Style Conditioner Base Only (Mikey)

Documentation

• Class name: Style Conditioner Base Only
• Category: Mikey/Conditioning
• Output node: False

This node is designed to conditionally apply styling to a base input without the additional refinement layer, focusing on modifying the base attributes according to a specified style and strength. It abstracts the complexity of style application, ensuring that the base input is enhanced or altered in a manner consistent with the desired aesthetic or thematic direction.

Input types

Required

• style
  • Specifies the style to be applied. This affects the overall aesthetic or thematic direction of the base input.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• strength
  • Determines the intensity of the style application, influencing how significantly the base input is altered.
  • Comfy dtype: FLOAT
  • Python dtype: float
• positive_cond_base
  • The base positive conditioning to which the style will be applied, serving as the initial state before styling.
  • Comfy dtype: CONDITIONING
  • Python dtype: torch.Tensor
• negative_cond_base
  • The base negative conditioning to which the style will be applied, complementing the positive conditioning in defining the initial styling state.
  • Comfy dtype: CONDITIONING
  • Python dtype: torch.Tensor
• base_clip
  • The CLIP model used for encoding the style prompts, integral to the process of applying the specified style.
  • Comfy dtype: CLIP
  • Python dtype: torch.nn.Module
• use_seed
  • Indicates whether a seed should be used to deterministically select a style from a predefined set, ensuring reproducibility.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: bool
• seed
  • The seed value used for deterministic style selection when 'use_seed' is true, affecting the style choice.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• base_pos_cond
  • Comfy dtype: CONDITIONING
  • The modified positive base conditioning after the style has been applied, reflecting the desired aesthetic changes.
  • Python dtype: torch.Tensor
• base_neg_cond
  • Comfy dtype: CONDITIONING
  • The modified negative base conditioning after the style has been applied, complementing the positive conditioning in the styled output.
  • Python dtype: torch.Tensor
• style_str
  • Comfy dtype: STRING
  • The style that was applied, providing a reference to the aesthetic or thematic direction chosen.
  • Python dtype: str

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class StyleConditionerBaseOnly:
    @classmethod
    def INPUT_TYPES(s):
        s.styles, s.pos_style, s.neg_style = read_styles()
        return {"required": {"style": (s.styles,),"strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
                             "positive_cond_base": ("CONDITIONING",), "negative_cond_base": ("CONDITIONING",),
                             "base_clip": ("CLIP",),
                             "use_seed": (['true','false'], {'default': 'false'}),
                             "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                             }
        }

    RETURN_TYPES = ('CONDITIONING','CONDITIONING','STRING',)
    RETURN_NAMES = ('base_pos_cond','base_neg_cond','style_str',)
    FUNCTION = 'add_style'
    CATEGORY = 'Mikey/Conditioning'

    def add_style(self, style, strength, positive_cond_base, negative_cond_base,
                  base_clip,
                  use_seed, seed):
        if use_seed == 'true' and len(self.styles) > 0:
            offset = seed % len(self.styles)
            style = self.styles[offset]
        pos_prompt = self.pos_style[style]
        neg_prompt = self.neg_style[style]
        pos_prompt = pos_prompt.replace('{prompt}', '')
        neg_prompt = neg_prompt.replace('{prompt}', '')
        if style == 'none':
            return (positive_cond_base, negative_cond_base, style, )
        # encode the style prompt
        positive_cond_base_new = CLIPTextEncodeSDXL.encode(self, base_clip, 1024, 1024, 0, 0, 1024, 1024, pos_prompt, pos_prompt)[0]
        negative_cond_base_new = CLIPTextEncodeSDXL.encode(self, base_clip, 1024, 1024, 0, 0, 1024, 1024, neg_prompt, neg_prompt)[0]
        # average the style prompt with the existing conditioning
        positive_cond_base = ConditioningAverage.addWeighted(self, positive_cond_base_new, positive_cond_base, strength)[0]
        negative_cond_base = ConditioningAverage.addWeighted(self, negative_cond_base_new, negative_cond_base, strength)[0]
        return (positive_cond_base, negative_cond_base, style, )