MaskDetailer (pipe)

Documentation

• Class name: MaskDetailerPipe
• Category: ImpactPack/Detailer
• Output node: False

The MaskDetailerPipe node is designed to enhance and refine mask details within images, leveraging advanced image processing techniques to improve the visual quality and accuracy of masks in various applications.

Input types

Required

• image
  • The 'image' parameter specifies the input image to be processed, serving as the primary subject for mask detailing and enhancement.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• mask
  • The 'mask' parameter provides the initial mask to be refined, playing a key role in the detailing process by indicating areas of interest.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor
• basic_pipe
  • The 'basic_pipe' parameter refers to a set of pre-configured models and settings used as a baseline for the detailing process, ensuring consistency and quality in output.
  • Comfy dtype: BASIC_PIPE
  • Python dtype: tuple
• guide_size
  • Specifies the size of the guide for the detailing process, affecting the level of detail achievable.
  • Comfy dtype: FLOAT
  • Python dtype: int
• guide_size_for
  • Specifies the guide size for the detailing process, affecting the precision and detail of the enhancement.
  • Comfy dtype: BOOLEAN
  • Python dtype: int
• max_size
  • Defines the maximum size limit for the detailing process, ensuring the output stays within desired dimensions.
  • Comfy dtype: FLOAT
  • Python dtype: int
• mask_mode
  • unknown
  • Comfy dtype: BOOLEAN
  • Python dtype: unknown
• seed
  • unknown
  • Comfy dtype: INT
  • Python dtype: unknown
• steps
  • unknown
  • Comfy dtype: INT
  • Python dtype: unknown
• cfg
  • unknown
  • Comfy dtype: FLOAT
  • Python dtype: unknown
• sampler_name
  • Specifies the sampler to use during the detailing process, affecting the quality of the output.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• scheduler
  • Determines the scheduling algorithm for the detailing process, impacting the efficiency and outcome.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• denoise
  • Flag to enable or disable denoising during the detailing process, affecting the clarity of the output.
  • Comfy dtype: FLOAT
  • Python dtype: bool
• feather
  • Feathering parameter to smooth edges in the detailing process, enhancing the visual quality of the mask.
  • Comfy dtype: INT
  • Python dtype: int
• crop_factor
  • Factor by which to crop the image during the detailing process, affecting the focus area.
  • Comfy dtype: FLOAT
  • Python dtype: float
• drop_size
  • Minimum size of details to be dropped during the detailing, affecting the level of detail retained.
  • Comfy dtype: INT
  • Python dtype: int
• refiner_ratio
  • Ratio for refining the mask, affecting the intensity of refinement.
  • Comfy dtype: FLOAT
  • Python dtype: float
• batch_size
  • The size of the batch for processing, affecting the throughput of the detailing process.
  • Comfy dtype: INT
  • Python dtype: int
• cycle
  • Number of cycles to perform the detailing process, affecting the thoroughness of enhancement.
  • Comfy dtype: INT
  • Python dtype: int

Optional

• refiner_basic_pipe_opt
  • Optional parameter for an alternative set of models and settings for refining, offering customization.
  • Comfy dtype: BASIC_PIPE
  • Python dtype: tuple or None
• detailer_hook
  • Hook for custom detailing logic, allowing for extended functionality.
  • Comfy dtype: DETAILER_HOOK
  • Python dtype: callable or None
• inpaint_model
  • Flag to enable or disable the use of an inpainting model during the detailing process, affecting the handling of missing areas.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• noise_mask_feather
  • Feathering parameter for the noise mask, affecting the blending of noise in the detailing.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The refined or enhanced image after mask detailing, showcasing improved visual quality.
  • Python dtype: torch.Tensor
• cropped_refined
  • Comfy dtype: IMAGE
  • A list of cropped and refined images, highlighting detailed areas of interest.
  • Python dtype: list
• cropped_enhanced_alpha
  • Comfy dtype: IMAGE
  • A list of cropped images with enhanced alpha channels, providing transparency details.
  • Python dtype: list
• basic_pipe
  • Comfy dtype: BASIC_PIPE
  • The set of models and settings used in the detailing process, returned for potential further use.
  • Python dtype: tuple
• refiner_basic_pipe_opt
  • Comfy dtype: BASIC_PIPE
  • The optional set of alternative models and settings for refining, if used, returned for potential further use.
  • Python dtype: tuple or None

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class MaskDetailerPipe:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
                    "image": ("IMAGE", ),
                    "mask": ("MASK", ),
                    "basic_pipe": ("BASIC_PIPE",),

                    "guide_size": ("FLOAT", {"default": 384, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
                    "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "mask bbox", "label_off": "crop region"}),
                    "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
                    "mask_mode": ("BOOLEAN", {"default": True, "label_on": "masked only", "label_off": "whole"}),

                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
                    "scheduler": (core.SCHEDULERS,),
                    "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),

                    "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
                    "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}),
                    "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
                    "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
                    "batch_size": ("INT", {"default": 1, "min": 1, "max": 100}),

                    "cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
                   },
                "optional": {
                    "refiner_basic_pipe_opt": ("BASIC_PIPE", ),
                    "detailer_hook": ("DETAILER_HOOK",),
                    "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                    "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                   }
                }

    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "BASIC_PIPE", "BASIC_PIPE")
    RETURN_NAMES = ("image", "cropped_refined", "cropped_enhanced_alpha", "basic_pipe", "refiner_basic_pipe_opt")
    OUTPUT_IS_LIST = (False, True, True, False, False)
    FUNCTION = "doit"

    CATEGORY = "ImpactPack/Detailer"

    def doit(self, image, mask, basic_pipe, guide_size, guide_size_for, max_size, mask_mode,
             seed, steps, cfg, sampler_name, scheduler, denoise,
             feather, crop_factor, drop_size, refiner_ratio, batch_size, cycle=1,
             refiner_basic_pipe_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0):

        if len(image) > 1:
            raise Exception('[Impact Pack] ERROR: MaskDetailer does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')

        model, clip, vae, positive, negative = basic_pipe

        if refiner_basic_pipe_opt is None:
            refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None
        else:
            refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt

        # create segs
        if mask is not None:
            mask = make_2d_mask(mask)
            segs = core.mask_to_segs(mask, False, crop_factor, False, drop_size)
        else:
            segs = ((image.shape[1], image.shape[2]), [])

        enhanced_img_batch = None
        cropped_enhanced_list = []
        cropped_enhanced_alpha_list = []

        for i in range(batch_size):
            if mask is not None:
                enhanced_img, _, cropped_enhanced, cropped_enhanced_alpha, _, _ = \
                    DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed+i, steps,
                                              cfg, sampler_name, scheduler, positive, negative, denoise, feather, mask_mode,
                                              force_inpaint=True, wildcard_opt=None, detailer_hook=detailer_hook,
                                              refiner_ratio=refiner_ratio, refiner_model=refiner_model, refiner_clip=refiner_clip,
                                              refiner_positive=refiner_positive, refiner_negative=refiner_negative,
                                              cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
            else:
                enhanced_img, cropped_enhanced, cropped_enhanced_alpha = image, [], []

            if enhanced_img_batch is None:
                enhanced_img_batch = enhanced_img
            else:
                enhanced_img_batch = torch.cat((enhanced_img_batch, enhanced_img), dim=0)

            cropped_enhanced_list += cropped_enhanced
            cropped_enhanced_alpha_list += cropped_enhanced_alpha

        # set fallback image
        if len(cropped_enhanced_list) == 0:
            cropped_enhanced_list = [empty_pil_tensor()]

        if len(cropped_enhanced_alpha_list) == 0:
            cropped_enhanced_alpha_list = [empty_pil_tensor()]

        return enhanced_img_batch, cropped_enhanced_list, cropped_enhanced_alpha_list, basic_pipe, refiner_basic_pipe_opt