Add CLIP SDXL Refiner Params

Documentation

• Class name: BNK_AddCLIPSDXLRParams
• Category: conditioning/advanced
• Output node: False

This node is designed to enhance the conditioning data for image generation by incorporating additional parameters such as width, height, and an aesthetic score. It operates by iterating over a list of conditioning elements, modifying each with the specified dimensions and aesthetic score, thereby preparing the data for more tailored and aesthetically pleasing image generation.

Input types

Required

• conditioning
  • The base conditioning data for image generation, which this node modifies by adding width, height, and an aesthetic score to each element.
  • Comfy dtype: CONDITIONING
  • Python dtype: List[Tuple[Any, Dict[str, Any]]]
• width
  • Specifies the width to be added to the conditioning data, influencing the dimensions of the generated image.
  • Comfy dtype: INT
  • Python dtype: int
• height
  • Specifies the height to be added to the conditioning data, influencing the dimensions of the generated image.
  • Comfy dtype: INT
  • Python dtype: int
• ascore
  • An aesthetic score to be added to the conditioning data, aiming to guide the image generation towards more visually appealing results.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• conditioning
  • Comfy dtype: CONDITIONING
  • The enhanced conditioning data, now including specified width, height, and aesthetic score for each element.
  • Python dtype: List[Tuple[Any, Dict[str, Any]]]

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class AddCLIPSDXLRParams:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "conditioning": ("CONDITIONING", ),
            "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
            "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
            "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
            }}

    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "encode"

    CATEGORY = "conditioning/advanced"

    def encode(self, conditioning, width, height, ascore):
        c = []
        for t in conditioning:
            n = [t[0], t[1].copy()]
            n[1]['width'] = width
            n[1]['height'] = height
            n[1]['aesthetic_score'] = ascore
            c.append(n)
        return (c,)