outdated. Update Impact Pack to latest version to use this.")

        return (regional_prompts, )