IP Adapter Tiled Settings (JPS)

Documentation

• Class name: IP Adapter Tiled Settings (JPS)
• Category: JPS Nodes/Settings
• Output node: False

This node is designed to configure and adapt image processing settings for tiled image generation, allowing for detailed customization of the image processing pipeline. It facilitates the adjustment of various parameters such as model selection, weight types, noise levels, and interpolation methods to tailor the image generation process to specific needs.

Input types

Required

• ipa_model
  • Specifies the model used in the image processing operation, affecting the overall quality and characteristics of the generated image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• ipa_wtype
  • Determines the weight type for the image processing, influencing how weights are applied throughout the generation process.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• ipa_weight
  • Sets the weight for the image processing, adjusting the influence of certain parameters on the final output.
  • Comfy dtype: FLOAT
  • Python dtype: float
• ipa_noise
  • Defines the level of noise to be added to the image processing, impacting the texture and detail of the generated image.
  • Comfy dtype: FLOAT
  • Python dtype: float
• ipa_start
  • Marks the starting point for the image processing operation, setting the initial conditions.
  • Comfy dtype: FLOAT
  • Python dtype: float
• ipa_end
  • Indicates the end point for the image processing operation, finalizing the conditions under which the image is generated.
  • Comfy dtype: FLOAT
  • Python dtype: float
• tile_short
  • Specifies the shortest side of the tile, affecting the overall tiling strategy and output dimensions.
  • Comfy dtype: INT
  • Python dtype: int
• tile_weight
  • Determines the weight applied to tiles during the image processing, influencing the balance between different tiles.
  • Comfy dtype: FLOAT
  • Python dtype: float
• zoom
  • Sets the zoom level for the image processing, altering the focus and scale of the generated image.
  • Comfy dtype: INT
  • Python dtype: int
• offset_w
  • Adjusts the horizontal offset for the image processing, shifting the image generation focus horizontally.
  • Comfy dtype: INT
  • Python dtype: int
• offset_h
  • Adjusts the vertical offset for the image processing, shifting the image generation focus vertically.
  • Comfy dtype: INT
  • Python dtype: int
• prepare_type
  • Defines the preparation type for the image processing, setting the approach for how images are prepared before processing.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• prepare_intpol
  • Specifies the interpolation method used in the image processing, affecting the smoothness and quality of the generated image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• prepare_sharpening
  • Sets the level of sharpening applied during the image processing, enhancing the clarity and detail of the generated image.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• ip_adapter_tiled_settings
  • Comfy dtype: BASIC_PIPE
  • Returns a comprehensive set of settings tailored for tiled image processing, encapsulating model details, weight adjustments, noise levels, and more, to optimize the image generation pipeline.
  • Python dtype: Tuple[int, str, float, float, float, float, int, float, int, int, int, int, str, float]

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class IP_Adapter_Tiled_Settings:

    preparetypes = ["Target AR + Target Res", "Target AR + Tile Res", "Tile AR + Target Res", "Source AR + Source Res", "Source AR + Tile Res", "Tile AR + Source Res", "Square AR + Target Res", "Square AR + Tile Res", "Direct Source" ]

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "ipa_model": (["SDXL ViT-H", "SDXL Plus ViT-H", "SDXL Plus Face ViT-H"],),
                "ipa_wtype": (["linear", "ease in", "ease out", "ease in-out", "reverse in-out", "weak input", "weak output", "weak middle", "strong middle"],),
                "ipa_weight": ("FLOAT", {"default": 0.5, "min": 0, "max": 3, "step": 0.01}),
                "ipa_noise": ("FLOAT", {"default": 0.0, "min": 0, "max": 1, "step": 0.05}),
                "ipa_start": ("FLOAT", {"default": 0.00, "min": 0, "max": 1, "step": 0.05}),
                "ipa_end": ("FLOAT", {"default": 1.00, "min": 0, "max": 1, "step": 0.05}),
                "tile_short": ("INT", { "default": 2, "min": 1, "max": 5, "step": 1, "display": "number" }),
                "tile_weight": ("FLOAT", {"default": 0.55, "min": 0, "max": 1, "step": 0.05}),
                "zoom": ("INT", {"default": 100, "min": 1, "max": 500, "step": 1}),
                "offset_w": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }),
                "offset_h": ("INT", { "default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number" }),
                "prepare_type": (s.preparetypes,),
                "prepare_intpol": (["lanczos", "nearest", "bilinear", "bicubic", "area", "nearest-exact"],),
                "prepare_sharpening": ("FLOAT", { "default": 0.0, "min": 0, "max": 1, "step": 0.05, "display": "number" }),
            }
        }
    RETURN_TYPES = ("BASIC_PIPE",)
    RETURN_NAMES = ("ip_adapter_tiled_settings",)
    FUNCTION = "get_ipatiled"

    CATEGORY="JPS Nodes/Settings"

    def get_ipatiled(self,ipa_model,ipa_wtype,ipa_weight,ipa_noise,ipa_start,ipa_end,tile_short,tile_weight,zoom,offset_w,offset_h,prepare_type,prepare_intpol,prepare_sharpening,):

        ipamodel = int (0)
        if(ipa_model == "SDXL ViT-H"):
            ipamodel = int(1)
        elif(ipa_model == "SDXL Plus ViT-H"):
            ipamodel = int(2)
        elif(ipa_model == "SDXL Plus Face ViT-H"):
            ipamodel = int(3)

        preparetype = int (0)
        if(prepare_type == "Target AR + Target Res"):
            preparetype = int(1)
        elif(prepare_type == "Target AR + Tile Res"):
            preparetype = int(2)
        elif(prepare_type == "Tile AR + Target Res"):
            preparetype = int(3)
        elif(prepare_type == "Source AR + Source Res"):
            preparetype = int(4)
        elif(prepare_type == "Source AR + Tile Res"):
            preparetype = int(5)
        elif(prepare_type == "Tile AR + Source Res"):
            preparetype = int(6)
        elif(prepare_type == "Square AR + Target Res"):
            preparetype = int(7)
        elif(prepare_type == "Square AR + Tile Res"):
            preparetype = int(8)
        elif(prepare_type == "Direct Source"):
            preparetype = int(9)

        ip_adapter_settings = ipamodel,ipa_wtype,ipa_weight,ipa_noise,ipa_start,ipa_end,tile_short,tile_weight,zoom,offset_w,offset_h,preparetype,prepare_intpol,prepare_sharpening

        return(ip_adapter_settings,)